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.

tests/unit/test_diar_pipeline_smoke.py

@@ -0,0 +1,25 @@
+"""Smoke test for MlxDiarizationPipeline orchestrator.
+
+Mocks all sub-components so no HF downloads or real inference is needed.
+30 s of silence → powerset returns all zeros → no active speaker slots → empty annotation.
+"""
+import numpy as np
+import mlx.core as mx
+from unittest.mock import MagicMock
+from pyannote_diarization_3_1_mlx.pipeline import MlxDiarizationPipeline
+
+
+def test_pipeline_smoke_on_30s_zeros(mocker):
+    p = MlxDiarizationPipeline.__new__(MlxDiarizationPipeline)
+    p._segmentation = MagicMock()
+    p._embedding = MagicMock()
+    # mock seg → all class 0 (silence) → no slots → empty annotation
+    p._segmentation.return_value = mx.zeros((1, 589, 7))
+    p._powerset = MagicMock()
+    p._powerset.to_multilabel.return_value = mx.zeros((589, 3))
+    p._embedding.return_value = mx.ones((1, 256))
+    # 30 s of silence
+    audio = np.zeros(30 * 16000, dtype=np.float32)
+    annotation = p({"waveform": mx.array(audio)[None, :], "sample_rate": 16000})
+    # silence → no turns
+    assert len(list(annotation.itertracks())) == 0