Skip to content

Latest commit

 

History

History
227 lines (160 loc) · 9.84 KB

CHANGELOG.md

File metadata and controls

227 lines (160 loc) · 9.84 KB
Raw

Changelog

develop

New features

  • feat(io): add option to select torchaudio backend

Fixes

  • fix(task): fix wrong train/development split when training with (some) meta-protocols (#1709)
  • fix(task): fix metadata preparation with missing validation subset (@clement-pages)

Improvements

  • improve(io): when available, default to using soundfile backend
  • improve(pipeline): do not extract embeddings when max_speakers is set to 1

Version 3.2.0 (2024-05-08)

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)
  • fix(doc): remove mention of unsupported numpy.ndarray waveform (with @Purfview)

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

Version 3.1.1 (2023-12-01)

TL;DR

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

Fixes

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