Initial commit: Radio Susan scripts, configs, and SFX
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Caine
Executable
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#!/usr/bin/env python3
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"""Analyse audio tracks with Essentia. Run daily via cron.
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Use venv: /var/lib/radio/venv/bin/python3 /var/lib/radio/analyse_tracks.py
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"""
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import math
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import os
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import sqlite3
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import subprocess
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import sys
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import tempfile
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import time
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from pathlib import Path
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FEATURES_DB = "/var/lib/radio/audio_features.db"
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MUSIC_ROOT = "/disks/Plex/Music/"
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SUPPORTED = {".flac", ".mp3", ".ogg", ".opus", ".m4a", ".wav"}
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# Energy percentile breakpoints from bulk analysis of ~6500 tracks.
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# Maps raw log-energy values to even 0-1 distribution.
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# Index 0 = 0th percentile, index 20 = 100th percentile (every 5%).
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ENERGY_BREAKPOINTS = [
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0.0, 0.7148, 0.7581, 0.7801, 0.7999, 0.8163, 0.8289, 0.8401,
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0.8493, 0.8587, 0.8667, 0.8744, 0.8811, 0.8877, 0.8949, 0.9018,
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0.9089, 0.9169, 0.9252, 0.9377, 1.0,
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]
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def normalize_energy(raw_log_energy: float) -> float:
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"""Map a raw log-energy value to 0-1 using the library's percentile curve."""
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bp = ENERGY_BREAKPOINTS
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if raw_log_energy <= bp[0]:
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return 0.0
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if raw_log_energy >= bp[-1]:
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return 1.0
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# Find which bucket it falls in and interpolate
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for i in range(1, len(bp)):
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if raw_log_energy <= bp[i]:
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frac = (raw_log_energy - bp[i - 1]) / (bp[i] - bp[i - 1]) if bp[i] != bp[i - 1] else 0.5
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return round((i - 1 + frac) / (len(bp) - 1), 4)
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return 1.0
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def init_db():
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"""Create features DB and table if needed."""
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conn = sqlite3.connect(FEATURES_DB)
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conn.execute("PRAGMA journal_mode=WAL")
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conn.execute("""
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CREATE TABLE IF NOT EXISTS features (
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path TEXT PRIMARY KEY,
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energy REAL,
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bpm REAL,
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loudness REAL,
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danceability REAL,
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analysed_at REAL
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)
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""")
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conn.commit()
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return conn
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def get_analysed_paths(conn):
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"""Return set of already-analysed file paths."""
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cur = conn.execute("SELECT path FROM features")
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return {row[0] for row in cur}
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def find_audio_files():
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"""Walk music root for all supported audio files."""
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root = Path(MUSIC_ROOT)
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files = []
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for p in root.rglob("*"):
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if p.suffix.lower() in SUPPORTED and p.is_file():
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files.append(str(p))
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return files
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def analyse_track(filepath):
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"""Extract audio features using Essentia. Returns dict or None on error."""
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try:
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import essentia
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import essentia.standard as es
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except ImportError:
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print("Error: essentia not installed. Install with: pip install essentia", file=sys.stderr)
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sys.exit(1)
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# Essentia's bundled decoder segfaults on opus and struggles with some
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# m4a/ogg files. Pre-decode these to a temp WAV via ffmpeg.
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NEEDS_PREDECODE = {".opus", ".ogg", ".m4a"}
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tmp_wav = None
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load_path = filepath
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if Path(filepath).suffix.lower() in NEEDS_PREDECODE:
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try:
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tmp_wav = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
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tmp_wav.close()
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subprocess.run(
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["ffmpeg", "-y", "-i", filepath, "-ac", "1", "-ar", "22050", "-sample_fmt", "s16", "-f", "wav", tmp_wav.name],
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capture_output=True, timeout=15,
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)
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load_path = tmp_wav.name
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except Exception as e:
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print(f" Skipping (ffmpeg decode error): {e}", file=sys.stderr)
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if tmp_wav and os.path.exists(tmp_wav.name):
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os.unlink(tmp_wav.name)
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return None
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try:
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sr = 22050 if tmp_wav else 44100
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audio = es.MonoLoader(filename=load_path, sampleRate=sr)()
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except Exception as e:
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print(f" Skipping (load error): {e}", file=sys.stderr)
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if tmp_wav and os.path.exists(tmp_wav.name):
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os.unlink(tmp_wav.name)
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return None
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finally:
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if tmp_wav and os.path.exists(tmp_wav.name):
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os.unlink(tmp_wav.name)
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# Energy — raw log-energy mapped through percentile curve
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try:
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raw_energy = es.Energy()(audio)
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raw_log = min(1.0, math.log1p(raw_energy) / 15.0)
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energy_norm = normalize_energy(raw_log)
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energy_raw = raw_log
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except Exception:
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energy_norm = 0.5
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energy_raw = 0.5
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# BPM
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try:
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bpm, _, _, _, _ = es.RhythmExtractor2013()(audio)
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if bpm < 30 or bpm > 300:
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bpm = 0.0
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except Exception:
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bpm = 0.0
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# Loudness
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try:
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loudness = es.Loudness()(audio)
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except Exception:
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loudness = 0.0
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# Danceability
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try:
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danceability, _ = es.Danceability()(audio)
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except Exception:
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danceability = 0.0
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return {
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"energy": round(energy_norm, 4),
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"energy_raw": round(energy_raw, 4),
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"bpm": round(bpm, 2),
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"loudness": round(loudness, 4),
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"danceability": round(danceability, 4),
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}
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def main():
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print(f"Essentia track analyser — {time.strftime('%Y-%m-%d %H:%M:%S')}")
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conn = init_db()
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analysed = get_analysed_paths(conn)
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print(f"Already analysed: {len(analysed)} tracks")
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all_files = find_audio_files()
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print(f"Found on disk: {len(all_files)} audio files")
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pending = [f for f in all_files if f not in analysed]
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print(f"To analyse: {len(pending)} new tracks")
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if not pending:
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print("Nothing to do.")
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conn.close()
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return
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done = 0
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errors = 0
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start = time.time()
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for i, filepath in enumerate(pending):
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if (i + 1) % 100 == 0 or i == 0:
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elapsed = time.time() - start
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rate = (done / elapsed) if elapsed > 0 and done > 0 else 0
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eta = ((len(pending) - i) / rate / 60) if rate > 0 else 0
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print(f" [{i+1}/{len(pending)}] {rate:.1f} tracks/sec, ETA {eta:.0f} min")
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features = analyse_track(filepath)
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if features is None:
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errors += 1
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continue
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try:
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conn.execute(
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"INSERT OR REPLACE INTO features (path, energy, energy_raw, bpm, loudness, danceability, analysed_at) VALUES (?, ?, ?, ?, ?, ?, ?)",
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(filepath, features["energy"], features["energy_raw"], features["bpm"], features["loudness"], features["danceability"], time.time()),
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)
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if done % 50 == 0:
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conn.commit()
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done += 1
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except Exception as e:
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print(f" DB error for {filepath}: {e}", file=sys.stderr)
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errors += 1
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conn.commit()
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conn.close()
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elapsed = time.time() - start
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print(f"Done: {done} analysed, {errors} errors, {elapsed:.1f}s total")
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if __name__ == "__main__":
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main()
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