luke
f3c91fc385
- Devon: more conversational when addressed directly (500 tokens, 3-5 sentences) - Devon: monitor prompt rewritten to encourage more contributions - Devon: polling interval 15s (was 30s), removed 2-message minimum - Whisper: no fuzzy name matching for 3-char names, require first letter match - fetch_music.py: post-fetch vocal detection filter using musicinfo tags - scan_music_vocals.py: new script to scan existing library for vocal tracks Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
123 lines
3.6 KiB
Python
123 lines
3.6 KiB
Python
"""Scan music directory for tracks that contain vocals/lyrics.
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Uses Whisper to transcribe a sample from each track — if it picks up
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actual words, the track likely has vocals.
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Usage:
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python scan_music_vocals.py # scan and report
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python scan_music_vocals.py --delete # scan and delete vocal tracks
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"""
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import argparse
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import sys
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from pathlib import Path
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import librosa
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import numpy as np
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from faster_whisper import WhisperModel
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MUSIC_DIR = Path(__file__).parent / "music"
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WHISPER_MODEL = "distil-large-v3"
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# Words Whisper hallucinates on silence/instrumental — ignore these
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HALLUCINATION_PHRASES = {
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"thank you", "thanks for watching", "subscribe", "like and subscribe",
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"please subscribe", "thank you for watching", "thanks for listening",
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"you", "the end", "bye", "okay",
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}
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def scan_track(model: WhisperModel, filepath: Path) -> tuple[bool, str]:
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"""Check a single track for vocals. Returns (has_vocals, transcription)."""
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try:
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audio, sr = librosa.load(str(filepath), sr=16000, mono=True)
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except Exception as e:
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return False, f"[load error: {e}]"
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duration = len(audio) / sr
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if duration < 10:
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return False, "[too short]"
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# Sample 30s from the middle (most likely to have vocals)
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mid = len(audio) // 2
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half_window = int(15 * sr) # 15s each side
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start = max(0, mid - half_window)
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end = min(len(audio), mid + half_window)
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sample = audio[start:end]
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segments, info = model.transcribe(
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sample,
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beam_size=3,
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language="en",
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vad_filter=True,
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vad_parameters=dict(min_speech_duration_ms=500),
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)
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segments_list = list(segments)
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text = " ".join(s.text for s in segments_list).strip()
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# Filter out Whisper hallucinations
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text_lower = text.lower().strip()
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if text_lower in HALLUCINATION_PHRASES or len(text_lower) < 4:
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return False, ""
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# If Whisper found substantial text, it's likely vocals
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word_count = len(text.split())
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has_vocals = word_count >= 3
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return has_vocals, text
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def main():
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parser = argparse.ArgumentParser(description="Scan music for vocal tracks")
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parser.add_argument("--delete", action="store_true", help="Delete tracks with vocals")
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args = parser.parse_args()
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audio_files = sorted(
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f for f in MUSIC_DIR.iterdir()
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if f.suffix.lower() in {".mp3", ".wav", ".ogg", ".flac"}
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)
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if not audio_files:
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print("No audio files found in music/")
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return
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print(f"Loading Whisper {WHISPER_MODEL}...")
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model = WhisperModel(WHISPER_MODEL, device="cpu", compute_type="int8")
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print(f"Scanning {len(audio_files)} tracks for vocals...\n")
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vocal_tracks = []
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for i, f in enumerate(audio_files, 1):
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print(f"[{i}/{len(audio_files)}] {f.name}...", end=" ", flush=True)
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has_vocals, text = scan_track(model, f)
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if has_vocals:
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print(f"VOCALS: {text[:80]}")
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vocal_tracks.append((f, text))
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else:
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print("OK")
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print(f"\n{'='*60}")
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print(f"Results: {len(vocal_tracks)} tracks with vocals out of {len(audio_files)}\n")
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if not vocal_tracks:
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print("All tracks appear to be instrumental!")
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return
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for f, text in vocal_tracks:
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print(f" {f.name}")
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print(f" Lyrics: {text[:120]}")
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print()
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if args.delete:
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print(f"Deleting {len(vocal_tracks)} vocal tracks...")
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for f, _ in vocal_tracks:
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f.unlink()
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print(f" Deleted: {f.name}")
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print("Done.")
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else:
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print("Run with --delete to remove these tracks.")
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if __name__ == "__main__":
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main()
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