Global

Open-source speech recognition toolkit for training, inference, streaming ASR, VAD, punctuation, speaker diarization pipelines, and OpenAI-compatible/MCP serving.

System requirements to run python examples on x86-64 systems:

> Why not just run a simple Whisper model on every audio batch? Whisper is designed for complete utterances, not real-time chunks. Processing small segments loses context, cuts off words mid-syllable, and produces poor transcription.

audio is an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it comes with state-of-the-art pretrained models and pipelines, that can be further finetuned to your own data for even better performance.

Noise Supression Application for PulseAudio or Pipewire

This project is a Windows port of the whisper.cpp implementation.

早耳 - Real-time multilingual speech-to-text on CPU only. Live subtitles, browser dashboard, speaker labels, translation. No GPU, no cloud.

Local Speech AI on a MacBook — watch the 4-minute open-source library tour on YouTube

A robust, efficient, low-latency speech-to-text library with advanced voice activity detection, wake word activation and instant transcription.