pyannote.audio: neural building blocks for speaker diarization
This toolkit addresses the problem of speaker diarization for researchers and practitioners by offering a flexible and optimized solution, though it is incremental as it builds on existing neural methods.
The authors introduced pyannote.audio, an open-source toolkit for speaker diarization that provides trainable neural building blocks and pre-trained models, achieving state-of-the-art performance in tasks like voice activity detection and speaker embedding.
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.