README.md

---
title: VoiceAPI
emoji: 🎙️
colorFrom: blue
colorTo: purple
sdk: docker
app_port: 7860
license: mit
tags:
  - tts
  - text-to-speech
  - indian-languages
  - vits
  - multilingual
  - speech-synthesis
---

# 🎙️ VoiceAPI - Multi-lingual Indian Language TTS

An advanced **multi-speaker, multilingual text-to-speech (TTS) synthesizer** supporting 11 Indian languages with 21 voice options.

**Live API**: [https://huggingface.co/proxy/harshil748-voiceapi.hf.space](https://huggingface.co/proxy/harshil748-voiceapi.hf.space)

## 🌟 Features

- **11 Indian Languages**: Hindi, Bengali, Marathi, Telugu, Kannada, Gujarati, Bhojpuri, Chhattisgarhi, Maithili, Magahi, English
- **21 Voice Options**: Male and female voices for each language
- **High-Quality Audio**: 22050 Hz sample rate, natural prosody
- **REST API**: Simple GET/POST endpoints for easy integration
- **Real-time Synthesis**: Fast inference on CPU/GPU

## 🗣️ Supported Languages

| Language | Code | Female | Male | Script |
|----------|------|--------|------|--------|
| Hindi | hi | ✅ | ✅ | देवनागरी |
| Bengali | bn | ✅ | ✅ | বাংলা |
| Marathi | mr | ✅ | ✅ | देवनागरी |
| Telugu | te | ✅ | ✅ | తెలుగు |
| Kannada | kn | ✅ | ✅ | ಕನ್ನಡ |
| Gujarati | gu | ✅ | - | ગુજરાતી |
| Bhojpuri | bho | ✅ | ✅ | देवनागरी |
| Chhattisgarhi | hne | ✅ | ✅ | देवनागरी |
| Maithili | mai | ✅ | ✅ | देवनागरी |
| Magahi | mag | ✅ | ✅ | देवनागरी |
| English | en | ✅ | ✅ | Latin |

## 📡 API Usage

### Endpoint

\`\`\`
GET/POST /Get_Inference
\`\`\`

### Parameters

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| \`text\` | string | Yes | Text to synthesize (lowercase for English) |
| \`lang\` | string | Yes | Language name (hindi, bengali, etc.) |
| \`speaker_wav\` | file | Yes | Reference WAV file (for API compatibility) |

### Example (Python)

\`\`\`python
import requests

base_url = 'https://huggingface.co/proxy/harshil748-voiceapi.hf.space/Get_Inference'
WavPath = 'reference.wav'

params = {
    'text': 'नमस्ते, आप कैसे हैं?',
    'lang': 'hindi',
}

with open(WavPath, "rb") as AudioFile:
    response = requests.get(base_url, params=params, files={'speaker_wav': AudioFile.read()})

if response.status_code == 200:
    with open('output.wav', 'wb') as f:
        f.write(response.content)
    print("Audio saved as 'output.wav'")
\`\`\`

### Example (cURL)

\`\`\`bash
curl -X POST "https://huggingface.co/proxy/harshil748-voiceapi.hf.space/Get_Inference?text=hello&lang=english" \\
  -F "speaker[email protected]" \\
  -o output.wav
\`\`\`

## 🏗️ Model Architecture

- **Base Model**: VITS (Variational Inference with adversarial learning for Text-to-Speech)
- **Encoder**: Transformer-based text encoder (6 layers, 192 hidden channels)
- **Decoder**: HiFi-GAN neural vocoder
- **Duration Predictor**: Stochastic duration predictor for natural prosody
- **Sample Rate**: 22050 Hz (16000 Hz for Gujarati)

## 📊 Training

### Datasets Used

| Dataset | Languages | Hours | Source | License |
|---------|-----------|-------|--------|---------|
| OpenSLR-103 | Hindi | 24h | [OpenSLR](https://www.openslr.org/103/) | CC BY 4.0 |
| OpenSLR-37 | Bengali | 22h | [OpenSLR](https://www.openslr.org/37/) | CC BY 4.0 |
| OpenSLR-64 | Marathi | 30h | [OpenSLR](https://www.openslr.org/64/) | CC BY 4.0 |
| OpenSLR-66 | Telugu | 28h | [OpenSLR](https://www.openslr.org/66/) | CC BY 4.0 |
| OpenSLR-79 | Kannada | 26h | [OpenSLR](https://www.openslr.org/79/) | CC BY 4.0 |
| OpenSLR-78 | Gujarati | 25h | [OpenSLR](https://www.openslr.org/78/) | CC BY 4.0 |
| Common Voice | Hindi, Bengali | 50h+ | [Mozilla](https://commonvoice.mozilla.org/) | CC0 |
| IndicTTS | Multiple | 100h+ | [IIT Madras](https://www.iitm.ac.in/donlab/tts/) | Research |
| Indic-Voices | Multiple | 200h+ | [AI4Bharat](https://ai4bharat.iitm.ac.in/indic-voices/) | CC BY 4.0 |

### Training Configuration

- **Epochs**: 1000
- **Batch Size**: 32
- **Learning Rate**: 2e-4
- **Optimizer**: AdamW
- **FP16 Training**: Enabled
- **Hardware**: NVIDIA V100/A100 GPUs

### Training Pipeline

1. **Data Preparation** (\`training/prepare_dataset.py\`)
   - Download audio datasets
   - Normalize audio to 22050 Hz
   - Generate text transcriptions
   - Create train/val splits

2. **Model Training** (\`training/train_vits.py\`)
   - Train VITS model with character-level tokenization
   - Multi-speaker training with speaker embeddings
   - Mixed precision training for efficiency

3. **Model Export** (\`training/export_model.py\`)
   - Export trained models to JIT format
   - Generate vocabulary files (chars.txt)
   - Package for inference

See \`training/\` directory for full training scripts and configurations.

## �� Project Structure

\`\`\`
VoiceAPI/
├── app.py                 # Application entry point
├── Dockerfile             # Docker configuration
├── requirements.txt       # Python dependencies
├── src/
│   ├── api.py             # FastAPI REST server
│   ├── engine.py          # TTS inference engine
│   ├── config.py          # Voice configurations
│   ├── tokenizer.py       # Text tokenization
│   └── model_loader.py    # Model loading utilities
├── models/                # Trained model checkpoints
│   ├── hi_male/           # Hindi male voice
│   ├── hi_female/         # Hindi female voice
│   ├── bn_male/           # Bengali male voice
│   └── ...                # Other voices
└── training/
    ├── train_vits.py      # VITS training script
    ├── prepare_dataset.py # Data preparation
    ├── export_model.py    # Model export
    ├── datasets.csv       # Dataset links
    └── configs/           # Training configs
\`\`\`

## 📜 License

- **Code**: MIT License
- **Models**: CC BY 4.0
- **Datasets**: Individual licenses (see training/datasets.csv)

## 🙏 Acknowledgments

- [SYSPIN IISc SPIRE Lab](https://syspin.iisc.ac.in/) for Indian language speech research
- [Facebook MMS](https://github.com/facebookresearch/fairseq/tree/main/examples/mms) for multilingual TTS
- [Coqui TTS](https://github.com/coqui-ai/TTS) for the TTS library
- [AI4Bharat](https://ai4bharat.iitm.ac.in/) for Indian language resources
- [OpenSLR](https://www.openslr.org/) for speech datasets

## 📧 Contact

Built for the **Voice Tech for All** Hackathon - Multi-lingual TTS for healthcare assistants serving low-income communities.