41 lines
1.1 KiB
Python
41 lines
1.1 KiB
Python
"""Audio similarity scanning — MFCC-based profile matching."""
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import numpy as np
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import librosa
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from .paths import _log
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_N_MFCC = 20
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_SR = 22050
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def _extract_mfcc(path: str, sr: int = _SR) -> np.ndarray:
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"""Load audio from a file and return a mean MFCC vector (20-dim)."""
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y, _ = librosa.load(path, sr=sr, mono=True)
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mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=_N_MFCC)
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return mfcc.mean(axis=1) # average over time → (20,)
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def build_profile(clip_paths: list[str]) -> dict | None:
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"""Extract MFCCs from reference clips.
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Returns dict with:
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- mean_vector: averaged MFCC across all clips (20,)
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- clip_vectors: list of individual MFCC vectors
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Returns None if no clips could be loaded.
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"""
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vectors = []
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for p in clip_paths:
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try:
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vec = _extract_mfcc(p)
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vectors.append(vec)
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except Exception as e:
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_log(f"audio_scan: skip {p}: {e}")
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if not vectors:
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return None
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arr = np.stack(vectors)
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return {
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"mean_vector": arr.mean(axis=0),
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"clip_vectors": vectors,
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}
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