zhukang
1c7abd81d1
- 310 registry:skill items, 1528 files, 7 categories (hub tags) - registry.json generated by @kortix/registry buildRegistry - nested SKILL.md in sn-da-* renamed SUBSKILL.md (no phantom skills) - provenance + licenses: SOURCES.md
98 lines
3.7 KiB
Python
98 lines
3.7 KiB
Python
#!/usr/bin/env python3
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"""Detect sharp audio transients (impacts, hits, claps) via spectral flux on a chosen freq band.
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Use for action montages where you want to cut on each strike: axe-on-wood, drum
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hit, fist-on-pad, ball-on-bat, wood landing on a pile, etc. The detector finds
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the broadband impulse that an impact creates, which ambient noise/wind doesn't.
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Usage:
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python3 detect_transients.py audio.wav [--band 1000:6000] \
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[--min-flux 30] [--min-gap 0.6] [--top N]
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Output: JSON list of {time, flux, db}, sorted by time, to stdout.
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Tuning notes:
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- min-flux 30 is a sane default. Real impacts register 50–300; ambient ~5–15.
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Too many false positives → raise to 40–50. Too few → drop to 20.
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- min-gap is min seconds between accepted peaks. Drumming: ~0.2; chopping: 0.6–1.0.
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- band: 1000:6000 covers axe/wood/metal impacts. Heavy thuds (kick, body shot)
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live lower (200:2000). Whistles, clinks, snare cracks live higher (4000:10000).
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"""
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import sys, json, wave, argparse
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import numpy as np
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def detect(path, band_lo=1000, band_hi=6000, min_flux=30.0, min_gap=0.6):
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w = wave.open(path, 'rb')
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if w.getnchannels() != 1 or w.getsampwidth() != 2:
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raise SystemExit(
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f"expected 16-bit mono WAV, got {w.getnchannels()}ch {w.getsampwidth()*8}-bit "
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f"— convert with: ffmpeg -i INPUT -vn -ac 1 -ar 16000 audio.wav")
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sr = w.getframerate()
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n = w.getnframes()
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data = np.frombuffer(w.readframes(n), dtype=np.int16).astype(np.float32) / 32768.0
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w.close()
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win_size = int(sr * 0.025) # 25 ms windows
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hop = int(sr * 0.01) # 10 ms hop
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n_frames = (len(data) - win_size) // hop
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if n_frames < 4:
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return []
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frames = np.lib.stride_tricks.as_strided(
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data,
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shape=(n_frames, win_size),
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strides=(data.strides[0] * hop, data.strides[0]),
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)
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spec = np.abs(np.fft.rfft(frames * np.hanning(win_size), axis=1))
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bin_lo = max(1, int(band_lo * win_size / sr))
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bin_hi = min(spec.shape[1], int(band_hi * win_size / sr))
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diff = np.diff(spec[:, bin_lo:bin_hi], axis=0)
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flux = np.sum(np.maximum(diff, 0), axis=1)
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times = (np.arange(len(flux)) + 1) * hop / sr
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rms = np.sqrt(np.mean(frames ** 2, axis=1) + 1e-10)
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db = 20 * np.log10(rms + 1e-10)
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min_gap_frames = max(1, int(min_gap * sr / hop))
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peaks = []
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last = -min_gap_frames
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for i in range(2, len(flux) - 2):
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if flux[i] < min_flux:
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continue
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if flux[i] != np.max(flux[max(0, i - 3):i + 4]):
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continue
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rec = {
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'time': round(float(times[i]), 3),
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'flux': float(flux[i]),
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'db': round(float(db[i]), 1) if i < len(db) else 0.0,
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}
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if i - last < min_gap_frames:
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if peaks and rec['flux'] > peaks[-1]['flux']:
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peaks[-1] = rec
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last = i
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continue
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peaks.append(rec)
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last = i
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return peaks
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if __name__ == '__main__':
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p = argparse.ArgumentParser()
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p.add_argument('audio_wav')
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p.add_argument('--band', default='1000:6000', help='freq band Hz, "lo:hi"')
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p.add_argument('--min-flux', type=float, default=30.0)
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p.add_argument('--min-gap', type=float, default=0.6,
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help='min seconds between accepted peaks')
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p.add_argument('--top', type=int, default=0,
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help='keep only N strongest peaks (0 = all)')
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args = p.parse_args()
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lo, hi = (int(x) for x in args.band.split(':'))
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peaks = detect(args.audio_wav, lo, hi, args.min_flux, args.min_gap)
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if args.top:
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peaks = sorted(peaks, key=lambda x: -x['flux'])[:args.top]
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peaks.sort(key=lambda x: x['time'])
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json.dump(peaks, sys.stdout, indent=2)
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sys.stdout.write('\n')
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