feat: design system overhaul — sidebar, AI chats, settings, brainstorm, color cleanup
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- Sidebar: dynamic brand-accent colors, brainstorm section restyled - AI chat general: popup panel with expand/collapse, hides when contextual AI open - AI chat contextual: tabs reordered (Actions first), X close button, height fix - Settings: all tabs restyled, 6 new color presets (sage, terracotta, iron, etc.) - Global color cleanup: emerald/orange hardcoded → brand-accent dynamic - Brainstorm page: orange → brand-accent throughout - PageEntry animation component added to key pages - Floating AI button: bg-brand-accent instead of hardcoded black - i18n: all 15 locales updated with new AI/billing keys - Billing: freemium quota tracking, BYOK, stripe subscription scaffolding - Admin: integrated into new design - AGENTS.md + CLAUDE.md project rules added
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Antigravity
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#!/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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"""Deep audio analysis -- chord progression, energy over time, spectral features,
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section boundaries, and harmonic/percussive separation analysis.
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Usage:
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python audio-deep-analysis.py <audio-file> [options]
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# Analyze a single track
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python audio-deep-analysis.py track.mp3
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# JSON output to file
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python audio-deep-analysis.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 os
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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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from json_archiver import resolve_archive_arg, write_archive
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SCRIPT_NAME = "audio-deep-analysis"
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VERSION = "1.0.0"
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def format_time(seconds):
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m = int(seconds // 60)
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s = int(seconds % 60)
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frac = int((seconds % 1) * 10)
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return f"{m}:{s:02d}.{frac}"
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def analyze_chords(y, sr, *, collect=False):
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"""Estimate chord/key progression over time using chroma features.
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When collect=True, returns data instead of printing.
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"""
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import numpy as np
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pitch_classes = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']
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major_profile = np.array([6.35, 2.23, 3.48, 2.33, 4.38, 4.09, 2.52, 5.19, 2.39, 3.66, 2.29, 2.88])
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minor_profile = np.array([6.33, 2.68, 3.52, 5.38, 2.60, 3.53, 2.54, 4.75, 3.98, 2.69, 3.34, 3.17])
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chroma = librosa.feature.chroma_cqt(y=y, sr=sr)
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hop_length = 512
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window_seconds = 10
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frames_per_window = int(window_seconds * sr / hop_length)
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num_windows = chroma.shape[1] // frames_per_window
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results = []
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if not collect:
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print("\n=== KEY/CHORD PROGRESSION ===")
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print(f"{'Time':<15} {'Estimated Key':<15} {'Confidence':>10} {'Dominant Notes'}")
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print("-" * 65)
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for i in range(num_windows):
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start_frame = i * frames_per_window
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end_frame = (i + 1) * frames_per_window
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chunk = chroma[:, start_frame:end_frame]
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avg = np.mean(chunk, axis=1)
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best_corr = -1
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best_key = "Unknown"
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for j in range(12):
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rolled = np.roll(avg, -j)
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maj_corr = np.corrcoef(rolled, major_profile)[0, 1]
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min_corr = np.corrcoef(rolled, minor_profile)[0, 1]
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if maj_corr > best_corr:
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best_corr = maj_corr
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best_key = f"{pitch_classes[j]} major"
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if min_corr > best_corr:
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best_corr = min_corr
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best_key = f"{pitch_classes[j]} minor"
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top_3 = np.argsort(avg)[-3:][::-1]
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dominant = ", ".join([pitch_classes[p] for p in top_3])
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start_time = i * window_seconds
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end_time = (i + 1) * window_seconds
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if collect:
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results.append({
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"time_start": start_time,
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"time_end": end_time,
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"key": best_key,
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"confidence": round(best_corr, 3),
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"dominant_notes": [pitch_classes[p] for p in top_3],
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})
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else:
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print(f"{format_time(start_time)}-{format_time(end_time):<8} {best_key:<15} {best_corr:>10.3f} {dominant}")
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return results
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def analyze_energy(y, sr, *, collect=False):
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"""Show energy/loudness over time.
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When collect=True, returns data instead of printing.
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"""
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import numpy as np
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rms = librosa.feature.rms(y=y)[0]
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hop_length = 512
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window_seconds = 5
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frames_per_window = int(window_seconds * sr / hop_length)
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max_rms = np.max(rms)
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if max_rms == 0:
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max_rms = 1
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num_windows = len(rms) // frames_per_window
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if not collect:
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print("\n=== ENERGY / LOUDNESS ARC ===")
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print(f"{'Time':<15} {'Energy':>7} {'Bar (visual)'}")
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print("-" * 60)
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energies = []
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windows = []
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for i in range(num_windows):
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start = i * frames_per_window
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end = (i + 1) * frames_per_window
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avg = np.mean(rms[start:end])
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pct = int((avg / max_rms) * 100)
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energies.append(pct)
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start_time = i * window_seconds
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if collect:
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windows.append({
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"time": start_time,
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"energy_pct": pct,
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})
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else:
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bar = "\u2588" * (pct // 2)
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print(f"{format_time(start_time):<15} {pct:>5}% {bar}")
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# Detect significant energy shifts
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shifts = []
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if not collect:
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print("\n--- Energy Shifts (>20% change) ---")
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found = False
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for i in range(1, len(energies)):
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diff = energies[i] - energies[i-1]
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if abs(diff) > 20:
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t = i * window_seconds
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direction = "UP" if diff > 0 else "DOWN"
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if collect:
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shifts.append({
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"time": t,
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"direction": direction,
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"change_pct": abs(diff),
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"from_pct": energies[i-1],
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"to_pct": energies[i],
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})
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else:
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print(f" {format_time(t)} \u2014 energy {direction} {abs(diff)}% ({energies[i-1]}% \u2192 {energies[i]}%)")
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found = True
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if not collect and not found:
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print(" No dramatic energy shifts detected (all changes < 20%)")
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return {"windows": windows, "shifts": shifts}
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def analyze_sections(y, sr, *, collect=False):
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"""Detect section boundaries using spectral novelty.
