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_powerset.py

@@ -0,0 +1,39 @@
+import numpy as np
+import mlx.core as mx
+from pyannote_diarization_3_1_mlx.powerset import Powerset, POWERSET_3_2_MAPPING
+
+
+def test_static_mapping_matches_pyannote():
+    assert POWERSET_3_2_MAPPING.shape == (7, 3)
+    expected = np.array([
+        [0, 0, 0],
+        [1, 0, 0],
+        [0, 1, 0],
+        [0, 0, 1],
+        [1, 1, 0],
+        [1, 0, 1],
+        [0, 1, 1],
+    ], dtype=np.float32)
+    np.testing.assert_array_equal(POWERSET_3_2_MAPPING, expected)
+
+
+def test_to_multilabel_hard_argmax():
+    p = Powerset()
+    # frame 0 → class 1 (S1 only), frame 1 → class 4 (S1+S2), frame 2 → class 0
+    logits = mx.array([
+        [0.0, 5.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0, 0.0, 5.0, 0.0, 0.0],
+        [5.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+    ])
+    out = p.to_multilabel(logits)
+    out_np = np.asarray(out)
+    np.testing.assert_array_equal(out_np[0], [1, 0, 0])
+    np.testing.assert_array_equal(out_np[1], [1, 1, 0])
+    np.testing.assert_array_equal(out_np[2], [0, 0, 0])
+
+
+def test_to_multilabel_shape():
+    p = Powerset()
+    logits = mx.zeros((589, 7))
+    out = p.to_multilabel(logits)
+    assert out.shape == (589, 3)