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pyannote.audio: neural building blocks for speaker diarization

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arxiv 1911.01255 v1 pith:U7RBMFVB submitted 2019-11-04 eess.AS cs.SD

Hervé Bredin , Ruiqing Yin , Juan Manuel Coria , Gregory Gelly , Pavel Korshunov , Marvin Lavechin , Diego Fustes , Hadrien Titeux
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Wassim Bouaziz Marie-Philippe Gill
This is my paper
classification eess.AScs.SD
keywords speakeraudiodetectiondiarizationpyannoteblocksbuildingneural
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce pyannote.audio, an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it provides a set of trainable end-to-end neural building blocks that can be combined and jointly optimized to build speaker diarization pipelines. pyannote.audio also comes with pre-trained models covering a wide range of domains for voice activity detection, speaker change detection, overlapped speech detection, and speaker embedding -- reaching state-of-the-art performance for most of them.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition

    eess.AS 2025-09 conditional novelty 7.0 of 10

    MMedFD introduces a real-world Chinese healthcare ASR benchmark for full-duplex, multi-turn dialogue, with a healthcare-specific WER metric and a Whisper-small baseline.

  2. HPP-Voice: A Large-Scale Evaluation of Speech Embeddings for Multi-Phenotypic Classification

    eess.AS 2025-05 conditional novelty 6.0 of 10

    A 30-second counting task, embedded with speaker-identification models, predicts male sleep apnea (AUC 0.64) and shows gender- and condition-specific model rankings across a new 7,188-recording clinical speech benchmark.

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