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Conference papers

Pyannote.audio: neural building blocks for speaker diarization

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.
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https://hal.archives-ouvertes.fr/hal-02995345
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Submitted on : Monday, November 9, 2020 - 10:12:38 AM
Last modification on : Wednesday, March 16, 2022 - 3:53:51 AM

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

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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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