sinhala-tts-vits

🗣️ Sinhala TTS VITS 🇱🇰

Sinhala Text-to-Speech — A Coqui TTS VITS model that generates natural Sinhala speech from text, with 16 distinct voices to choose from.

🎯 Model Details

🗣️ Available Speakers

🚀 Usage

Option 1: Coqui TTS (Recommended)

import torch
from TTS.tts.configs.vits_config import VitsConfig
from TTS.tts.models.vits import Vits
from TTS.tts.utils.text import TTSTokenizer
from TTS.tts.utils.speakers import SpeakerManager
from TTS.utils.audio import AudioProcessor

# Load config
config = VitsConfig()
config.load_json("config.json")

# Initialize components
ap = AudioProcessor.init_from_config(config)
tokenizer, new_config = TTSTokenizer.init_from_config(config)
speaker_manager = SpeakerManager()
speaker_manager.load_ids_from_file("speakers.json")

# Create and load model
model = Vits(new_config, ap, tokenizer, speaker_manager)
from safetensors.torch import load_file
state_dict = load_file("sinhala_tts_vits_model.safetensors")
model.load_state_dict(state_dict, strict=False)
model.eval()

# Synthesize
text = "ආයුබෝවන්! ඔබට කොහොමද?"
outputs = model.synthesize(text, config=new_config, speaker="mettananda")

# Save audio
import soundfile as sf
sf.write("output.wav", outputs["wav"], 16000)

Option 2: REST API (with included server.py)

# Start the server
python server.py

# Generate speech
curl -X POST http://localhost:8081/tts \
  -H "Content-Type: application/json" \
  -d '{
    "text": "ආයුබෝවන්!",
    "speaker": "mettananda",
    "emotion": "neutral"
  }' \
  --output output.wav

# Health check
curl http://localhost:8081/health

# List speakers
curl http://localhost:8081/speakers

Option 3: HuggingFace Inference API

⚠️ This model uses Coqui TTS (not Transformers) and cannot be used via the standard HF Inference API. Use Coqui TTS directly or the included REST API server.

Option 4: Docker Deployment

docker build -t sinhala-tts-server .
docker run -p 8081:8081 sinhala-tts-server

🛠️ Development Platforms

📦 Files

🎓 Training & Fine-Tuning

For detailed instructions, see the DEVELOPER_GUIDE.md which covers:

• Setup: Environment configuration and dependency installation
• Training from scratch: Full training pipeline with the Sinhala dataset
• Fine-tuning: Adapting the model to new voices or domains
• Dataset preparation: Preprocessing Sinhala audio data
• Export to SafeTensors: Converting PyTorch checkpoints to SafeTensors format
• Cloud GPU training: Step-by-step guides for Kaggle, Colab, and Modal

🌐 Deployment Options

⚠️ Limitations

• Audio quality: Trained on a limited dataset (~200 samples × 16 speakers) — quality may vary
• Inference speed: CPU inference is slower; GPU recommended for production
• Emotion control: Basic emotion prefixes are supported but effects are subtle
• Proper nouns: May struggle with non-Sinhala words or names
• Out-of-vocabulary characters: Limited to the 93-character vocabulary

📝 License

This model is released under the Apache 2.0 License.

🙏 Maintainer

Death Legion Team — 🤗 HuggingFace

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<p align="center"> <a href="https://huggingface.co/spaces/deathlegionteam/sinhala-tts-demo">🎧 Try the Live Demo</a> • <a href="./DEVELOPER_GUIDE.md">📖 Developer Guide</a> • <a href="https://huggingface.co/deathlegionteam">🏠 Death Legion Team</a> </p>