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

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.