HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Pyannote.audio: neural building blocks for speaker diarization

Hervé Bredin 1 Ruiqing Yin 1 Juan Manuel Coria 1 Gregory Gelly 1 Pavel Korshunov Marvin Lavechin Diego Fustes Hadrien Titeux Wassim Bouaziz Marie-Philippe Gill
1 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur
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.
Keywords : speaker diarization voice activity detection speaker change detection overlapped speech detection speaker embedding
Document type :
Conference papers
Complete list of metadata
https://hal.archives-ouvertes.fr/hal-02995345
Contributor : Limsi Publications Connect in order to contact the contributor
Submitted on : Monday, November 9, 2020 - 10:12:38 AM
Last modification on : Wednesday, March 16, 2022 - 3:53:51 AM

Links full text

arxiv

Identifiers

  • HAL Id : hal-02995345, version 1
  • ARXIV : 1911.01255

Collections

LIMSI | CNRS | SORBONNE-UNIVERSITE | UNIV-PARIS-SACLAY | SU-TI | GS-COMPUTER-SCIENCE | LISN | GS-ENGINEERING

Citation

Hervé Bredin, Ruiqing Yin, Juan Manuel Coria, Gregory Gelly, Pavel Korshunov, et al.. Pyannote.audio: neural building blocks for speaker diarization. IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2020, Barcelona, Spain. ⟨hal-02995345⟩

Export

BibTeX TEI DC DCterms EndNote Datacite

Share

Metrics

Record views

166