Audio
Whisper
Robust Speech Recognition via Large-Scale Weak Supervision
★ 109.9k starsMIT
View guide →Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text…
PaddleSpeech is an open-source audio project by PaddlePaddle (PaddlePaddle/PaddleSpeech on GitHub). It has 12,690 GitHub stars and 1,955 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 12 August 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/PaddlePaddle/PaddleSpeech.git
cd PaddleSpeechIsolate 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 PaddlePaddle/PaddleSpeech with 1,955 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
Deep learning toolkit for text-to-speech, battle-tested in research and production
Fish Speech — Fish Audio's expressive open text-to-speech and voice cloning: SOTA-quality TTS you can self-host, under the Fish Audio Community License (check terms)
VITS — the original end-to-end text-to-speech model: a conditional VAE with adversarial learning, in PyTorch.
The most-starred other Audio projects in the LibreAI catalogue — the realistic shortlist if PaddleSpeech 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.