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273 lines
8.7 KiB
Python
273 lines
8.7 KiB
Python
#!/usr/bin/env python3
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# /// script
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# requires-python = ">=3.10"
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# dependencies = ["librosa>=0.10", "numpy>=1.24"]
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# ///
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"""Detailed tempo analysis -- shows BPM over time to detect tempo changes
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and off-beats.
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Usage:
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python tempo-detail.py <audio-file> [options]
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# Analyze a single track
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python tempo-detail.py track.mp3
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# JSON output to file
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python tempo-detail.py track.mp3 --format json -o results.json
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Exit codes:
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0 = success
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1 = invalid arguments or runtime error
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2 = missing dependencies
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"""
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import argparse
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import json
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import sys
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from datetime import datetime, timezone
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent / "_shared"))
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from audio_deps import require_audio_deps
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SCRIPT_NAME = "tempo-detail"
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VERSION = "1.0.0"
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def analyze_tempo_text(filepath):
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"""Run tempo analysis with text output (original format)."""
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import numpy as np
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print(f"Loading: {filepath}")
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y, sr = librosa.load(filepath, sr=22050)
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duration = librosa.get_duration(y=y, sr=sr)
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print(f"Duration: {int(duration//60)}:{int(duration%60):02d}")
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# Overall tempo
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tempo_overall, beats = librosa.beat.beat_track(y=y, sr=sr)
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tempo_val = float(tempo_overall[0]) if hasattr(tempo_overall, '__len__') else float(tempo_overall)
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print(f"\nOverall BPM: {tempo_val:.1f}")
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# Beat times
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beat_times = librosa.frames_to_time(beats, sr=sr)
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if len(beat_times) < 4:
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print("Too few beats detected for detailed analysis.")
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return
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# Inter-beat intervals
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ibis = np.diff(beat_times)
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local_bpms = 60.0 / ibis
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# Show tempo in ~15-second windows
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print(f"\n{'Time Window':<20} {'Avg BPM':>8} {'Min BPM':>8} {'Max BPM':>8} {'Stability':>10}")
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print("-" * 60)
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window_size = 15 # seconds
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num_windows = int(np.ceil(duration / window_size))
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for i in range(num_windows):
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start = i * window_size
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end = min((i + 1) * window_size, duration)
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mask = (beat_times[:-1] >= start) & (beat_times[:-1] < end)
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window_bpms = local_bpms[mask]
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if len(window_bpms) > 0:
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avg = np.mean(window_bpms)
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mn = np.min(window_bpms)
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mx = np.max(window_bpms)
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std = np.std(window_bpms)
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stability = "steady" if std < 5 else "slight variation" if std < 15 else "TEMPO CHANGE"
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time_label = f"{int(start//60)}:{int(start%60):02d}-{int(end//60)}:{int(end%60):02d}"
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print(f"{time_label:<20} {avg:>8.1f} {mn:>8.1f} {mx:>8.1f} {stability:>10}")
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# Detect significant tempo shifts between consecutive beats
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print("\n--- Potential Tempo Events ---")
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found = False
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for i in range(len(local_bpms) - 1):
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diff = abs(local_bpms[i+1] - local_bpms[i])
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if diff > 20:
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t = beat_times[i+1]
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print(f" {int(t//60)}:{int(t%60):02d}.{int((t%1)*10)} \u2014 BPM jumps from {local_bpms[i]:.0f} to {local_bpms[i+1]:.0f} (\u0394{diff:.0f})")
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found = True
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if not found:
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print(" No significant tempo shifts detected (all beat-to-beat changes < 20 BPM)")
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# Odd time / irregular beat detection
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print("\n--- Beat Regularity ---")
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median_ibi = np.median(ibis)
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irregular = []
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for i, ibi in enumerate(ibis):
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ratio = ibi / median_ibi
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if ratio < 0.75 or ratio > 1.33:
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t = beat_times[i]
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pct = (ratio - 1) * 100
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irregular.append((t, ratio, pct))
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if irregular:
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print(f" {len(irregular)} irregular beats detected (>33% deviation from median):")
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for t, ratio, pct in irregular[:15]:
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label = "shorter" if ratio < 1 else "longer"
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print(f" {int(t//60)}:{int(t%60):02d}.{int((t%1)*10)} \u2014 beat is {abs(pct):.0f}% {label} than expected")
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else:
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print(" All beats within normal variance \u2014 consistent 4/4 feel")
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def analyze_tempo_json(filepath):
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"""Run tempo analysis and return structured data for JSON output."""
