تقرير
pyannote.audio: neural building blocks for speaker diarization
| العنوان: | pyannote.audio: neural building blocks for speaker diarization |
|---|---|
| المؤلفون: | Bredin, Hervé, Yin, Ruiqing, Coria, Juan Manuel, Gelly, Gregory, Korshunov, Pavel, Lavechin, Marvin, Fustes, Diego, Titeux, Hadrien, Bouaziz, Wassim, Gill, Marie-Philippe |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound |
| الوصف: | 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. Comment: Submitted to ICASSP 2020 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1911.01255 |
| رقم الأكسشن: | edsarx.1911.01255 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |