Bredin, HerveYin, RuiqingCoria, Juan ManuelKorshunov, PavelLavechin, MarvinFustes, DiegoTiteux, HadrienBouaziz, WassimGill, Marie-Philippe2020-05-272020-05-272020-05-27202010.1109/ICASSP40776.2020.9052974https://infoscience.epfl.ch/handle/20.500.14299/1689701911.01255We 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.text::conference output::conference paper not in proceedings