• fix(pipeline): add missing support for subfolder in Pipeline.from_pretrained
• feat(pipeline): add basic support for (sequential) batch inference

• improve(telemetry): reduce number of sent packages @litdarya
• feat(cli): add --average-case option to optimize command @antoinelaurent
• fix(task): fix Task.prepare_data to support saving preprocessors that produce int values in metadata @lylyhan

• feat(sample): add transcription of sample file
• setup: relax torch dependencies constraints
• fix(pipeline): fix HF authentication for Speechbrain speaker embedding @krisoye

• feat(cli): add --revision option to most CLI commands
• feat(util): add Calibration.safe_transform method (supports NaNs as well as any shape)
• fix(model): fix Model.from_pretrained to support lightning 2.6+
• setup: update pyannote-database dependency to 6.1+

• BREAKING(util): make Binarize.__call__ return string tracks (instead of int) @benniekiss
• fix(torch): pin torch, torchcodec, and torchaudio versions to avoid segmentation fault
• fix(pyannoteAI): update pyannoteAI wrapper to return both regular and exclusive diarization
• feat(pipeline): add Pipeline.cuda() convenience method @tkanarsky
• feat(pipeline): add preload option to base Pipeline.__call__ to force preloading audio in memory (@antoinelaurent)
• feat(cli): add option to apply pipeline on a directory of audio files
• improve(util): make permutate faster thanks to vectorized cost function

• feat: allow passing preloaded pipeline config to get_pipeline
• setup: update pyannoteai-sdk dependency to 0.3.0
• fix: relax version constraint on OpenTelemetry dependencies
• improve: warn (instead of raise) when passing unsupported arguments to speaker diarization pipeline

pyannote/speaker-diarization-community-1 pretrained pipeline relies on VBx clustering instead of agglomerative hierarchical clustering (as suggested by BUT Speech@FIT researchers Petr Pálka and Jiangyu Han).

pyannote/speaker-diarization-community-1 pretrained pipeline returns a new exclusive speaker diarization, on top of the regular speaker diarization. This is a feature which is backported from our latest commercial model that simplifies the reconciliation between fine-grained speaker diarization timestamps and (sometimes not so precise) transcription timestamps.

from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained(
    "pyannote/speaker-diarization-community-1", token="huggingface-access-token")
output = pipeline("/path/to/conversation.wav")
print(output.speaker_diarization)            # regular speaker diarization
print(output.exclusive_speaker_diarization)  # exclusive speaker diarization

Metadata caching and optimized dataloaders make training on large scale datasets much faster.
This led to a 15x speed up on pyannoteAI internal large scale training.

Change one line of code to use pyannoteAI premium models and enjoy more accurate speaker diarization.

from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained(
-    "pyannote/speaker-diarization-community-1", token="huggingface-access-token")
+    "pyannote/speaker-diarization-precision-2, token="pyannoteAI-api-key")
diarization = pipeline("/path/to/conversation.wav")

Pipelines can now be stored alongside their internal models in the same repository, streamlining fully offline use.

1. Accept pyannote/speaker-diarization-community-1 pipeline user agreement

2. Clone the pipeline repository from Huggingface (if prompted for a password, use a Huggingface access token with correct permissions)

   $ git lfs install
   $ git clone https://hf.co/pyannote/speaker-diarization-community-1 /path/to/directory/pyannote-speaker-diarization-community-1

3. Enjoy!

   # load pipeline from disk (works without internet connection)
   from pyannote.audio import Pipeline
   pipeline = Pipeline.from_pretrained('/path/to/directory/pyannote-speaker-diarization-community-1')
   
   # run the pipeline locally on your computer
   diarization = pipeline("audio.wav")

With the optional telemetry feature in pyannote.audio, you can choose to send anonymous usage metrics to help the pyannote team improve the library.

