pyannote.audio: neural building blocks for speaker diarization
| Type of publication: | Conference paper |
| Citation: | Bredin_ICASSP_2020 |
| Publication status: | Published |
| Booktitle: | IEEE International Conference on Acoustics, Speech, and Signal Processing |
| Year: | 2020 |
| Month: | May |
| URL: | https://arxiv.org/pdf/1911.012... |
| 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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| Projects |
Idiap SWAN |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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