File size: 12,926 Bytes
a3fa32f 892f418 a3fa32f 892f418 a3fa32f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 | #!/usr/bin/env python3
"""
VYNL AI Mastering Module
Reference matching + genre presets + loudness normalization
"""
import numpy as np
from pathlib import Path
import tempfile
try:
import librosa
import soundfile as sf
from scipy.ndimage import uniform_filter1d
HAS_LIBROSA = True
except ImportError:
HAS_LIBROSA = False
uniform_filter1d = None
try:
import pyloudnorm as pyln
HAS_PYLOUDNORM = True
except ImportError:
HAS_PYLOUDNORM = False
# ============================================================================
# MASTERING PRESETS
# ============================================================================
PRESETS = {
'Balanced': {
'eq_low': 0,
'eq_mid': 0,
'eq_high': 0,
'compression_ratio': 3,
'compression_threshold': -18,
'target_lufs': -14,
},
'Warm': {
'eq_low': 2,
'eq_mid': -1,
'eq_high': -2,
'compression_ratio': 2.5,
'compression_threshold': -16,
'target_lufs': -14,
},
'Bright': {
'eq_low': -1,
'eq_mid': 1,
'eq_high': 3,
'compression_ratio': 3,
'compression_threshold': -18,
'target_lufs': -13,
},
'Punchy': {
'eq_low': 3,
'eq_mid': 0,
'eq_high': 1,
'compression_ratio': 4,
'compression_threshold': -20,
'target_lufs': -12,
},
'Reference Match': {
'eq_low': 0,
'eq_mid': 0,
'eq_high': 0,
'compression_ratio': 3,
'compression_threshold': -18,
'target_lufs': -14,
},
}
# ============================================================================
# AUDIO ANALYSIS
# ============================================================================
def analyze_audio(audio_path):
"""Analyze audio file for mastering metrics"""
if not HAS_LIBROSA:
return None
try:
y, sr = librosa.load(audio_path, sr=44100, mono=False)
# Handle mono/stereo
if y.ndim == 1:
y_mono = y
else:
y_mono = librosa.to_mono(y)
# Peak level
peak_db = 20 * np.log10(np.max(np.abs(y_mono)) + 1e-10)
# RMS level
rms = np.sqrt(np.mean(y_mono**2))
rms_db = 20 * np.log10(rms + 1e-10)
# Dynamic range (simplified)
frame_length = int(sr * 0.1) # 100ms frames
hop_length = frame_length // 2
frames_rms = []
for i in range(0, len(y_mono) - frame_length, hop_length):
frame = y_mono[i:i+frame_length]
frame_rms = np.sqrt(np.mean(frame**2))
if frame_rms > 0:
frames_rms.append(20 * np.log10(frame_rms + 1e-10))
if frames_rms:
dynamic_range = np.percentile(frames_rms, 95) - np.percentile(frames_rms, 5)
else:
dynamic_range = 0
# LUFS (integrated loudness)
lufs = -14 # Default
if HAS_PYLOUDNORM:
try:
meter = pyln.Meter(sr)
lufs = meter.integrated_loudness(y_mono)
except:
pass
# Spectral centroid (brightness)
spectral_centroid = np.mean(librosa.feature.spectral_centroid(y=y_mono, sr=sr))
return {
'peak_db': float(peak_db),
'rms_db': float(rms_db),
'lufs': float(lufs) if not np.isinf(lufs) else -24,
'dynamic_range': float(dynamic_range),
'spectral_centroid': float(spectral_centroid),
'duration': float(len(y_mono) / sr),
'sample_rate': sr,
}
except Exception as e:
return {'error': str(e)}
def analyze_reference(reference_path, target_path):
"""Analyze reference track and compute matching parameters"""
ref_analysis = analyze_audio(reference_path)
target_analysis = analyze_audio(target_path)
if not ref_analysis or not target_analysis:
return PRESETS['Balanced']
if 'error' in ref_analysis or 'error' in target_analysis:
return PRESETS['Balanced']
# Compute EQ adjustments based on spectral difference
centroid_diff = ref_analysis['spectral_centroid'] - target_analysis['spectral_centroid']
# Brightness adjustment
if centroid_diff > 500:
