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conference paper not in proceedings
pyannote.audio: neural building blocks for speaker diarization
2020
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.
Type
conference paper not in proceedings
ArXiv ID
1911.01255
Authors
Bredin, Herve
•
Yin, Ruiqing
•
Coria, Juan Manuel
•
Korshunov, Pavel
•
Lavechin, Marvin
•
Fustes, Diego
•
Titeux, Hadrien
•
Bouaziz, Wassim
•
Gill, Marie-Philippe
Publication date
2020
Peer reviewed
REVIEWED
EPFL units
Available on Infoscience
May 27, 2020
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