CHANGELOG.md

Changelog

develop

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(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)

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)

Version 3.1.1 (2023-12-01)

TL;DR

Providing num_speakers to pyannote/speaker-diarization-3.1 now works as expected.

Fixes

• fix(pipeline): fix support for setting num_speakers in pyannote/speaker-diarization-3.1 pipeline

Version 3.1.0 (2023-11-16)

TL;DR

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

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

Version 3.0.1 (2023-09-28)

• fix(pipeline): fix WeSpeaker GPU support

Version 3.0.0 (2023-09-26)

Features and improvements

• feat(pipeline): send pipeline to device with pipeline.to(device)
• feat(pipeline): add return_embeddings option to SpeakerDiarization pipeline
• feat(pipeline): make segmentation_batch_size and embedding_batch_size mutable in SpeakerDiarization pipeline (they now default to 1)
• feat(pipeline): add progress hook to pipelines
• feat(task): add powerset support to SpeakerDiarization task
• feat(task): add support for multi-task models
• feat(task): add support for label scope in speaker diarization task
• feat(task): add support for missing classes in multi-label segmentation task
• feat(model): add segmentation model based on torchaudio self-supervised representation
• feat(pipeline): check version compatibility at load time
• improve(task): load metadata as tensors rather than pyannote.core instances
• improve(task): improve error message on missing specifications

Breaking changes

• BREAKING(task): rename Segmentation task to SpeakerDiarization
• BREAKING(pipeline): pipeline defaults to CPU (use pipeline.to(device))
• BREAKING(pipeline): remove SpeakerSegmentation pipeline (use SpeakerDiarization pipeline)
• BREAKING(pipeline): remove segmentation_duration parameter from SpeakerDiarization pipeline (defaults to duration of segmentation model)
• BREAKING(task): remove support for variable chunk duration for segmentation tasks
• BREAKING(pipeline): remove support for FINCHClustering and HiddenMarkovModelClustering
• BREAKING(setup): drop support for Python 3.7
• BREAKING(io): channels are now 0-indexed (used to be 1-indexed)
• BREAKING(io): multi-channel audio is no longer downmixed to mono by default. You should update how pyannote.audio.core.io.Audio is instantiated:
  • replace Audio() by Audio(mono="downmix");
  • replace Audio(mono=True) by Audio(mono="downmix");
  • replace Audio(mono=False) by Audio().
• BREAKING(model): get rid of (flaky) Model.introspection If, for some weird reason, you wrote some custom code based on that, you should instead rely on Model.example_output.
• BREAKING(interactive): remove support for Prodigy recipes

Fixes and improvements

• fix(pipeline): fix reproducibility issue with Ampere CUDA devices
• fix(pipeline): fix support for IOBase audio
• fix(pipeline): fix corner case with no speaker
• fix(train): prevent metadata preparation to happen twice
• fix(task): fix support for "balance" option
• improve(task): shorten and improve structure of Tensorboard tags

Dependencies update

• setup: switch to torch 2.0+, torchaudio 2.0+, soundfile 0.12+, lightning 2.0+, torchmetrics 0.11+
• setup: switch to pyannote.core 5.0+, pyannote.database 5.0+, and pyannote.pipeline 3.0+
• setup: switch to speechbrain 0.5.14+

Version 2.1.1 (2022-10-27)

• BREAKING(pipeline): rewrite speaker diarization pipeline
• feat(pipeline): add option to optimize for DER variant
• feat(clustering): add support for NeMo speaker embedding
• feat(clustering): add FINCH clustering
• feat(clustering): add min_cluster_size hparams to AgglomerativeClustering
• feat(hub): add support for private/gated models
• setup(hub): switch to latest hugginface_hub API
• fix(pipeline): fix support for missing reference in Resegmentation pipeline
• fix(clustering) fix corner case where HMM.fit finds too little states

Version 2.0.1 (2022-07-20)

• BREAKING: complete rewrite
• feat: much better performance
• feat: Python-first API
• feat: pretrained pipelines (and models) on Huggingface model hub
• feat: multi-GPU training with pytorch-lightning
• feat: data augmentation with torch-audiomentations
• feat: Prodigy recipe for model-assisted audio annotation

Version 1.1.2 (2021-01-28)

• fix: make sure master branch is used to load pretrained models (#599)

Version 1.1 (2020-11-08)

• last release before complete rewriting

Version 1.0.1 (2018-07-19)

• fix: fix regression in Precomputed.call (#110, #105)

Version 1.0 (2018-07-03)

• chore: switch from keras to pytorch (with tensorboard support)
• improve: faster & better traning (AutoLR, advanced learning rate schedulers, improved batch generators)
• feat: add tunable speaker diarization pipeline (with its own tutorial)
• chore: drop support for Python 2 (use Python 3.6 or later)

Version 0.3.1 (2017-07-06)

• feat: add python 3 support
• chore: rewrite neural speaker embedding using autograd
• feat: add new embedding architectures
• feat: add new embedding losses
• chore: switch to Keras 2
• doc: add tutorial for (MFCC) feature extraction
• doc: add tutorial for (LSTM-based) speech activity detection
• doc: add tutorial for (LSTM-based) speaker change detection
• doc: add tutorial for (TristouNet) neural speaker embedding

Version 0.2.1 (2017-03-28)

• feat: add LSTM-based speech activity detection
• feat: add LSTM-based speaker change detection
• improve: refactor LSTM-based speaker embedding
• feat: add librosa basic support
• feat: add SMORMS3 optimizer

Version 0.1.4 (2016-09-26)

• feat: add 'covariance_type' option to BIC segmentation

Version 0.1.3 (2016-09-23)

• chore: rename sequence generator in preparation of the release of TristouNet reproducible research package.

Version 0.1.2 (2016-09-22)

• first public version