whisper-diarization
Automatic Speech Recognition with Speaker Diarization based on OpenAI Whisper
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is whisper-diarization?
Whisper-Diarization is an open-source library that extends the capabilities of OpenAI's Whisper model to include speaker diarization, allowing for accurate transcription and identification of speakers in audio files.
Key differentiator
“Whisper-Diarization stands out by offering an open-source solution that integrates speaker diarization with the robust speech recognition capabilities of OpenAI's Whisper, making it a powerful tool for developers and researchers dealing with multi-speaker audio content.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Official documentation lacks detailed guides on customizing speaker diarization parameters
Processing times significantly increase and can lead to memory exhaustion for files longer than 1 hour
Fit analysis
Who is it for?
✓ Best for
Developers working on applications that require accurate speaker identification and transcription from audio inputs.
Researchers conducting studies on multi-speaker conversations where automated diarization is crucial.
✕ Not a fit for
Projects requiring real-time processing of audio streams, as Whisper-Diarization processes files post-recording.
Applications needing support for languages not covered by the underlying Whisper model.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Works well with
Integrations
Next step
Get Started with whisper-diarization
Step-by-step setup guide with code examples and common gotchas.