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coqui-ai/TTS

Coqui TTS: A deep learning toolkit for text-to-speech, proven through research and production practice.

MPL-2.0Python 40.7kcoqui-ai Last Updated: 2024-08-16
https://github.com/coqui-ai/TTS

Coqui TTS Project Detailed Introduction

Project Overview

Coqui TTS is an advanced open-source Text-to-Speech (TTS) deep learning toolkit developed by the Coqui AI team. Thoroughly validated through research and production environments, this project provides users with a powerful and flexible speech synthesis solution.

Basic Information

  • Project Name: Coqui TTS (🐸TTS)
  • Development Team: Coqui AI
  • Project Type: Open-source Deep Learning Toolkit
  • Main Uses: Text-to-Speech, Speech Synthesis, Voice Cloning
  • Supported Languages: 1100+ languages
  • Technology Stack: Python, PyTorch, Deep Learning

Core Features and Characteristics

🎯 Main Features

1. Text-to-Speech Synthesis

  • Supports various advanced TTS model architectures
  • High-quality speech output
  • Real-time speech synthesis (latency < 200ms)
  • Supports batch processing

2. Multilingual Support

  • 1100+ pre-trained models covering a wide range of languages
  • Supports multilingual mixed synthesis
  • Includes popular languages such as English, Chinese, French, German, Spanish, etc.
  • Supports Fairseq model integration

3. Voice Cloning Technology

  • Zero-shot voice cloning: Replicates voice characteristics using a small number of audio samples
  • Multi-speaker TTS: Supports speech synthesis for multiple speakers
  • Real-time voice conversion: Converts the voice of one speaker to that of another
  • Cross-lingual voice cloning: Supports voice transfer between different languages

4. Advanced Model Architectures

Text2Speech Models
  • Tacotron & Tacotron2: Classic end-to-end TTS models
  • Glow-TTS: Flow-based fast TTS model
  • SpeedySpeech: Efficient non-autoregressive TTS model
  • FastPitch & FastSpeech: Fast speech synthesis models
  • VITS: End-to-end speech synthesis model
  • XTTS: Coqui's production-grade multilingual TTS model
Vocoder Models
  • MelGAN: Generative Adversarial Network vocoder
  • HiFiGAN: High-fidelity audio generation
  • WaveRNN: Recurrent Neural Network vocoder
  • ParallelWaveGAN: Parallel waveform generation
  • UnivNet: Universal neural vocoder

🛠️ Technical Features

1. Training and Fine-tuning

  • Complete training pipeline: Complete process from data preprocessing to model training
  • Model fine-tuning support: Can be fine-tuned based on pre-trained models
  • Detailed training logs: Terminal and TensorBoard visualization
  • Flexible training configuration: Supports various training parameter adjustments

2. Data Processing Tools

  • Dataset analysis tool: Automatically analyzes the quality of speech datasets
  • Data preprocessing: Audio normalization, text cleaning, etc.
  • Data augmentation: Supports various data augmentation techniques
  • Format conversion: Supports multiple audio formats

3. Model Optimization

  • Speaker Encoder: Efficient speaker encoder
  • Attention mechanism optimization: Including Guided Attention, Dynamic Convolutional Attention, etc.
  • Alignment Network: Improves the alignment quality of text and audio
  • Dual Decoder Consistency: Improves model stability

🚀 Latest Feature Highlights

TTSv2 Version Update

  • 16 language support: Expanded multilingual capabilities
  • Comprehensive performance improvement: Faster inference speed and higher sound quality
  • Streaming synthesis: Supports real-time streaming speech synthesis
  • Production-ready: Validated in large-scale production environments

Integrated Third-Party Models

  • 🐶 Bark: Unconstrained voice cloning
  • 🐢 Tortoise: High-quality speech synthesis
  • Fairseq model integration: Supports Facebook's large-scale multilingual models

Installation and Usage

Quick Installation

# PyPI Installation (Inference Only)
pip install TTS

# Development Installation (Full Features)
git clone https://github.com/coqui-ai/TTS
pip install -e .[all,dev,notebooks]

Basic Usage Examples

Python API Usage

import torch
from TTS.api import TTS

# Get device
device = "cuda" if torch.cuda.is_available() else "cpu"

# Initialize TTS model
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(device)

# Speech synthesis
tts.tts_to_file(
    text="你好，世界！",
    speaker_wav="speaker_sample.wav",
    language="zh",
    file_path="output.wav"
)

Command Line Usage

# List available models
tts --list_models

# Basic speech synthesis
tts --text "Hello World" --out_path output.wav

# Multilingual synthesis
tts --text "你好世界" --model_name "tts_models/multilingual/multi-dataset/xtts_v2" --out_path output.wav

Docker Support

# Run Docker container
docker run --rm -it -p 5002:5002 --entrypoint /bin/bash ghcr.io/coqui-ai/tts-cpu

# Start TTS server
python3 TTS/server/server.py --model_name tts_models/en/vctk/vits

Application Scenarios

1. Research and Development

  • Academic research: Speech synthesis algorithm research
  • Model development: New TTS model architecture development
  • Benchmarking: Model performance comparison and evaluation

2. Commercial Applications

  • Voice assistants: Voice interaction for smart devices
  • Audiobook production: Automated audio content generation
  • Multimedia production: Video and game voiceovers
  • Accessibility services: Providing text reading for the visually impaired

3. Personal Projects

  • Voice cloning: Personal voice model training
  • Multilingual learning: Pronunciation practice and language learning
  • Creative projects: Audio content creation

Project Advantages

Technical Advantages

  • Advanced model architecture: Integrates the latest TTS research results
  • High performance: Optimized inference speed and sound quality
  • Flexibility: Modular design, easy to extend and customize
  • Complete toolchain: Complete solution from data processing to model deployment

Ecosystem Advantages

  • Active community: Continuous development and maintenance
  • Rich documentation: Detailed user guides and API documentation
  • Pre-trained models: A large number of ready-to-use pre-trained models
  • Cross-platform support: Supports Linux, Windows, macOS

Commercial Advantages

  • Open-source and free: No license fees required
  • Production validation: Tested in large-scale production environments
  • Customizable: Supports private deployment and custom development
  • Continuous updates: Regularly releases new features and improvements

Technical Architecture

Core Components

TTS/
├── bin/                    # Executable files
├── tts/                    # TTS model
│   ├── layers/            # Model layer definitions
│   ├── models/            # Model implementations
│   └── utils/             # TTS utility functions
├── speaker_encoder/       # Speaker encoder
├── vocoder/              # Vocoder model
├── utils/                # General utilities
└── notebooks/            # Jupyter examples

Model Flow

Text Input → Text Processing → TTS Model → Spectrogram → Vocoder → Audio Output
    ↓
Speaker Encoding → Voice Features → Model Modulation

Performance Metrics

Inference Performance

  • Real-time factor: < 0.1 (10x faster than real-time)
  • Latency: < 200ms (streaming synthesis)
  • Memory footprint: Depending on model size, typically < 2GB
  • Batch processing support: Can handle multiple requests simultaneously

Audio Quality Metrics

  • MOS score: 4.0+ (close to human speech)
  • WER: < 5% (speech recognition accuracy)
  • Frequency response: Supports 22kHz high-fidelity audio
  • Dynamic range: Supports full dynamic range audio

Summary

Coqui TTS is a powerful and technologically advanced open-source text-to-speech toolkit. It not only provides rich pre-trained models and advanced technical features but also has good ease of use and scalability. Researchers, developers, and enterprise users can all benefit from this project.