3.1.0

## TL;DR

[`pyannote/speaker-diarization-3.1`](https://hf.co/pyannote/speaker-diarization-3.1) no longer requires [unpopular](https://github.com/pyannote/pyannote-audio/issues/1537) 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`