CONF Bredin_ICASSP_2020/IDIAP pyannote.audio: neural building blocks for speaker diarization Bredin, Herve Yin, Ruiqing Coria, Juan Manuel Korshunov, Pavel Lavechin, Marvin Fustes, Diego Titeux, Hadrien Bouaziz, Wassim Gill, Marie-Philippe IEEE International Conference on Acoustics, Speech, and Signal Processing 2020 https://arxiv.org/pdf/1911.01255.pdf URL 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.