TTS open source analysis

🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production

Project overview

⭐ 44130 · Python · Last activity on GitHub: 2024-08-16

GitHub: https://github.com/coqui-ai/TTS

Why it matters for engineering teams

TTS addresses the practical challenge of converting text into natural-sounding speech, a key requirement for voice-enabled applications and accessibility tools. It provides a production ready solution that has been tested both in research environments and real-world deployments, ensuring reliability for engineering teams working on speech synthesis. This open source tool for engineering teams is particularly suited for machine learning and AI engineers focused on deep learning models for text-to-speech tasks. However, it may not be the best choice for teams seeking a lightweight or low-latency solution without the need for customisation, as the models can be resource-intensive and require expertise in Python and PyTorch.

When to use this project

Use TTS when your project demands high-quality, multi-speaker voice synthesis with flexibility for custom voice cloning or conversion. Consider alternatives if you need a simpler or more lightweight text-to-speech system with minimal setup or lower computational requirements.

Team fit and typical use cases

Machine learning and AI engineering teams benefit most from TTS, using it to develop and deploy advanced speech synthesis models within products like virtual assistants, accessibility tools, and interactive voice response systems. It serves as a self hosted option for teams needing control over voice data and customisation, often integrated into larger AI-driven platforms requiring natural voice output.

Best suited for

Topics and ecosystem

deep-learning glow-tts hifigan melgan multi-speaker-tts python pytorch speaker-encoder speaker-encodings speech speech-synthesis tacotron text-to-speech tts tts-model vocoder voice-cloning voice-conversion voice-synthesis

Activity and freshness

Latest commit on GitHub: 2024-08-16. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.