Version 3.1.0

TL;DR

pyannote/speaker-diarization-3.1 no longer requires unpopular ONNX runtime

Full changelog

New features

• feat(model): add WeSpeaker embedding wrapper based on PyTorch
• feat(model): add support for multi-speaker statistics pooling
• feat(pipeline): add TimingHook for profiling processing time
• feat(pipeline): add ArtifactHook for saving internal steps
• feat(pipeline): add support for list of hooks with Hooks
• feat(utils): add "soft" option to Powerset.to_multilabel

Fixes

• fix(pipeline): add missing "embedding" hook call in SpeakerDiarization
• fix(pipeline): fix AgglomerativeClustering to honor num_clusters when provided
• fix(pipeline): fix frame-wise speaker count exceeding max_speakers or detected num_speakers in SpeakerDiarization pipeline

Improvements

• improve(pipeline): compute fbank on GPU when requested

Breaking changes

• BREAKING(pipeline): rename WeSpeakerPretrainedSpeakerEmbedding to ONNXWeSpeakerPretrainedSpeakerEmbedding
• BREAKING(setup): remove onnxruntime dependency.
  You can still use ONNX hbredin/wespeaker-voxceleb-resnet34-LM but you will have to install onnxruntime yourself.
• BREAKING(pipeline): remove logging_hook (use ArtifactHook instead)
• BREAKING(pipeline): remove onset and offset parameter in SpeakerDiarizationMixin.speaker_count
  You should now binarize segmentations before passing them to speaker_count