Audio
Whisper
Robust Speech Recognition via Large-Scale Weak Supervision
★ 109.9k starsMIT
View guide →Official code for "F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching"
F5 TTS is an open-source audio project by SWivid (SWivid/F5-TTS on GitHub). It has 15,330 GitHub stars and 2,240 forks, is released under the MIT license, and was last updated on GitHub on 21 September 2026.
Speech-to-text, text-to-speech, voice cloning, or music/SFX generation — each project here has a lane.
Python packages, Docker images, and C++ builds cover servers, desktops, and edge devices.
Download the weights or pick a voice; many tools bundle a strong default so you can start immediately.
Transcribe files, synthesize scripts, or generate music — then export standard audio formats.
Open speech recognition now rivals paid APIs on accuracy across languages.
Modern TTS is strikingly human — good enough for narration and voiceovers.
Clone a voice from a short sample for consistent characters and branding.
Compose backing tracks and sound effects from text descriptions.
Entire pipelines work without internet — ideal for private or field use.
Drop-in endpoints that mimic commercial APIs, so existing apps switch easily.
Download the source and move into the project folder.
git clone https://github.com/SWivid/F5-TTS.git
cd F5-TTSIsolate dependencies so nothing clashes with your other projects.
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activateInstall the requirements, then follow the README's launch command (it varies per project).
pip install -r requirements.txtExcellent — the leading open models match commercial services on clean audio, in many languages.
The tech clones from short samples; only ever clone voices you own or have explicit permission to use.
It is an open-source project at SWivid/F5-TTS with 2,240 forks. Check the repo's contributors and recent commits to see how active maintenance is.
Transcription and TTS run on CPU acceptably; GPUs speed up batch jobs and music generation a lot.
Coverage varies — transcription is broadest, TTS quality is best in major languages.
Usually yes, but check both the code license and the model license.
Robust Speech Recognition via Large-Scale Weak Supervision
Port of OpenAI's Whisper model in C/C++
Faster Whisper transcription with CTranslate2
IndexTTS: industrial-level, controllable zero-shot text-to-speech with voice cloning
Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text…
MOSS-TTS-Nano: a 0.1B open multilingual speech model that clones voices and streams in real time on CPU
The most-starred other Audio projects in the LibreAI catalogue — the realistic shortlist if F5 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.