Spaces:
Running
Running
File size: 14,974 Bytes
6bbce1b fe2ddc5 |
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 |
"""
Implementation of the 'audio effects chain normalization'
"""
import numpy as np
import scipy
import soundfile as sf
import pyloudnorm
from glob import glob
import os
import sys
currentdir = os.path.dirname(os.path.realpath(__file__))
sys.path.append(currentdir)
from utils_data_normalization import *
from normalization_imager import *
'''
Audio Effects Chain Normalization
process: normalizes input stems according to given precomputed features
'''
class Audio_Effects_Normalizer:
def __init__(self, precomputed_feature_path=None, \
STEMS=['drums', 'bass', 'other', 'vocals'], \
EFFECTS=['eq', 'compression', 'imager', 'loudness'], \
audio_extension='wav'):
self.STEMS = STEMS # Stems to be normalized
self.EFFECTS = EFFECTS # Effects to be normalized, order matters
self.audio_extension = audio_extension
self.precomputed_feature_path = precomputed_feature_path
# Audio settings
self.SR = 44100
self.SUBTYPE = 'PCM_16'
# General Settings
self.FFT_SIZE = 2**16
self.HOP_LENGTH = self.FFT_SIZE//4
# Loudness
self.NTAPS = 1001
self.LUFS = -30
self.MIN_DB = -40 # Min amplitude to apply EQ matching
# Compressor
self.COMP_USE_EXPANDER = False
self.COMP_PEAK_NORM = -10.0
self.COMP_TRUE_PEAK = False
self.COMP_PERCENTILE = 75 # features_mean (v1) was done with 25
self.COMP_MIN_TH = -40
self.COMP_MAX_RATIO = 20
comp_settings = {key:{} for key in self.STEMS}
for key in comp_settings:
if key=='vocals':
comp_settings[key]['attack'] = 7.5
comp_settings[key]['release'] = 400.0
comp_settings[key]['ratio'] = 4
comp_settings[key]['n_mels'] = 128
elif key=='drums':
comp_settings[key]['attack'] = 10.0
comp_settings[key]['release'] = 180.0
comp_settings[key]['ratio'] = 6
comp_settings[key]['n_mels'] = 128
elif key=='bass':
comp_settings[key]['attack'] = 10.0
comp_settings[key]['release'] = 500.0
comp_settings[key]['ratio'] = 5
comp_settings[key]['n_mels'] = 16
elif key=='other' or key=='mixture':
comp_settings[key]['attack'] = 15.0
comp_settings[key]['release'] = 666.0
comp_settings[key]['ratio'] = 4
comp_settings[key]['n_mels'] = 128
self.comp_settings = comp_settings
if precomputed_feature_path!=None and os.path.isfile(precomputed_feature_path):
# Load Pre-computed Audio Effects Features
features_mean = np.load(precomputed_feature_path, allow_pickle='TRUE')[()]
self.features_mean = self.smooth_feature(features_mean)
# compute audio effects' mean feature values
def compute_mean(self, base_dir_path, save_feat=True, single_file=False):
audio_path_dict = {}
for cur_stem in self.STEMS:
# if single_file=True, base_dir_path = the target file path
audio_path_dict[cur_stem] = [base_dir_path] if single_file else glob(os.path.join(base_dir_path, "**", f"{cur_stem}.{self.audio_extension}"), recursive=True)
features_dict = {}
features_mean = {}
for effect in self.EFFECTS:
features_dict[effect] = {key:[] for key in self.STEMS}
features_mean[effect] = {key:[] for key in self.STEMS}
stems_names = self.STEMS.copy()
for effect in self.EFFECTS:
print(f'{effect} ...')
j=0
for key in self.STEMS:
print(f'{key} ...')
