A deep learning toolkit for Text-to-Speech generation with pretrained models in over 1100 languages and tools for training.
🐸TTS is a deep learning toolkit for Text-to-Speech generation. It provides a library of pretrained models capable of synthesizing speech in over 1100 languages, along with tools for training new models, fine-tuning existing ones, and analyzing datasets. It solves the problem of creating high-quality, customizable, and efficient TTS systems for both research and production use.
AI researchers, machine learning engineers, and developers working on speech synthesis, voice cloning, or multi-lingual TTS applications, especially those needing to train, fine-tune, or deploy custom TTS models.
Developers choose 🐸TTS for its extensive model zoo covering the latest TTS research, support for a vast number of languages, and its balance between a user-friendly API for inference and powerful, flexible tools for training and experimentation.
🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Implements state-of-the-art models like ⓍTTS, VITS, and YourTTS from the extensive model zoo, providing a comprehensive suite for various TTS tasks as listed in the README.
Supports over 1100 languages through Fairseq integration and dedicated models, making it highly versatile for global applications, as highlighted in the key features.
Includes voice cloning with YourTTS and voice conversion with FreeVC, enabling personalized and adaptive speech synthesis, demonstrated in the Python API examples.
Offers a modular Trainer API and dataset analysis utilities, facilitating custom model training and fine-tuning, which is core to its philosophy for research and production.
Requires managing Python versions, system dependencies, and optional extras, with platform-specific challenges noted (e.g., Windows instructions via external link), making setup cumbersome compared to cloud APIs.
Documentation is spread across ReadTheDocs, GitHub Discussions, and Discord, which can lead to fragmented information and difficulty in troubleshooting, as indicated in the 'Where to ask questions' section.
The inclusion of many experimental and third-party models means quality, latency, and stability can vary, requiring careful selection and testing for production use, as some models are noted as 'experimental'.