pyannote-speaker-diarization-3.1
ivrit-ai/pyannote-speaker-diarization-3.1
Tracked by OpenModelStats since August 24, 2026View on Hugging Face ↗
- Downloads 30DRolling recent-window download count as reported by the source platform.
- 217.9K
- Total downloadsCumulative all-time download counter reported by the source platform.
- 857.3K
- Gained 7DIncrease in cumulative total downloads between OpenModelStats' own observations seven days apart.
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- Gained 30DIncrease in cumulative total downloads between OpenModelStats' own observations roughly thirty days apart.
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- LikesCommunity likes reported by the source platform.
- 5
- Rank · tracked modelsPosition among all eligible models tracked by OpenModelStats, ordered by recent downloads. #118 among tracked Speech Recognition models.
- #1,478
- –this week
- Parameters
- —
- Last source update
- Aug 15, 2025
Downloads over time
Cumulative total downloads observed by OpenModelStatsNot enough tracked history yet for a chart.
OpenModelStats began tracking this model on 2026-08-24. Charts appear as daily observations accumulate.
Likes over time
Not enough tracked history yet for a chart.
OpenModelStats began tracking this model on 2026-08-24. Charts appear as daily observations accumulate.
Rank among tracked models
Lower is betterNot enough tracked history yet for a chart.
OpenModelStats began tracking this model on 2026-08-24. Charts appear as daily observations accumulate.
Overview
pyannote-speaker-diarization-3.1 is a speech recognition model published by ivrit-ai. OpenModelStats has tracked the model since Aug 24, 2026, most recently observing it 24 min ago.
- Published
- Aug 15, 2025
- Last updated
- Aug 15, 2025
- Library
- pyannote-audio
- License
- mit
- Task
- Speech Recognition
- Parameters
- —
- Spaces
- —
- Derivative models
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- Velocity 7D/day
- —
pyannote-audiopyannotepyannote-audio-pipelineaudiovoicespeechspeakerspeaker-diarizationspeaker-change-detectionvoice-activity-detectionoverlapped-speech-detectionautomatic-speech-recognition
Publisher
ivrit-aiView publisher statistics →Similar models
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