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CHANGELOG.md

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Changelog

Version 3.0 (xxxx-xx-xx)

Highlights

  • "Harder". Fixed major reproducibility issue with Ampere (A100) NVIDIA GPUs In case you tried pyannote.audio pretrained pipelines in the past on Ampera (A100) NVIDIA GPUs and were disappointed by the accuracy, please give it another try with this new version.
  • "Better".
  • "Faster".
  • "Stronger".

Breaking changes

  • BREAKING(task): rename Segmentation task to SpeakerDiarization
  • BREAKING(task): remove support for variable chunk duration for segmentation tasks
  • BREAKING(pipeline): pipeline defaults to CPU (use pipeline.to(device))
  • BREAKING(pipeline): remove SpeakerSegmentation pipeline (use SpeakerDiarization pipeline)
  • BREAKING(pipeline): remove support for FINCHClustering and HiddenMarkovModelClustering
  • BREAKING(pipeline): remove segmentation_duration parameter from SpeakerDiarization pipeline (defaults to duration of segmentation model)
  • 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.

Features and improvements

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

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
  • improve(task): shorten and improve structure of Tensorboard tags

Dependencies

  • 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+ and pyannote.database 5.0+
  • 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