# MahmoudAshraf97/whisper-diarization

Automatic Speech Recognition with Speaker Diarization based on OpenAI Whisper

Repository: https://github.com/MahmoudAshraf97/whisper-diarization
Canonical: https://ross.abutalabs.com/products/whisper-diarization
Language: Jupyter Notebook
License: BSD-2-Clause
License Family: permissive
Topics: asr, speaker-diarization, speech, speech-recognition, speech-to-text, whisper
Last push: 2026-08-15T09:11:27+00:00

## Health v2 (maintenance only)
Score: 75/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 97, release rhythm 35, longevity 94
- inputs: {"age_days": 1316, "days_push": 18, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5630, forks 503 (observed 2026-08-28T04:09:26.068458+00:00)

## What it is
A pipeline that combines OpenAI Whisper transcription with speaker diarization using Voice Activity Detection (MarbleNet) and speaker embeddings (TitaNet). It aligns timestamps with ctc-forced-aligner to attribute each word in the transcript to the correct speaker.

## Use cases
- transcribe a meeting recording with speaker labels
- add speaker diarization to whisper transcripts
- convert podcast audio into a who-said-what transcript
- identify speakers in an interview audio file
- transcribe multi-speaker audio with timestamps
- run speech-to-text with diarization on GPU

## When to choose
- you need transcripts with per-word or per-sentence speaker attribution
- you want a ready-made pipeline combining Whisper, VAD, and speaker embeddings
- you want a Colab-friendly or CLI transcription tool with diarization

## When to avoid
- you only need plain transcription without speaker labels
- you need real-time streaming diarization
- you cannot run GPU-accelerated models locally

## Facets
- artifact type: library
- maturity: active
- function: speech-recognition, audio-processing, machine-learning, cli
- domain: speech-processing, artificial-intelligence
- platform: python, cross-platform
- tags: whisper, speaker-diarization, asr, transcription, vad, nemo, speech-to-text, natural-language-processing, gpu

## Member repositories
- MahmoudAshraf97/whisper-diarization (main) score 75

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:26.068458+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:55:20.725690+00:00, confidence not recorded.
  - readme: https://github.com/MahmoudAshraf97/whisper-diarization (fetched 2026-08-28T04:09:26.068458+00:00, sha 7d7495515358)
- Data as of 2026-08-30T08:39:29.467469+00:00.