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When collect=True, returns data instead of printing.
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"""
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mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
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bounds = librosa.segment.agglomerative(mfcc, k=8)
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bound_times = librosa.frames_to_time(bounds, sr=sr)
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results = []
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if not collect:
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print("\n=== SECTION BOUNDARIES (spectral novelty) ===")
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print("Detected section changes at:")
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for i, t in enumerate(bound_times):
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if t > 0.5: # Skip very start
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if collect:
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results.append({
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"section": i + 1,
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"time": round(float(t), 2),
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})
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else:
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print(f" Section {i+1}: {format_time(t)}")
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return results
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def analyze_spectral_balance(y, sr, *, collect=False):
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"""Show low vs mid vs high frequency balance over time."""
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import numpy as np
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S = np.abs(librosa.stft(y))
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freqs = librosa.fft_frequencies(sr=sr)
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low_mask = freqs < 250
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mid_mask = (freqs >= 250) & (freqs < 2000)
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high_mask = freqs >= 2000
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window_seconds = 10
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hop_length = 512
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frames_per_window = int(window_seconds * sr / hop_length)
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num_windows = S.shape[1] // frames_per_window
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if not collect:
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print("\n=== SPECTRAL BALANCE (low/mid/high) ===")
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print(f"{'Time':<15} {'Low(<250Hz)':>12} {'Mid(250-2k)':>12} {'High(>2kHz)':>12} {'Balance'}")
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print("-" * 70)
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results = []
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for i in range(num_windows):
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start = i * frames_per_window
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end = (i + 1) * frames_per_window
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chunk = S[:, start:end]
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low = np.mean(chunk[low_mask, :])
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mid = np.mean(chunk[mid_mask, :])
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high = np.mean(chunk[high_mask, :])
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total = low + mid + high
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if total == 0:
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total = 1
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l_pct = int(low / total * 100)
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m_pct = int(mid / total * 100)
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h_pct = int(high / total * 100)
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dominant = "BASS-heavy" if l_pct > 45 else "MID-heavy" if m_pct > 50 else "balanced"
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start_time = i * window_seconds
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if collect:
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results.append({
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"time": start_time,
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"low_pct": l_pct,
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"mid_pct": m_pct,
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"high_pct": h_pct,
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"balance": dominant,
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})
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else:
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print(f"{format_time(start_time):<15} {l_pct:>10}% {m_pct:>10}% {h_pct:>10}% {dominant}")
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return results
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def format_json_output(filepath, duration, energy_data, chord_data, section_data, spectral_data):
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"""Build structured JSON output."""
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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": os.path.basename(filepath),
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"duration_seconds": round(duration, 2),
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"energy_arc": energy_data,
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"chord_progression": chord_data,
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"section_boundaries": section_data,
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"spectral_balance": spectral_data,
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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="Deep single-track audio analysis — energy, chords, sections, spectral balance.",
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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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parser.add_argument(
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"--archive", nargs="?", const="", default="",
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help=(
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"Persist full JSON output to a per-song archive. "
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"With no path: writes to docs/audio-analysis/songs/<song-slug>.json. "
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"Pass an explicit path to override. Default: ON."
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),
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)
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parser.add_argument(
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"--no-archive", dest="archive", action="store_const", const=None,
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help="Skip writing the JSON archive.",
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)
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args = parser.parse_args()
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filepath = args.audio_file
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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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if args.output_format == "text":
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print(f"Loading: {os.path.basename(filepath)}")
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print(f"Duration: {int(duration//60)}:{int(duration%60):02d}\n")
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analyze_energy(y, sr)
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analyze_chords(y, sr)
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analyze_sections(y, sr)
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analyze_spectral_balance(y, sr)
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else:
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energy_data = analyze_energy(y, sr, collect=True)
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chord_data = analyze_chords(y, sr, collect=True)
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section_data = analyze_sections(y, sr, collect=True)
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spectral_data = analyze_spectral_balance(y, sr, collect=True)
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result = format_json_output(filepath, duration, energy_data, chord_data, section_data, spectral_data)
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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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# Per-song JSON archive (default ON unless --no-archive)
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song_slug = os.path.splitext(os.path.basename(filepath))[0]
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archive_target = resolve_archive_arg("songs", song_slug, args.archive)
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if archive_target is not None:
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res = write_archive(archive_target, result)
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print(f" ARCHIVED: {res['path']} ({res['bytes_written']} bytes)", file=sys.stderr)
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if __name__ == "__main__":
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main()
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