Llasa-1B

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf

llasa_1b ='HKUSTAudio/Llasa-1B'

tokenizer = AutoTokenizer.from_pretrained(llasa_1b)
model = AutoModelForCausalLM.from_pretrained(llasa_1b)
model.eval() 
model.to('cuda')

from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUSTAudio/xcodec2"  
 
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()   

input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
 
    speech_tokens_str = []
    for speech_id in speech_ids:
        speech_tokens_str.append(f"<|s_{speech_id}|>")
    return speech_tokens_str

def extract_speech_ids(speech_tokens_str):
 
    speech_ids = []
    for token_str in speech_tokens_str:
        if token_str.startswith('<|s_') and token_str.endswith('|>'):
            num_str = token_str[4:-2]

            num = int(num_str)
            speech_ids.append(num)
        else:
            print(f"Unexpected token: {token_str}")
    return speech_ids

#TTS start!
with torch.no_grad():
 
    formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"

    # Tokenize the text
    chat = [
        {"role": "user", "content": "Convert the text to speech:" + formatted_text},
        {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
    ]

    input_ids = tokenizer.apply_chat_template(
        chat, 
        tokenize=True, 
        return_tensors='pt', 
        continue_final_message=True
    )
    input_ids = input_ids.to('cuda')
    speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')

    # Generate the speech autoregressively
    outputs = model.generate(
        input_ids,
        max_length=2048,  # We trained our model with a max length of 2048
        eos_token_id= speech_end_id ,
        do_sample=True,    
        top_p=1,           #  Adjusts the diversity of generated content
        temperature=0.8,   #  Controls randomness in output
    )
    # Extract the speech tokens
    generated_ids = outputs[0][input_ids.shape[1]:-1]

    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   

    # Convert  token <|s_23456|> to int 23456 
    speech_tokens = extract_speech_ids(speech_tokens)

    speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)

    # Decode the speech tokens to speech waveform
    gen_wav = Codec_model.decode_code(speech_tokens) 
 

sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)