whisper-diarization
A pipeline that combines OpenAI Whisper for speech-to-text with speaker diarization to identify who said what in audio.
What is whisper-diarization?
Whisper Diarization is an open-source tool that performs Automatic Speech Recognition (ASR) with speaker diarization. It uses OpenAI's Whisper model to transcribe audio and combines it with voice activity detection and speaker embedding models from Nvidia NeMo to label each part of the transcript with the correct speaker. It solves the problem of creating accurate, speaker-identified transcripts from multi-speaker audio files like meetings or interviews.
Developers, researchers, and data scientists working with audio data who need to transcribe conversations and identify participants, such as in computational linguistics, media analysis, or automated note-taking applications.
It provides a ready-to-use, integrated pipeline that combines best-in-class ASR with speaker identification, saving significant development time. Being open-source and self-hostable, it offers a free and customizable alternative to proprietary transcription services that include speaker diarization.
Overview
Automatic Speech Recognition with Speaker Diarization based on OpenAI Whisper
Use Cases
Best For
- Transcribing multi-speaker meetings and generating speaker-labeled minutes
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