• BREAKING(io): remove support for sox and soundfile audio I/O backends (only ffmpeg or in-memory audio is supported)
• BREAKING(setup): drop support to Python < 3.10
• BREAKING(hub): rename use_auth_token to token
• BREAKING(hub): drop support for {pipeline_name}@{revision} syntax in Model.from_pretrained(...) and Pipeline.from_pretrained(...) -- use new revision keyword argument instead
• BREAKING(task): remove OverlappedSpeechDetection task (part of SpeakerDiarization task)
• BREAKING(pipeline): remove OverlappedSpeechDetection and Resegmentation unmaintained pipelines (part of SpeakerDiarization)
• BREAKING(cache): rely on huggingface_hub caching directory (PYANNOTE_CACHE is no longer used)
• BREAKING(inference): Inference now only supports already instantiated models
• BREAKING(task): drop support for multilabel training in SpeakerDiarization task
• BREAKING(task): drop support for warm_up option in SpeakerDiarization task
• BREAKING(task): drop support for weigh_by_cardinality option in SpeakerDiarization task
• BREAKING(task): drop support for vad_loss option in SpeakerDiarization task
• BREAKING(chore): switch to native namespace package
• BREAKING(cli): remove deprecated pyannote-audio-train CLI

• feat(io): switch from torchaudio to torchcodec for audio I/O
• feat(pipeline): add support for VBx clustering (@Selesnyan and jyhan03)
• feat(pyannoteAI): add wrapper around pyannoteAI SDK
• improve(hub): add support for pipeline repos that also include underlying models
• feat(clustering): add support for k-means clustering
• feat(model): add wav2vec_frozen option to freeze/unfreeze wav2vec in SSeRiouSS architecture
• feat(task): add support for manual optimization in SpeakerDiarization task
• feat(utils): add hidden option to ProgressHook
• feat(utils): add FilterByNumberOfSpeakers protocol files filter
• feat(core): add Calibration class to calibrate logits/distances into probabilities
• feat(metric): add DetectionErrorRate, SegmentationErrorRate, DiarizationPrecision, and DiarizationRecall metrics
• feat(cli): add CLI to download, apply, benchmark, and optimize pipelines
• feat(cli): add CLI to strip checkpoints to their bare inference minimum

• improve(model): improve WavLM (un)freezing support for SSeRiouSS architecture (@clement-pages)
• improve(task): improve SpeakerDiarization training with manual optimization (@clement-pages)
• improve(train): speed up dataloaders
• improve(setup): switch to uv
• improve(setup): switch to lightning from pytorch-lightning
• improve(utils): improve dependency check when loading pretrained models and/or pipeline
• improve(utils): add option to skip dependency check
• improve(utils): add option to load a pretrained model checkpoint from an io.BytesIO buffer
• improve(pipeline): add option to load a pretrained pipeline from a dict (@benniekiss)

• fix(model): improve WavLM (un)freezing support for ToTaToNet architecture (@clement-pages)
• fix(separation): fix clipping issue in speech separation pipeline (@joonaskalda)
• fix(separation): fix alignment between separated sources and diarization (@Lebourdais and @clement-pages)
• fix(separation): prevent leakage removal collar from being applied to diarization (@clement-pages)
• fix(separation): fix PixIT training with manual optimization (@clement-pages)
• fix(doc): fix link to pytorch (@emmanuel-ferdman)
• fix(task): fix corner case with small (<9) number of validation samples (@antoinelaurent)
• fix(doc): fix default embedding in SpeechSeparation and SpeakerDiarization docstring (@razi-tm).

• setup: pin pyannote.{core,database,metrics,pipeline} dependencies as future releases of these packages will break the 3.x branch

• fix: (really) fix support for numpy==2.x (@metal3d)
• doc: fix Pipeline docstring (@huisman)

• setup: drop support for Python 3.8

• fix: fix support for numpy==2.x (@ibevers)
• fix: fix support for speechbrain==1.x (@Adel-Moumen)

pyannote.audio does speech separation: multi-speaker audio in, one audio channel per speaker out!

pip install pyannote.audio[separation]==3.3.0

• feat(task): add PixIT joint speaker diarization and speech separation task (with @joonaskalda)
• feat(model): add ToTaToNet joint speaker diarization and speech separation model (with @joonaskalda)
• feat(pipeline): add SpeechSeparation pipeline (with @joonaskalda)
• feat(io): add option to select torchaudio backend

• 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)

• improve(io): when available, default to using soundfile backend
• improve(pipeline): do not extract embeddings when max_speakers is set to 1
• improve(pipeline): optimize memory usage of most pipelines (#1713 by @benniekiss)

• 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

• 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)

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

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

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

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

• 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

• 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

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

• 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

• fix(pipeline): fix WeSpeaker GPU support

• 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(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

• 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

• 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+

• 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

• 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

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

• last release before complete rewriting

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

• chore: switch from keras to pytorch (with tensorboard support)
• improve: faster & better training (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)

• 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

• 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

• feat: add 'covariance_type' option to BIC segmentation

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

• first public version