Devon personality + Whisper name fix + music vocal filtering

- 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>

scan_music_vocals.py

@@ -0,0 +1,122 @@
+"""Scan music directory for tracks that contain vocals/lyrics.
+
+Uses Whisper to transcribe a sample from each track — if it picks up
+actual words, the track likely has vocals.
+
+Usage:
+    python scan_music_vocals.py              # scan and report
+    python scan_music_vocals.py --delete     # scan and delete vocal tracks
+"""
+
+import argparse
+import sys
+from pathlib import Path
+
+import librosa
+import numpy as np
+from faster_whisper import WhisperModel
+
+MUSIC_DIR = Path(__file__).parent / "music"
+WHISPER_MODEL = "distil-large-v3"
+
+# Words Whisper hallucinates on silence/instrumental — ignore these
+HALLUCINATION_PHRASES = {
+    "thank you", "thanks for watching", "subscribe", "like and subscribe",
+    "please subscribe", "thank you for watching", "thanks for listening",
+    "you", "the end", "bye", "okay",
+}
+
+
+def scan_track(model: WhisperModel, filepath: Path) -> tuple[bool, str]:
+    """Check a single track for vocals. Returns (has_vocals, transcription)."""
+    try:
+        audio, sr = librosa.load(str(filepath), sr=16000, mono=True)
+    except Exception as e:
+        return False, f"[load error: {e}]"
+
+    duration = len(audio) / sr
+    if duration < 10:
+        return False, "[too short]"
+
+    # Sample 30s from the middle (most likely to have vocals)
+    mid = len(audio) // 2
+    half_window = int(15 * sr)  # 15s each side
+    start = max(0, mid - half_window)
+    end = min(len(audio), mid + half_window)
+    sample = audio[start:end]
+
+    segments, info = model.transcribe(
+        sample,
+        beam_size=3,
+        language="en",
+        vad_filter=True,
+        vad_parameters=dict(min_speech_duration_ms=500),
+    )
+    segments_list = list(segments)
+    text = " ".join(s.text for s in segments_list).strip()
+
+    # Filter out Whisper hallucinations
+    text_lower = text.lower().strip()
+    if text_lower in HALLUCINATION_PHRASES or len(text_lower) < 4:
+        return False, ""
+
+    # If Whisper found substantial text, it's likely vocals
+    word_count = len(text.split())
+    has_vocals = word_count >= 3
+
+    return has_vocals, text
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Scan music for vocal tracks")
+    parser.add_argument("--delete", action="store_true", help="Delete tracks with vocals")
+    args = parser.parse_args()
+
+    audio_files = sorted(
+        f for f in MUSIC_DIR.iterdir()
+        if f.suffix.lower() in {".mp3", ".wav", ".ogg", ".flac"}
+    )
+
+    if not audio_files:
+        print("No audio files found in music/")
+        return
+
+    print(f"Loading Whisper {WHISPER_MODEL}...")
+    model = WhisperModel(WHISPER_MODEL, device="cpu", compute_type="int8")
+
+    print(f"Scanning {len(audio_files)} tracks for vocals...\n")
+
+    vocal_tracks = []
+    for i, f in enumerate(audio_files, 1):
+        print(f"[{i}/{len(audio_files)}] {f.name}...", end=" ", flush=True)
+        has_vocals, text = scan_track(model, f)
+        if has_vocals:
+            print(f"VOCALS: {text[:80]}")
+            vocal_tracks.append((f, text))
+        else:
+            print("OK")
+
+    print(f"\n{'='*60}")
+    print(f"Results: {len(vocal_tracks)} tracks with vocals out of {len(audio_files)}\n")
+
+    if not vocal_tracks:
+        print("All tracks appear to be instrumental!")
+        return
+
+    for f, text in vocal_tracks:
+        print(f"  {f.name}")
+        print(f"    Lyrics: {text[:120]}")
+        print()
+
+    if args.delete:
+        print(f"Deleting {len(vocal_tracks)} vocal tracks...")
+        for f, _ in vocal_tracks:
+            f.unlink()
+            print(f"  Deleted: {f.name}")
+        print("Done.")
+    else:
+        print("Run with --delete to remove these tracks.")
+
+
+if __name__ == "__main__":
+    main()