4.0.0

# Version 4.0.0

## TL;DR

### Improved speaker assignment and counting

`pyannote/speaker-diarization-community-1` pretrained pipeline relies on VBx clustering instead of agglomerative hierarchical clustering (as suggested by [BUT Speech@FIT](https://speech.fit.vut.cz/) researchers [Petr Pálka](https://github.com/Selesnyan) and [Jiangyu Han](https://github.com/jyhan03)).

### *Exclusive* speaker diarization

`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](https://www.pyannote.ai/blog/precision-2) that simplifies the reconciliation between fine-grained speaker diarization timestamps and (sometimes not so precise) transcription timestamps.

```python
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
```

### Faster training

Metadata caching and optimized dataloaders make training on large scale datasets much faster.  
This led to a 15x speed up on [pyannoteAI](https://www.pyannote.ai) internal large scale training.

### [pyannoteAI](https://www.pyannote.ai) premium speaker diarization

Change one line of code to use [pyannoteAI](https://docs.pyannote.ai) premium models and enjoy **more accurate speaker diarization**.

```diff
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")
```

### Offline (air-gapped) use

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](https://hf.co/pyannote/speaker-diarization-community-1)
2. Clone the pipeline repository from Huggingface (if prompted for a password, use a Huggingface access token with correct permissions)

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

3. Enjoy!

    ```python
    # 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")
    ```

### Telemetry

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 changes

- 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

## New features

- feat(io): switch from `torchaudio` to `torchcodec` for audio I/O
- feat(pipeline): add support for VBx clustering ([@Selesnyan](https://github.com/Selesnyan) and [jyhan03](https://github.com/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 
 
## Improvements

- improve(model): improve WavLM (un)freezing support for `SSeRiouSS` architecture ([@clement-pages](https://github.com/clement-pages/))
- improve(task): improve `SpeakerDiarization` training with manual optimization ([@clement-pages](https://github.com/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](https://github.com/benniekiss/))

## Fixes

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