eq_high = 2
elif centroid_diff < -500:
eq_high = -2
else:
eq_high = 0
# Target LUFS from reference
target_lufs = ref_analysis['lufs']
if target_lufs < -20 or target_lufs > -6:
target_lufs = -14
return {
'eq_low': 0,
'eq_mid': 0,
'eq_high': eq_high,
'compression_ratio': 3,
'compression_threshold': -18,
'target_lufs': target_lufs,
'reference_lufs': ref_analysis['lufs'],
'reference_peak': ref_analysis['peak_db'],
}
# ============================================================================
# PROCESSING
# ============================================================================
def apply_eq(y, sr, low_db=0, mid_db=0, high_db=0):
"""Apply 3-band EQ"""
if not HAS_LIBROSA:
return y
# Define frequency bands
low_freq = 200
high_freq = 4000
# Get STFT
D = librosa.stft(y)
freqs = librosa.fft_frequencies(sr=sr)
# Create gain masks
low_mask = freqs < low_freq
mid_mask = (freqs >= low_freq) & (freqs < high_freq)
high_mask = freqs >= high_freq
# Apply gains
gains = np.ones(len(freqs))
gains[low_mask] *= 10 ** (low_db / 20)
gains[mid_mask] *= 10 ** (mid_db / 20)
gains[high_mask] *= 10 ** (high_db / 20)
# Apply to STFT
D_eq = D * gains[:, np.newaxis]
# Inverse STFT
y_eq = librosa.istft(D_eq, length=len(y))
return y_eq
def apply_compression(y, sr, ratio=3, threshold_db=-18, attack_ms=10, release_ms=100):
"""Apply dynamic range compression"""
if ratio <= 1:
return y
# Convert to linear
threshold = 10 ** (threshold_db / 20)
# Envelope follower
attack_samples = int(sr * attack_ms / 1000)
release_samples = int(sr * release_ms / 1000)
envelope = np.abs(y)
# Smooth envelope
envelope = uniform_filter1d(envelope, size=attack_samples)
# Compute gain reduction
gain = np.ones_like(envelope)
above_thresh = envelope > threshold
if np.any(above_thresh):
# Gain reduction for samples above threshold
gain[above_thresh] = (threshold / envelope[above_thresh]) ** (1 - 1/ratio)
# Apply gain
y_compressed = y * gain
# Makeup gain
makeup = 1 / np.mean(gain[gain < 1]) if np.any(gain < 1) else 1
y_compressed *= min(makeup, 2) # Limit makeup gain
return y_compressed
def apply_limiter(y, ceiling_db=-0.3):
"""Apply brick-wall limiter"""
ceiling = 10 ** (ceiling_db / 20)
# Soft clipping
y_limited = np.tanh(y / ceiling) * ceiling
return y_limited
def normalize_loudness(y, sr, target_lufs=-14):
"""Normalize to target LUFS"""
if not HAS_PYLOUDNORM:
# Fallback: simple peak normalization
peak = np.max(np.abs(y))
if peak > 0:
target_peak = 10 ** (-1 / 20) # -1 dB
y = y * (target_peak / peak)
return y
try:
meter = pyln.Meter(sr)
current_lufs = meter.integrated_loudness(y)
if np.isinf(current_lufs) or np.isnan(current_lufs):
return y
# Calculate gain needed
gain_db = target_lufs - current_lufs
gain = 10 ** (gain_db / 20)
# Apply gain with limiter
y_normalized = y * gain
y_normalized = apply_limiter(y_normalized)
return y_normalized
except:
return y
# ============================================================================
# MAIN MASTERING FUNCTION
# ============================================================================
def master_audio(input_path, output_path=None, preset='Balanced',
reference_path=None, target_lufs=None,
eq_low=None, eq_mid=None, eq_high=None):
"""
Master audio file
Args:
input_path: Path to input audio
output_path: Path for output (optional, creates temp file if None)
preset: Preset name or 'Reference Match'
reference_path: Path to reference track (for Reference Match)
target_lufs: Override target LUFS
eq_low/mid/high: Override EQ settings
Returns:
(output_path, analysis_dict)
"""
if not HAS_LIBROSA:
