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_audio_fbank.py

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+import numpy as np
+import mlx.core as mx
+import torch
+from torchaudio.compliance import kaldi as ta_kaldi
+from pyannote_diarization_3_1_mlx.audio import kaldi_fbank, load_waveform
+
+
+def _fixed_signal(seconds: float = 3.0, sr: int = 16000):
+    t = np.linspace(0, seconds, int(seconds * sr), endpoint=False)
+    sig = (
+        0.5 * np.sin(2 * np.pi * 220 * t)
+        + 0.3 * np.sin(2 * np.pi * 880 * t)
+    ).astype(np.float32)
+    return sig
+
+
+def test_fbank_matches_torchaudio_within_1pct():
+    sig = _fixed_signal()
+    # torchaudio reference: same params as pyannote WeSpeaker
+    sig_torch = torch.from_numpy(sig).unsqueeze(0) * (1 << 15)
+    ref = ta_kaldi.fbank(
+        sig_torch,
+        num_mel_bins=80,
+        frame_length=25,
+        frame_shift=10,
+        dither=0.0,
+        window_type="hamming",
+        use_energy=False,
+        sample_frequency=16000,
+    ).numpy()  # (T, 80)
+
+    # Our MLX implementation, with same scaling and CMN
+    sig_mx = mx.array(sig)
+    out = kaldi_fbank(
+        sig_mx,
+        num_mel_bins=80,
+        frame_length_ms=25,
+        frame_shift_ms=10,
+        dither=0.0,
+        window_type="hamming",
+        use_energy=False,
+        sample_rate=16000,
+    )
+    out_np = np.asarray(out)
+    assert out_np.shape == ref.shape
+    # max abs diff should be small (kaldi-compliant, no random init)
+    diff = np.abs(out_np - ref).max()
+    assert diff < 0.05, f"max abs diff {diff:.4f}"
+
+
+def test_load_waveform_resamples_to_16k():
+    import soundfile as sf
+    import tempfile, os
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
+        sf.write(f.name, _fixed_signal(seconds=1.0, sr=22050), 22050)
+        wav_mx = load_waveform(f.name)
+        os.unlink(f.name)
+    assert wav_mx.shape[-1] == 16000  # 1 second @ 16k after resample
+    assert wav_mx.dtype == mx.float32