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Communication Dans Un Congrès Année : 2020

Pyannote.audio: neural building blocks for speaker diarization

Résumé

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.

Dates et versions

hal-02995345 , version 1 (09-11-2020)

Identifiants

Citer

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⟩
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