ICASSP 2020accepted0 citations
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
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.
BibTeX
@inproceedings{icassp2020_pyannoteaudioneu,
title = {Pyannote.Audio: Neural Building Blocks for Speaker Diarization},
author = {Hervé Bredin and Ruiqing Yin and Juan Manuel Coria and Gregory Gelly and Pavel Korshunov and Marvin Lavechin and Diego Fustes and Hadrien Titeux and Wassim Bouaziz and Marie-Philippe Gill},
booktitle = {ICASSP 2020},
year = {2020}
}