🐸 Coqui TTS - Advanced Text-to-Speech Toolkit

A comprehensive library for advanced Text-to-Speech generation

📑 Table of Contents

• Latest Updates
• Quick Start
• Features
• Installation
• Basic Usage
• Available Models
• Advanced Usage
• Performance Optimization
• Deployment
• Contributing
• Community & Support
• Security
• Citation

🔥 Latest Updates

• 📣 ⓍTTSv2 released with 16 languages and improved performance
• 📣 ⓍTTS fine-tuning code available
• 📣 ⓍTTS now supports streaming with <200ms latency
• 📣 Support for ~1100 Fairseq models
• 📣 Integration with 🐶Bark and 🐢Tortoise View all updates

🚀 Quick Start

# Install TTS
pip install TTS

# Quick text-to-speech generation
python -c "from TTS.api import TTS; tts = TTS('tts_models/multilingual/multi-dataset/xtts_v2'); tts.tts_to_file(text='Hello, this is a test!', file_path='output.wav')"

✨ Features

• 🌟 High-performance Deep Learning models
• 🌍 Support for 1100+ languages
• 🎯 Production-ready performance
• 🔧 Easy-to-use API
• 📚 Comprehensive documentation
• 🛠️ Flexible training pipeline

💻 Installation

Requirements

• Python >= 3.9, < 3.12
• Operating Systems: Ubuntu 18.04+ (Primary), Windows, macOS
• GPU (Optional but recommended for training)

Basic Installation

pip install TTS

Development Installation

git clone https://github.com/coqui-ai/TTS
pip install -e .[all,dev,notebooks]

Docker Installation

docker run --rm -it -p 5002:5002 ghcr.io/coqui-ai/tts-cpu

Detailed Installation Guide

📖 Basic Usage

Simple Text-to-Speech

from TTS.api import TTS

# Initialize TTS
tts = TTS("tts_models/en/ljspeech/tacotron2-DDC")

# Generate speech
tts.tts_to_file("Hello world!", file_path="output.wav")

Multi-lingual Voice Cloning

tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2")
tts.tts_to_file(
    text="Hello world!",
    speaker_wav="path/to/speaker.wav",
    language="en",
    file_path="output.wav"
)

🎯 Available Models

Text-to-Speech Models

Model	Languages	Speed	Quality	GPU Memory
ⓍTTS v2	16	⭐⭐⭐	⭐⭐⭐⭐⭐	4GB+
YourTTS	13	⭐⭐⭐⭐	⭐⭐⭐⭐	2GB+
Tacotron 2	Any	⭐⭐	⭐⭐⭐	1GB+
FastSpeech 2	Any	⭐⭐⭐⭐⭐	⭐⭐⭐	1GB+

Complete Model List

🚄 Performance Optimization

Hardware Requirements

• Training: NVIDIA GPU with 8GB+ VRAM recommended
• Inference: CPU or GPU (2GB+ VRAM)
• RAM: 8GB minimum, 16GB recommended

Optimization Tips

• Use batch processing for multiple inputs
• Enable GPU acceleration when available
• Implement caching for repeated phrases
• Use quantized models for faster inference

🌐 Deployment

Production Setup

1. Load models during initialization
2. Implement proper error handling
3. Set up monitoring and logging
4. Use appropriate scaling strategies

Docker Deployment

docker run -d --gpus all -p 5002:5002 ghcr.io/coqui-ai/tts-gpu

🛠 Contributing

Development Setup

1. Fork the repository
2. Set up development environment
3. Run tests: pytest tests/
4. Submit PR with detailed description

Contributing Guidelines

🤝 Community & Support

Get Help

• Discord Community
• GitHub Discussions
• Documentation

Commercial Support

• Contact Coqui

🔒 Security

Best Practices

• Keep models and dependencies updated
• Use environment variables for sensitive data
• Implement proper API authentication
• Monitor for unusual usage patterns

Security Policy

📚 Citation

@misc{coqui-ai-tts,
  author = {Eren Gölge and others},
  title = {🐸TTS - a deep learning toolkit for Text-to-Speech},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/coqui-ai/TTS}},
}

📊 Performance Benchmarks

🌍 Language Support

• 16 primary languages with ⓍTTS v2
• 1100+ languages via Fairseq models
• Support for custom language training

Language Documentation

📁 Directory Structure

|- notebooks/       # Jupyter Notebooks for examples
|- TTS/
    |- bin/        # Training scripts
    |- tts/        # Core TTS models
    |- vocoder/    # Vocoder models
    |- utils/      # Utilities

For more detailed information, visit our Documentation.