Pyannote.Audio: Neural Building Blocks for Speaker Diarization
Hervé Bredin, Ruiqing Yin, Juan Manuel Coria, Grégory Gelly, Pavel Korshunov, Marvin Lavechin, Diego Fustes, Hadrien Titeux, Wassim Bouaziz, Marie-Philippe Gill
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.
Topics & Concepts
Speaker diarisationComputer scienceSpeech recognitionPython (programming language)EmbeddingSpeaker recognitionArtificial neural networkOpen sourceArtificial intelligenceProgramming languageOperating systemSoftwareSpeech Recognition and SynthesisSpeech and Audio ProcessingMusic and Audio Processing