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 [BibTeX] [Marc21]
pyannote.audio: neural building blocks for speaker diarization
Type of publication: Conference paper
Citation: Bredin_ICASSP_2020
Publication status: Published
Booktitle: IEEE International Conference on Acoustics, Speech, and Signal Processing
Year: 2020
Month: May
URL: https://arxiv.org/pdf/1911.012...
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.
Projects Idiap
Authors Bredin, Herve
Yin, Ruiqing
Coria, Juan Manuel
Korshunov, Pavel
Lavechin, Marvin
Fustes, Diego
Titeux, Hadrien
Bouaziz, Wassim
Gill, Marie-Philippe
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