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3.6
TFLOPS
Hervé Bredin
hbredin
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https://herve.niderb.fr/
hbredin
hbredin
herve-bredin
AI & ML interests
Co-founder and Chief Science Officer at pyannoteAI
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Introducing Precision-3! Note: We'll walk through the model and take questions in our webinar this Thursday, October 8 at 5 PM CEST : https://app.livestorm.co/pyannote-ai/precision-3 Different tasks want different diarization errors: TTS data prep needs overlap removed, transcription needs every quiet word kept. Precision-3 lets you shift the operating point at inference time, without retraining, via vad_sensitivity and crosstalk_sensitivity (log-prior shifts, not post-hoc thresholds). Results across the 11 DIHARD domains: - VAD F1: 95.8% (vs 95.0% community-1, 91.9% Silero VAD) - Overlap detection F1: 59.7% vs 48.2% - The optimal VAD setting varies by domain, from −0.8 (Restaurant) to +2.0 (Audiobooks) - Averaged over 15 datasets, DER drops from 16.02 (Precision-2) to 14.35, mostly from lower speaker confusion. Write-up: https://pyannote.ai/blog/tune-vad-crosstalk-speaker-diarization Let us know what you're building with pyannote!
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hbredin/pyannoteAI-EMMA-20k
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hbredin/pyannoteAI-EMMA
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hbredin/test-hf-file-sytem
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