Open-source speech recognition toolkit for training, inference, streaming ASR, VAD, punctuation, speaker diarization pipelines, and OpenAI-compatible/MCP serving.
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Updated
Jul 15, 2026 - Python
Open-source speech recognition toolkit for training, inference, streaming ASR, VAD, punctuation, speaker diarization pipelines, and OpenAI-compatible/MCP serving.
A PyTorch-based Speech Toolkit
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
End-to-End Speech Processing Toolkit
On-device Speech AI for Apple Silicon
Automatic Speech Recognition with Speaker Diarization based on OpenAI Whisper
Whisper & Faster-Whisper standalone executables for those who don't want to bother with Python.
A Repository for Single- and Multi-modal Speaker Verification, Speaker Recognition and Speaker Diarization
Multilingual Automatic Speech Recognition with word-level timestamps and confidence
Frontier CoreML audio models in your apps — text-to-speech, speech-to-text, voice activity detection, and speaker diarization. In Swift, powered by SOTA open source.
A python package to build AI-powered real-time audio applications
A curated list of awesome Speaker Diarization papers, libraries, datasets, and other resources.
This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization.
Open-source LLM-based ASR model family for Chinese, dialect, accent, and multilingual speech, with FunASR, vLLM, streaming, and llama.cpp runtimes.
Research and Production Oriented Speaker Verification, Recognition and Diarization Toolkit
AI speech toolkit for Apple Silicon — ASR, TTS, speech-to-speech, VAD, and diarization powered by MLX and CoreML
turnkey self-hosted offline transcription and diarization service with llm summary
一站式全自动字幕生成软件,下载、转录、翻译、压制全流程覆盖,无需人工介入 / One-stop automated subtitle generator. Handles downloading, transcription, translation, and hardcoding—zero human intervention required.
Python re-implementation of the (constrained) spectral clustering algorithms used in Google's speaker diarization papers.
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