Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Conferences, Workshops, Symposiums, and Seminars
  4. pyannote.audio: neural building blocks for speaker diarization
 
conference paper not in proceedings

pyannote.audio: neural building blocks for speaker diarization

Bredin, Herve
•
Yin, Ruiqing
•
Coria, Juan Manuel
Show more
2020
IEEE International Conference on Acoustics, Speech, and Signal Processing

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.

  • Details
  • Metrics
Type
conference paper not in proceedings
DOI
10.1109/ICASSP40776.2020.9052974
ArXiv ID

1911.01255

Author(s)
Bredin, Herve
Yin, Ruiqing
Coria, Juan Manuel
Korshunov, Pavel
Lavechin, Marvin
Fustes, Diego
Titeux, Hadrien
Bouaziz, Wassim
Gill, Marie-Philippe
Date Issued

2020

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LIDIAP  
Event name
IEEE International Conference on Acoustics, Speech, and Signal Processing
Available on Infoscience
May 27, 2020
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/168970
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés