Introducing the Coqui-ai-TTS Project
Coqui-ai-TTS is an advanced open-source text-to-speech (TTS) library designed to convert written text into spoken words using deep learning models. This project stands out by offering a wide range of features and tools to both research enthusiasts and developers interested in TTS technologies.
Key Features:
- Multi-Language Support: The library provides pretrained models supporting over 1100 languages, making it a versatile solution for global applications.
- Model Variety: It includes Text2Spec models like Tacotron and Glow-TTS, and vocoder models like MelGAN and WaveGrad, enabling users to generate high-quality speech synthesis.
- Fine-Tuning and Customization: Users can train new models or fine-tune existing ones, allowing for customization according to specific needs or languages.
- Fast Performance: The library boasts impressive performance with capabilities such as under 200ms latency for streaming TTS and faster inference with models like Tortoise.
- Voice Cloning and Conversion: With features for unconstrained voice cloning and voice conversion, users can replicate voices and convert audio to different speaker attributes.
Recent Updates:
Coqui-ai-TTS recently released TTSv2, which supports 16 languages and offers enhanced performance. This update, along with the release of ⓍTTS—a multi-language production TTS model—demonstrates their commitment to continuous improvement.
Installation and Setup:
The library requires a Python environment (version >= 3.9, < 3.13) and can be easily installed via PyPI with the following command:
pip install coqui-tts
For those wishing to delve into development or model training, cloning the GitHub repository is another option:
git clone https://github.com/idiap/coqui-ai-TTS
cd coqui-ai-TTS
pip install -e .
API and Command Line Usage:
Coqui TTS provides a comprehensive Python API, easily integrating into applications for both single-speaker and multi-speaker TTS tasks. Moreover, a command-line tool is available for synthesizing speech without delving into code, supporting both provided models and custom trained models.
Community and Resources:
The project encourages community engagement through GitHub discussions and a dedicated Discord channel. Rich documentation and extensive tutorials are available to assist new users in getting started and extending the capabilities of Coqui TTS.
Conclusion:
With its broad language support, high performance, and extensive customization options, Coqui-ai-TTS serves as an invaluable tool for anyone interested in text-to-speech technology. Whether for practical applications or academic research, its open-source nature and active community support make it a compelling choice.