return None, {'error': 'librosa not installed'}
try:
# Load audio
y, sr = librosa.load(input_path, sr=44100, mono=True)
# Get preset settings
if preset == 'Reference Match' and reference_path:
settings = analyze_reference(reference_path, input_path)
else:
settings = PRESETS.get(preset, PRESETS['Balanced']).copy()
# Override with manual settings
if eq_low is not None:
settings['eq_low'] = eq_low
if eq_mid is not None:
settings['eq_mid'] = eq_mid
if eq_high is not None:
settings['eq_high'] = eq_high
if target_lufs is not None:
settings['target_lufs'] = target_lufs
# Analyze input
input_analysis = analyze_audio(input_path)
# Apply processing chain
y_processed = y.copy()
# 1. EQ
y_processed = apply_eq(
y_processed, sr,
low_db=settings['eq_low'],
mid_db=settings['eq_mid'],
high_db=settings['eq_high']
)
# 2. Compression
y_processed = apply_compression(
y_processed, sr,
ratio=settings['compression_ratio'],
threshold_db=settings['compression_threshold']
)
# 3. Loudness normalization
y_processed = normalize_loudness(
y_processed, sr,
target_lufs=settings['target_lufs']
)
# 4. Final limiter
y_processed = apply_limiter(y_processed, ceiling_db=-0.3)
# Create output path if needed
if output_path is None:
temp_dir = tempfile.mkdtemp()
output_path = Path(temp_dir) / f"{Path(input_path).stem}_mastered.wav"
# Save
sf.write(str(output_path), y_processed, sr)
# Analyze output
output_analysis = analyze_audio(str(output_path))
# Build result
result = {
'input': input_analysis,
'output': output_analysis,
'settings': settings,
'preset': preset,
}
return str(output_path), result
except Exception as e:
return None, {'error': str(e)}
def format_analysis(analysis):
"""Format analysis dict for display"""
if not analysis:
return "Analysis unavailable"
if 'error' in analysis:
return f"Error: {analysis['error']}"
lines = []
if 'input' in analysis:
inp = analysis['input']
lines.append("INPUT:")
lines.append(f" LUFS: {inp.get('lufs', 'N/A'):.1f}")
lines.append(f" Peak: {inp.get('peak_db', 'N/A'):.1f} dB")
lines.append(f" Dynamic Range: {inp.get('dynamic_range', 'N/A'):.1f} dB")
if 'output' in analysis:
out = analysis['output']
lines.append("\nOUTPUT:")
lines.append(f" LUFS: {out.get('lufs', 'N/A'):.1f}")
lines.append(f" Peak: {out.get('peak_db', 'N/A'):.1f} dB")
lines.append(f" Dynamic Range: {out.get('dynamic_range', 'N/A'):.1f} dB")
if 'settings' in analysis:
settings = analysis['settings']
lines.append("\nSETTINGS:")
lines.append(f" Target LUFS: {settings.get('target_lufs', -14)}")
lines.append(f" EQ: Low {settings.get('eq_low', 0):+.0f} / Mid {settings.get('eq_mid', 0):+.0f} / High {settings.get('eq_high', 0):+.0f}")
lines.append(f" Compression: {settings.get('compression_ratio', 3)}:1 @ {settings.get('compression_threshold', -18)} dB")
return "\n".join(lines)
# ============================================================================
# CLI
# ============================================================================
if __name__ == "__main__":
import sys
if len(sys.argv) < 2:
print("Usage: python mastering.py <input.wav> [output.wav] [preset]")
print("Presets: Balanced, Warm, Bright, Punchy, Reference Match")
sys.exit(1)
input_path = sys.argv[1]
output_path = sys.argv[2] if len(sys.argv) > 2 else None
preset = sys.argv[3] if len(sys.argv) > 3 else 'Balanced'
print(f"Mastering: {input_path}")
print(f"Preset: {preset}")
out_path, analysis = master_audio(input_path, output_path, preset)
if out_path:
print(f"\nOutput: {out_path}")
print(format_analysis(analysis))
else:
print(f"Error: {analysis.get('error', 'Unknown error')}")
|