Version 3.1.1

TL;DR

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

Full changelog

Fixes

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

Version 3.1.0

TL;DR

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

Full changelog

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

TL;DR

pyannote/speaker-diarization-3.0 is now much faster when sent to GPU.

import torch
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.0")
pipeline.to(torch.device("cuda"))

Full changelog

Fixes and improvements

• fix: fix WeSpeakerPretrainedSpeakerEmbedding GPU support

Dependencies update

• setup: switch from onnxruntime to onnxruntime-gpu

Version 3.0.0

TL;DR

Better pretrained pipeline and model

• Much better overlapping speech detection with powerset pyannote/segmentation-3.0
• Much better speaker diarization performance with pyannote/speaker-diarization-3.0

Benchmark (DER %)	v2.1	v3.0
AISHELL-4	14.1	12.3
AliMeeting (channel 1)	27.4	24.3
AMI (IHM)	18.9	19.0
AMI (SDM)	27.1	22.2
AVA-AVD	-	49.1
DIHARD 3 (full)	26.9	21.7
MSDWild	-	24.6
REPERE (phase2)	8.2	7.8
VoxConverse (v0.3)	11.2	11.3

Major breaking changes

• BREAKING: pipelines now run on CPU by default
  Use pipeline.to(torch.device('cuda')) to use GPU
• BREAKING: removed SpeakerSegmentation pipeline
  Use SpeakerDiarization pipeline instead
• BREAKING: removed support for prodi.gy recipes

Full changelog

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

Version 2.1.x introduces a major overhaul of pyannote.audio default speaker diarization pipeline, made of three main stages:

• neural speaker segmentation applied to a short sliding window;
• neural speaker embedding of each (local) speakers;
• (global) agglomerative clustering.

More details in the attached technical report.

Version 1.1.1

chore: do not update to pyannote.pipeline >= 2.0