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/integration/test_diar_60s_smoke.py

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+"""Day-1 sanity gate. If this fails, do NOT spend further time on Plan A."""
+import time
+import numpy as np
+import librosa
+import pytest
+import soundfile as sf
+import torch
+from pyannote.audio import Pipeline
+
+from pyannote_diarization_3_1_mlx import MlxDiarizationPipeline
+
+
+@pytest.mark.integration
+def test_diar_60s_parity_vs_pyannote():
+    audio_path = "/tmp/_diar_smoke_60s.wav"
+    # use any 60s slice of the existing test audio
+    sig, _ = librosa.load("/tmp/audio_first_3min.wav", sr=16000, duration=60)
+    sf.write(audio_path, sig, 16000)
+
+    # MLX pipeline
+    mlx_pipe = MlxDiarizationPipeline.from_pretrained()
+    mlx_ann = mlx_pipe({"waveform": torch.from_numpy(sig).unsqueeze(0),
+                        "sample_rate": 16000},
+                       min_speakers=1, max_speakers=3)
+    mlx_speakers = set(mlx_ann.labels())
+
+    # pyannote PyTorch reference
+    ref_pipe = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
+    ref_out = ref_pipe({"waveform": torch.from_numpy(sig).unsqueeze(0),
+                        "sample_rate": 16000},
+                       min_speakers=1, max_speakers=3)
+    # pyannote 3.x returns the annotation directly
+    if hasattr(ref_out, "exclusive_speaker_diarization"):
+        ref_ann = ref_out.exclusive_speaker_diarization
+    else:
+        ref_ann = ref_out
+    ref_speakers = set(ref_ann.labels())
+
+    # gate: speaker count within ±1
+    assert abs(len(mlx_speakers) - len(ref_speakers)) <= 1, \
+        f"speaker count diff: mlx={len(mlx_speakers)} ref={len(ref_speakers)}"
+
+    # gate: DER < 0.30 (Hungarian-aligned)
+    from pyannote.metrics.diarization import DiarizationErrorRate
+    der = DiarizationErrorRate()
+    der_value = der(ref_ann, mlx_ann)
+    assert der_value <= 0.30, f"DER {der_value:.3f} > 0.30 (gate ≤ 0.30)"
+
+    # gate: wall-clock under 30s (MLX should be fast on M2/M3)
+    t0 = time.time()
+    mlx_pipe({"waveform": torch.from_numpy(sig).unsqueeze(0),
+              "sample_rate": 16000})
+    wall = time.time() - t0
+    assert wall < 30, f"wall {wall:.1f}s > 30s for 60s audio"