Audio
Mozilla TTS
Mozilla's deep-learning text-to-speech library with Tacotron, Glow-TTS and neural vocoders.
★ 10.2k starsMPL-2.0
View guide →Deep learning toolkit for text-to-speech, battle-tested in research and production
Coqui TTS is an open-source audio project by coqui-ai (coqui-ai/TTS on GitHub). It has 46,109 GitHub stars and 6,166 forks, is released under the MPL-2.0 license, and was last updated on GitHub on 16 August 2024.
Decide what you want from Coqui TTS and check the README and requirements to confirm it fits your setup and Python environment.
Clone the repository from GitHub (github.com/coqui-ai/TTS) or use the documented install channel if you do not need the source.
Follow the README quickstart to install dependencies and set any required configuration or credentials.
Work through the README's first example end to end, then adapt it to your own project or data.
Implements Tacotron, Glow-TTS and other spectrogram and end-to-end models.
Includes HiFiGAN, MelGAN and other vocoders for high-quality waveforms.
Supports speaker encoders and voice cloning/conversion workflows.
Train and run multi-speaker models with speaker encodings.
Provides pretrained models ready for inference via the `tts` command.
Ships a Docker image (ghcr.io/coqui-ai/tts-cpu) for quick trials.
Start by getting the source and entering the project directory.
git clone https://github.com/coqui-ai/TTS.git
cd TTSInstall the Python package from PyPI.
pip install TTSRun the CLI with a pretrained model on your first sentence.
tts --text "Hello world" --out_path out.wav # see README for the exact commandIt is maintained by coqui-ai and contributors on GitHub, where the repository github.com/coqui-ai/TTS is the canonical home for code, issues and releases.
Yes — it is open source under the MPL-2.0 license. Read the license in the repository for what is permitted, including any conditions on redistribution or commercial use.
No. Models run locally; note the project has been quiet since 2024, so pin versions carefully.
Compared with piper or eSpeak: Coqui TTS offers trainable, high-quality neural models and cloning, beyond lightweight rule-based or small-footprint engines.
Clone github.com/coqui-ai/TTS, follow the README quickstart to install it, and run the documented first example before adapting it to your own work.
Mozilla's deep-learning text-to-speech library with Tacotron, Glow-TTS and neural vocoders.
Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text…
Open research toolkit for text-to-speech, voice conversion, singing voice and text-to-audio generation.
VITS — the original end-to-end text-to-speech model: a conditional VAE with adversarial learning, in PyTorch.
Speech-to-text-to-speech for VRChat, VTubers, and streamers.
IndexTTS: industrial-level, controllable zero-shot text-to-speech with voice cloning
The most-starred other Audio projects in the LibreAI catalogue — the realistic shortlist if Coqui TTS is not the right fit.
利用 AI 大模型和自动化工作流，根据主题或关键词一键生成高清短视频。Generate HD short videos from a topic or keyword with an automated AI workflow.
Robust Speech Recognition via Large-Scale Weak Supervision
1 min voice data can also be used to train a good TTS model! (few shot voice cloning)
Clone a voice from 5 seconds of audio and generate arbitrary speech in real time
Port of OpenAI's Whisper model in C/C++
Free and open — grab the source, star the repo, and start creating.