pyannote.audio: neural building blocks for speaker diarization

Autor:	Bredin, Hervé, Yin, Ruiqing, Coria, Juan Manuel, Gelly, Gregory, Korshunov, Pavel, Lavechin, Marvin, Fustes, Diego, Titeux, Hadrien, Bouaziz, Wassim, Gill, Marie-Philippe
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Sound
Druh dokumentu:	Working Paper
Popis:	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
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.01255 Zobrazit plný text záznamu View this record from Arxiv