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pyannote.audio: neural building blocks for speaker diarization

2019-11-04 14:46:31
Hervé Bredin, Ruiqing Yin, Juan Manuel Coria, Gregory Gelly, Pavel Korshunov, Marvin Lavechin, Diego Fustes, Hadrien Titeux, Wassim Bouaziz, Marie-Philippe Gill
arXiv_SD
         
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.

Abstract (translated)

URL

https://arxiv.org/abs/1911.01255

PDF

https://arxiv.org/pdf/1911.01255.pdf

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