pyannote.audio: neural building blocks for speaker diarization

Hervé Bredin; Ruiqing Yin; Juan Manuel Coria; Gregory Gelly; Pavel Korshunov; Marvin Lavechin; Diego Fustes; Hadrien Titeux; Wassim Bouaziz; Marie-Philippe Gill

arXiv · 1911.01255

pyannote.audio: neural building blocks for speaker diarization

Hervé Bredin·Ruiqing Yin·Juan Manuel Coria·Gregory Gelly·Pavel Korshunov·Marvin Lavechin·Diego Fustes·Hadrien Titeux·Wassim Bouaziz·Marie-Philippe Gill

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Abstract

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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Hervé Bredin, Ruiqing Yin, Juan Manuel Coria, Gregory Gelly, Pavel Korshunov, Marvin Lavechin, Diego Fustes, Hadrien Titeux, Wassim Bouaziz, Marie-Philippe Gill. 2019-11-04. pyannote.audio: neural building blocks for speaker diarization. https://arxiv.org/abs/1911.01255

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