nyrahealth/CrisperWhisper

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1349 stars · 86 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

CrisperWhisper 2.0 is a controllable speech recognition model and Python library that transcribes audio either verbatim (including fillers, repetitions, stutters, and vocal sounds) or in a cleaned 'intended' form, with precise word-level timestamps. It builds on Whisper with a fast CTranslate2 runtime, multilingual support, and seamless longform transcription.

Use cases

• transcribe audio verbatim with fillers and stutters included
• generate clean readable transcripts from speech
• get word-level timestamps for subtitles or alignment
• convert clean transcript corpora into verbatim datasets for TTS training
• analyze disfluencies in clinical speech or stuttering research
• transcribe long recordings without chunk-boundary artifacts

When to choose

• you need verbatim transcription that captures disfluencies, fillers, and vocal events
• precise word-level timing matters, e.g. for subtitles, labeling, or speech analysis
• you want a controllable choice between verbatim and cleaned output in one model
• you need multilingual speech recognition with disfluency detection

When to avoid

• you only need lightweight real-time dictation with minimal compute
• you require a permissively licensed model for commercial redistribution without checking the custom license
• you need speaker diarization or full-duplex conversation analytics out of the box

Facets

library · maturity active

speech-recognition nlp machine-learning llm-inference speech-processing machine-learning healthcare python cross-platform asr whisper verbatim-transcription word-level-timestamps disfluency-detection cttranslate2 filler-detection stutter-detection multilingual natural-language-processing gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="nyrahealth/CrisperWhisper")