feat: initial public release v0.1.0 — MLX port of pyannote-speaker-diarization-3.1

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.

README.md

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+# pyannote-speaker-diarization-3.1-mlx
+
+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.
+
+## Install
+
+```bash
+uv add "pyannote-speaker-diarization-3.1-mlx @ git+https://gitea.tavportal.com/olivier/pyannote-speaker-diarization-3.1-mlx.git"
+```
+
+## Quickstart
+
+```python
+from pyannote_diarization_3_1_mlx import MlxDiarizationPipeline
+
+pipeline = MlxDiarizationPipeline.from_pretrained("pyannote/speaker-diarization-3.1")
+diarization = pipeline("audio.wav")
+
+for turn, _, speaker in diarization.itertracks(yield_label=True):
+    print(f"{turn.start:.1f}s - {turn.end:.1f}s {speaker}")
+```
+
+## Parity
+
+| Evidence | MLX | Reference | Result |
+| --- | --- | --- | --- |
+| Cosine distance (200 cross-window pairs) | mean=0.763718 | pyannote-PyTorch mean=0.763718 | identical at 6 decimals |
+| 5h10 bench | 173s / 44 speakers / 1.27 GB | pyannote-MPS 431s / 43 speakers / 1.72 GB | Cross-DER 0.076 |
+
+## Architecture
+
+SincNet → BiLSTM → Powerset(3,2) head + WeSpeaker ResNet34 speaker embedding + AgglomerativeClustering wrapper.
+
+## Module Naming
+
+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.
+
+## Citation
+
+This project ports the pyannote speaker diarization 3.1 pipeline architecture to MLX. Please cite the original pyannote.audio work when using this package:
+
+```bibtex
+@inproceedings{Plaquet23,
+  author = {Alexis Plaquet and Hervé Bredin},
+  title = {{Powerset multi-class cross entropy loss for neural speaker diarization}},
+  booktitle = {Proc. INTERSPEECH 2023},
+  year = {2023},
+}
+```
+
+## Provenance
+
+Extracted from MLX_CONVERTOR/src/mlxconv/diar at commit 5f9eafa. Maintained at https://gitea.tavportal.com/olivier/pyannote-speaker-diarization-3.1-mlx.
+
+## License
+
+MIT