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import numpy as np
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y, sr = librosa.load(filepath, sr=22050)
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duration = librosa.get_duration(y=y, sr=sr)
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tempo_overall, beats = librosa.beat.beat_track(y=y, sr=sr)
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tempo_val = float(tempo_overall[0]) if hasattr(tempo_overall, '__len__') else float(tempo_overall)
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beat_times = librosa.frames_to_time(beats, sr=sr)
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if len(beat_times) < 4:
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return {
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"script": SCRIPT_NAME,
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"version": VERSION,
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"timestamp": datetime.now(timezone.utc).isoformat(),
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"status": "pass",
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"metrics": {
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"file": str(Path(filepath).name),
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"duration_seconds": round(duration, 2),
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"bpm_overall": round(tempo_val, 1),
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"beats_detected": len(beat_times),
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"note": "Too few beats for detailed analysis",
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},
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"findings": [],
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"summary": {"total": 0},
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}
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ibis = np.diff(beat_times)
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local_bpms = 60.0 / ibis
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# Tempo windows
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window_size = 15
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num_windows = int(np.ceil(duration / window_size))
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windows = []
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for i in range(num_windows):
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start = i * window_size
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end = min((i + 1) * window_size, duration)
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mask = (beat_times[:-1] >= start) & (beat_times[:-1] < end)
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window_bpms = local_bpms[mask]
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if len(window_bpms) > 0:
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avg = float(np.mean(window_bpms))
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mn = float(np.min(window_bpms))
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mx = float(np.max(window_bpms))
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std = float(np.std(window_bpms))
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stability = "steady" if std < 5 else "slight_variation" if std < 15 else "tempo_change"
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windows.append({
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"time_start": start,
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"time_end": round(end, 2),
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"avg_bpm": round(avg, 1),
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"min_bpm": round(mn, 1),
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"max_bpm": round(mx, 1),
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"std_bpm": round(std, 2),
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"stability": stability,
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})
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# Tempo events (>20 BPM jump)
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tempo_events = []
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for i in range(len(local_bpms) - 1):
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diff = abs(local_bpms[i+1] - local_bpms[i])
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if diff > 20:
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t = float(beat_times[i+1])
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tempo_events.append({
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"time": round(t, 2),
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"from_bpm": round(float(local_bpms[i]), 1),
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"to_bpm": round(float(local_bpms[i+1]), 1),
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"delta": round(float(diff), 1),
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})
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# Beat regularity
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median_ibi = float(np.median(ibis))
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irregular_beats = []
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for i, ibi in enumerate(ibis):
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ratio = ibi / median_ibi
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if ratio < 0.75 or ratio > 1.33:
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t = float(beat_times[i])
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pct = (ratio - 1) * 100
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irregular_beats.append({
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"time": round(t, 2),
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"ratio": round(float(ratio), 3),
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"deviation_pct": round(float(abs(pct)), 1),
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"direction": "shorter" if ratio < 1 else "longer",
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})
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return {
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"script": SCRIPT_NAME,
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"version": VERSION,
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"timestamp": datetime.now(timezone.utc).isoformat(),
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"status": "pass",
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"metrics": {
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"file": str(Path(filepath).name),
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"duration_seconds": round(duration, 2),
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"bpm_overall": round(tempo_val, 1),
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"beats_detected": len(beat_times),
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"median_inter_beat_interval": round(median_ibi, 4),
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"tempo_windows": windows,
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"tempo_events": tempo_events,
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"irregular_beats": irregular_beats,
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"irregular_beat_count": len(irregular_beats),
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},
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"findings": [],
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"summary": {"total": 0},
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}
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def main():
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require_audio_deps()
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import librosa as _librosa # noqa: E402
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import numpy as np # noqa: E402, F401
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# Make librosa available to module-level helper functions
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globals()["librosa"] = _librosa
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parser = argparse.ArgumentParser(
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description="Detailed tempo analysis -- BPM over time, stability, beat regularity.",
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)
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parser.add_argument(
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"audio_file",
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help="Path to the audio file to analyze",
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)
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parser.add_argument(
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"--format",
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choices=["json", "text"],
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default="json",
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dest="output_format",
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help="Output format (default: json)",
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)
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parser.add_argument(
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"-o", "--output",
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default=None,
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help="Output file path (default: stdout)",
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)
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args = parser.parse_args()
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if args.output_format == "text":
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analyze_tempo_text(args.audio_file)
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else:
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result = analyze_tempo_json(args.audio_file)
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output = json.dumps(result, indent=2)
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if args.output:
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Path(args.output).write_text(output + "\n")
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else:
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print(output)
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if __name__ == "__main__":
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main()
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