speaker-diarization

17 proyectos comparten este topic de GitHub

Las líneas conectan a los miembros que están mediblemente relacionados entre sí. El tamaño de los puntos refleja las estrellas.

🧬 Miembros

FunASR

Open-source speech recognition toolkit for training, inference, streaming ASR, VAD, punctuation, speaker…

★ 19.5k

speechbrain

A PyTorch-based Speech Toolkit

★ 11.7k

espnet

End-to-End Speech Processing Toolkit

★ 9.9k

argmax-oss-swift

On-device Speech AI for Apple Silicon

★ 6.3k

whisper-diarization

Automatic Speech Recognition with Speaker Diarization based on OpenAI Whisper

★ 5.6k

whisper-standalone-win

Whisper & Faster-Whisper standalone executables for those who don't want to bother with Python.

★ 3.1k

whisper-timestamped

Multilingual Automatic Speech Recognition with word-level timestamps and confidence

★ 2.8k

diart

A python package to build AI-powered real-time audio applications

★ 2k

awesome-diarization

A curated list of awesome Speaker Diarization papers, libraries, datasets, and other resources.

★ 1.9k

Fun-ASR

Open-source LLM-based ASR model family for Chinese, dialect, accent, and multilingual speech, with FunASR,…

★ 1.4k

speech-swift

AI speech toolkit for Apple Silicon — ASR, TTS, speech-to-speech, VAD, and diarization powered by MLX and…

★ 1.1k

transcriptionstream

turnkey self-hosted offline transcription and diarization service with llm summary

★ 944

reverb

Open source inference code for Rev's model

★ 436

VAF_2

Aims to create a comprehensive voice toolkit for training, testing, and deploying speaker verification…

★ 383

parakeet-rs

very fast speech-to-text, diarization, streaming (even in CPU) with NVIDIA Parakeet in Rust

★ 378

izwi

Voice AI runtime. Local first transcription, speaker diarization, TTS, and voice cloning with an OpenAI…

★ 351

humla

Open-source AI meeting notes for Mac. Records mic + system audio with no bot, transcribes on-device or via…

★ 173

Medido a partir de los temas de GitHub compartidos por ambos proyectos, ponderado por cuán raros son cada uno de los temas.