Veena-GGUF

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

pip install transformers torch torchaudio
pip install snac bitsandbytes  # For audio decoding and quantization

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from snac import SNAC
import soundfile as sf

# Model configuration for 4-bit inference
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "maya-research/veena-tts",
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("maya-research/veena-tts", trust_remote_code=True)

# Initialize SNAC decoder
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()

# Control token IDs (fixed for Veena)
START_OF_SPEECH_TOKEN = 128257
END_OF_SPEECH_TOKEN = 128258
START_OF_HUMAN_TOKEN = 128259
END_OF_HUMAN_TOKEN = 128260
START_OF_AI_TOKEN = 128261
END_OF_AI_TOKEN = 128262
AUDIO_CODE_BASE_OFFSET = 128266

# Available speakers
speakers = ["kavya", "agastya", "maitri", "vinaya"]

def generate_speech(text, speaker="kavya", temperature=0.4, top_p=0.9):
    """Generate speech from text using specified speaker voice"""

    # Prepare input with speaker token
    prompt = f"<spk_{speaker}> {text}"
    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)

    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
    input_tokens = [
        START_OF_HUMAN_TOKEN,
        *prompt_tokens,
        END_OF_HUMAN_TOKEN,
        START_OF_AI_TOKEN,
        START_OF_SPEECH_TOKEN
    ]

    input_ids = torch.tensor([input_tokens], device=model.device)

    # Calculate max tokens based on text length
    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)

    # Generate audio tokens
    with torch.no_grad():
        output = model.generate(
            input_ids,
            max_new_tokens=max_tokens,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.05,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
        )

    # Extract SNAC tokens
    generated_ids = output[0][len(input_tokens):].tolist()
    snac_tokens = [
        token_id for token_id in generated_ids
        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
    ]

    if not snac_tokens:
        raise ValueError("No audio tokens generated")

    # Decode audio
    audio = decode_snac_tokens(snac_tokens, snac_model)
    return audio

def decode_snac_tokens(snac_tokens, snac_model):
    """De-interleave and decode SNAC tokens to audio"""
    if not snac_tokens or len(snac_tokens) % 7 != 0:
        return None

    # De-interleave tokens into 3 hierarchical levels
    codes_lvl = [[] for _ in range(3)]
    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]

    for i in range(0, len(snac_tokens), 7):
        # Level 0: Coarse (1 token)
        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
        # Level 1: Medium (2 tokens)
        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
        # Level 2: Fine (4 tokens)
        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])

    # Convert to tensors for SNAC decoder
    hierarchical_codes = []
    for lvl_codes in codes_lvl:
        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_model.device).unsqueeze(0)
        if torch.any((tensor < 0) | (tensor > 4095)):
            raise ValueError("Invalid SNAC token values")
        hierarchical_codes.append(tensor)

    # Decode with SNAC
    with torch.no_grad():
        audio_hat = snac_model.decode(hierarchical_codes)

    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()

# --- Example Usage ---

# Hindi
text_hindi = "आज मैंने एक नई तकनीक के बारे में सीखा जो कृत्रिम बुद्धिमत्ता का उपयोग करके मानव जैसी आवाज़ उत्पन्न कर सकती है।"
audio = generate_speech(text_hindi, speaker="kavya")
sf.write("output_hindi_kavya.wav", audio, 24000)

# English
text_english = "Today I learned about a new technology that uses artificial intelligence to generate human-like voices."
audio = generate_speech(text_english, speaker="agastya")
sf.write("output_english_agastya.wav", audio, 24000)

# Code-mixed
text_mixed = "मैं तो पूरा presentation prepare कर चुका हूं! कल रात को ही मैंने पूरा code base चेक किया।"
audio = generate_speech(text_mixed, speaker="maitri")
sf.write("output_mixed_maitri.wav", audio, 24000)