Version 3.2.0

New features

• feat(task): add option to cache task training metadata to speed up training (with @clement-pages)
• feat(model): add receptive_field, num_frames and dimension to models (with @Bilal-Rahou)
• feat(model): add fbank_only property to WeSpeaker models
• feat(util): add Powerset.permutation_mapping to help with permutation in powerset space (with @FrenchKrab)
• feat(sample): add sample file at pyannote.audio.sample.SAMPLE_FILE
• feat(metric): add reduce option to diarization_error_rate metric (with @Bilal-Rahou)
• feat(pipeline): add Waveform and SampleRate preprocessors

Fixes

• fix(task): fix random generators and their reproducibility (with @FrenchKrab)
• fix(task): fix estimation of training set size (with @FrenchKrab)
• fix(hook): fix torch.Tensor support in ArtifactHook
• fix(doc): fix typo in Powerset docstring (with @lukasstorck)

Improvements

• improve(metric): add support for number of speakers mismatch in diarization_error_rate metric
• improve(pipeline): track both Model and nn.Module attributes in Pipeline.to(device)
• improve(io): switch to torchaudio >= 2.2.0
• improve(doc): update tutorials (with @clement-pages)

Breaking changes

• BREAKING(model): get rid of Model.example_output in favor of num_frames method, receptive_field property, and dimension property
• BREAKING(task): custom tasks need to be updated (see "Add your own task" tutorial)

Community contributions

• community: add tutorial for offline use of pyannote/speaker-diarization-3.1 (by @simonottenhauskenbun)