2b1a3c1312
Byte-parity with pyannote-PyTorch reference (cosine 0.763718 identical at 6 decimals on 200 cross-window slot pairs). 2.5x faster than pyannote-MPS on Apple Silicon native. Extracted from gitea.tavportal.com/olivier/MLX_CONVERTOR commit 5f9eafa.
58 lines
1.9 KiB
Markdown
58 lines
1.9 KiB
Markdown
# pyannote-speaker-diarization-3.1-mlx
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First MLX port of pyannote-speaker-diarization-3.1 with byte-parity to the PyTorch reference. 2.5x faster than pyannote-MPS on Apple Silicon native.
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## Install
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```bash
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uv add "pyannote-speaker-diarization-3.1-mlx @ git+https://gitea.tavportal.com/olivier/pyannote-speaker-diarization-3.1-mlx.git"
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```
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## Quickstart
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```python
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from pyannote_diarization_3_1_mlx import MlxDiarizationPipeline
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pipeline = MlxDiarizationPipeline.from_pretrained("pyannote/speaker-diarization-3.1")
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diarization = pipeline("audio.wav")
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for turn, _, speaker in diarization.itertracks(yield_label=True):
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print(f"{turn.start:.1f}s - {turn.end:.1f}s {speaker}")
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```
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## Parity
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| Evidence | MLX | Reference | Result |
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| --- | --- | --- | --- |
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| Cosine distance (200 cross-window pairs) | mean=0.763718 | pyannote-PyTorch mean=0.763718 | identical at 6 decimals |
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| 5h10 bench | 173s / 44 speakers / 1.27 GB | pyannote-MPS 431s / 43 speakers / 1.72 GB | Cross-DER 0.076 |
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## Architecture
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SincNet → BiLSTM → Powerset(3,2) head + WeSpeaker ResNet34 speaker embedding + AgglomerativeClustering wrapper.
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## Module Naming
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The repository name is `pyannote-speaker-diarization-3.1-mlx`; the Python import is `pyannote_diarization_3_1_mlx`. The import name follows PEP 8 and embeds the pyannote model version so future 4.0 ports can co-install.
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## Citation
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This project ports the pyannote speaker diarization 3.1 pipeline architecture to MLX. Please cite the original pyannote.audio work when using this package:
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```bibtex
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@inproceedings{Plaquet23,
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author = {Alexis Plaquet and Hervé Bredin},
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title = {{Powerset multi-class cross entropy loss for neural speaker diarization}},
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booktitle = {Proc. INTERSPEECH 2023},
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year = {2023},
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}
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```
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## Provenance
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Extracted from MLX_CONVERTOR/src/mlxconv/diar at commit 5f9eafa. Maintained at https://gitea.tavportal.com/olivier/pyannote-speaker-diarization-3.1-mlx.
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## License
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MIT
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