Audio
whisper.cpp
Port of OpenAI's Whisper model in C/C++
★ 54,1k estrellasMIT
Ver la guía →transcribe.cpp es un proyecto de audio de código abierto de handy-computer (handy-computer/transcribe.cpp en GitHub). Tiene 1.982 estrellas en GitHub y 112 bifurcaciones, se publica bajo la licencia MIT y se actualizó por última vez en GitHub el 4 de octubre de 2026.
Fast local speech-to-text across 16+ model families, powered by ggml.
Choose one of the 16+ supported speech model families and grab its weights.
Compile the C++ code or use a prebuilt binary for your platform.
Feed it a file or a live stream and get text back in seconds.
Pipe transcripts into notes, subtitles, or your own pipeline.
Runs ggml speech-to-text inference across a wide range of model families.
Audio never leaves your machine; no cloud account or key needed.
Native performance makes transcription fast even on modest CPUs.
Compact model files that are easy to download and swap.
Permissive license that suits open and commercial projects alike.
Designed for both file transcription and low-latency streams.
Download the source and move into the project folder.
git clone https://github.com/handy-computer/transcribe.cpp.git
cd transcribe.cppEvery project documents its own setup; start with install requirements.
cat README.md | head -60Follow the project's launch command — Docker, pip, npm, or a binary release.
# see README for the exact launch commandIt is an open-source project at handy-computer/transcribe.cpp with 112 forks. Check the repo's contributors and recent commits to see how active maintenance is.
MIT licensed — commercial use allowed with attribution.
It supports 16+ speech-to-text model families via ggml/GGUF weights; check the README for the current list.
A GPU helps, but ggml is built for CPU efficiency, so many models run fine on a regular laptop.
The headline difference is breadth: it aims to cover many model families, not just one.
Port of OpenAI's Whisper model in C/C++
Faster Whisper transcription with CTranslate2
Speech-to-text, text-to-speech, speaker diarization, speech enhancement, source separation, and VAD using next-gen Kaldi with onnxruntime without Internet co…
Fast and local neural text-to-speech engine
Local-first AI meeting minutes workbench: FunASR speech-to-text + Ollama LLM.
MetaVoice-1B: a 1.2B-parameter foundation model for emotional, zero-shot text-to-speech
Los demás proyectos de Audio con más estrellas del catálogo de LibreAI: la lista realista de alternativas si transcribe.cpp no es lo que buscas.
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++
Proyectos de Audio cercanos en el catálogo, los más próximos en estrellas a transcribe.cpp: sigue explorando a ambos lados.
WaveRNN vocoder plus Tacotron TTS in PyTorch: train your own neural speech synthesis on LJSpeech.
Turn books and documents into natural speech with the Kokoro TTS model.
ToucanTTS: fast, controllable text-to-speech for over 7,000 languages, from Stuttgart's IMS.
Obs-localvocal — obs plugin for local speech recognition and captioning, open-source.
GAN vocoder that turns mel-spectrograms into high-fidelity speech audio, fast enough for real-time use.
Instant, ultra-realistic text-to-speech from a simple local app.
Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.