From the model card
Fields below are copied from the tags and counters on the HuggingFace repository pyannote/speaker-diarization-3.1 at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- pyannote
- Pipeline tag
- automatic-speech-recognition
- Library
- pyannote.audio
- License tag
mit— read the license file in the repo before relying on it- Papers cited
- arXiv:2111.14448, arXiv:2012.01477
- Downloads (HF counter at last fetch)
- 9,260,640
- Likes (HF counter at last fetch)
- 3,350
- Model card
- https://huggingface.co/pyannote/speaker-diarization-3.1
Use cases
- Meeting recording segmentation by speaker for per-speaker transcription
- Podcast and interview audio segmentation for editing workflows
- Call center audio analytics requiring per-speaker turn identification
- Research transcription where speaker attribution is required
- Pre-processing step before speaker-labeled ASR
Pros
- Complete end-to-end pipeline covering VAD, segmentation, embedding, and clustering
- MIT license for commercial use
- Well-maintained pyannote ecosystem with active research updates
- State-of-the-art diarization error rates on standard benchmarks
Cons
- Requires accepting pyannote model terms on HuggingFace — not automatic download
- Performance degrades significantly with overlapping speech segments
- Number of speakers must be estimated or provided; errors cascade to final output
- GPU recommended for real-time processing; CPU inference is slow on long recordings
- Hyperparameter tuning (clustering threshold, min/max speakers) required per domain
Tags
pyannote-audiopyannotepyannote-audio-pipelineaudiovoicespeechspeakerspeaker-diarizationspeaker-change-detectionvoice-activity-detectionoverlapped-speech-detectionautomatic-speech-recognitionarxiv:2111.14448arxiv:2012.01477license:mitendpoints_compatibleregion:us