eryx-swahili-tts-v1
1
license:apache-2.0
by
Engeryx
Audio Model
OTHER
2B params
New
0 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
5GB+ RAM
Mobile
Laptop
Server
Quick Summary
AI model with specialized capabilities.
Device Compatibility
Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
2GB+ RAM
Code Examples
Usagepythonpytorch
import torch
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts
from huggingface_hub import hf_hub_download
# Download speaker embeddings
embedding_path = hf_hub_download(
repo_id="EryxLabs/eryx-swahili-tts-v1",
filename="swahili_speaker.pt"
)
# Load XTTS-v2 model
model_path = "path/to/xtts_v2" # or download from coqui
config = XttsConfig()
config.load_json(f"{model_path}/config.json")
model = Xtts.init_from_config(config)
model.load_checkpoint(config, checkpoint_dir=model_path, eval=True)
# Load Swahili speaker embeddings
embeddings = torch.load(embedding_path)
gpt_cond_latent = embeddings['gpt_cond_latent']
speaker_embedding = embeddings['speaker_embedding']
# Synthesize Swahili text
# Note: Use 'en' for language since XTTS-v2 doesn't support 'sw' directly
out = model.inference(
text="Habari yako, mimi ni msaidizi wa Kiswahili.",
language="en", # Swahili uses Latin script, works with English tokenizer
gpt_cond_latent=gpt_cond_latent,
speaker_embedding=speaker_embedding,
)
# Save audio
import torchaudio
torchaudio.save("output.wav", torch.tensor(out["wav"]).unsqueeze(0), 24000)Deploy This Model
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