i = []
for i_, p_ in enumerate(audio_path_dict[key]):
i.append(i_)
i = np.asarray(i) + j
j += len(i)
features_ = []
for cur_i, cur_audio_path in enumerate(audio_path_dict[key]):
print(f'getting {effect} features for {key}- stem {cur_i} of {len(audio_path_dict[key])-1} {cur_audio_path}')
features_.append(self.get_norm_feature(cur_audio_path, cur_i, effect, key))
features_dict[effect][key] = features_
print(effect, key, len(features_dict[effect][key]))
s = np.asarray(features_dict[effect][key])
s = np.mean(s, axis=0)
features_mean[effect][key] = s
if effect == 'eq':
assert len(s)==1+self.FFT_SIZE//2, len(s)
elif effect == 'compression':
assert len(s)==2, len(s)
elif effect == 'panning':
assert len(s)==1+self.FFT_SIZE//2, len(s)
elif effect == 'loudness':
assert len(s)==1, len(s)
if effect == 'eq':
if key in ['other', 'vocals', 'mixture']:
f = 401
else:
f = 151
features_mean[effect][key] = scipy.signal.savgol_filter(features_mean[effect][key],
f, 1, mode='mirror')
elif effect == 'panning':
features_mean[effect][key] = scipy.signal.savgol_filter(features_mean[effect][key],
501, 1, mode='mirror')
if save_feat:
np.save(self.precomputed_feature_path, features_mean)
self.features_mean = self.smooth_feature(features_mean)
print('---feature mean computation completed---')
return self.features_mean
def get_norm_feature(self, path, i, effect, stem):
if isinstance(path, str):
audio, fs = sf.read(path)
assert(fs == self.SR)
else:
audio = path
fs = self.SR
all_zeros = not np.any(audio)
if all_zeros == False:
audio = np.pad(audio, ((self.FFT_SIZE, self.FFT_SIZE), (0, 0)), mode='constant')
max_db = amp_to_db(np.max(np.abs(audio)))
if max_db > self.MIN_DB:
if effect == 'loudness':
meter = pyln.Meter(self.SR)
loudness = meter.integrated_loudness(audio)
return [loudness]
elif effect == 'eq':
audio = lufs_normalize(audio, self.SR, self.LUFS, log=False)
audio_spec = compute_stft(audio,
self.HOP_LENGTH,
self.FFT_SIZE,
np.sqrt(np.hanning(self.FFT_SIZE+1)[:-1]))
audio_spec = np.abs(audio_spec)
audio_spec_avg = np.mean(audio_spec, axis=(0,1))
return audio_spec_avg
elif effect == 'panning':
phi = get_SPS(audio,
n_fft=self.FFT_SIZE,
hop_length=self.HOP_LENGTH,
smooth=False,
frames=False)
return(phi[1])
elif effect == 'compression':
x = pyln.normalize.peak(audio, self.COMP_PEAK_NORM)
peak_std = get_mean_peak(x,
sr=self.SR,
true_peak=self.COMP_TRUE_PEAK,
percentile=self.COMP_PERCENTILE,
n_mels=self.comp_settings[stem]['n_mels'])
if peak_std is not None:
return peak_std
else:
return None
elif effect == 'imager':
mid, side = lr_to_ms(audio[:,0], audio[:,1])
return print_balance(mid, side, verbose=False)
else:
print(f'{path} is silence...')
return None
else:
print(f'{path} is only zeros...')
return None
# normalize current audio input with the order of designed audio FX
def normalize_audio(self, audio, src):
assert src in self.STEMS
normalized_audio = audio
for cur_effect in self.EFFECTS:
normalized_audio = self.normalize_audio_per_effect(normalized_audio, src=src, effect=cur_effect)
return normalized_audio
# normalize current audio input with current targeted audio FX
def normalize_audio_per_effect(self, audio, src, effect):
audio = audio.astype(dtype=np.float32)
audio_track = np.pad(audio, ((self.FFT_SIZE, self.FFT_SIZE), (0, 0)), mode='constant')
assert len(audio_track.shape) == 2 # Always expects two dimensions
if audio_track.shape[1] == 1: # Converts mono to stereo with repeated channels
audio_track = np.repeat(audio_track, 2, axis=-1)
output_audio = audio_track.copy()
max_db = amp_to_db(np.max(np.abs(output_audio)))
if max_db > self.MIN_DB:
if effect == 'eq':
# normalize each channel
for ch in range(audio_track.shape[1]):
audio_eq_matched = get_eq_matching(output_audio[:, ch],
self.features_mean[effect][src],
sr=self.SR,
n_fft=self.FFT_SIZE,
hop_length=self.HOP_LENGTH,
min_db=self.MIN_DB,
ntaps=self.NTAPS,
lufs=self.LUFS)
np.copyto(output_audio[:,ch], audio_eq_matched)
elif effect == 'compression':
assert(len(self.features_mean[effect][src])==2)
# normalize each channel
for ch in range(audio_track.shape[1]):
try:
audio_comp_matched = get_comp_matching(output_audio[:, ch],
self.features_mean[effect][src][0],
self.features_mean[effect][src][1],
self.comp_settings[src]['ratio'],
self.comp_settings[src]['attack'],
self.comp_settings[src]['release'],
sr=self.SR,
min_db=self.MIN_DB,
min_th=self.COMP_MIN_TH,
comp_peak_norm=self.COMP_PEAK_NORM,
max_ratio=self.COMP_MAX_RATIO,
n_mels=self.comp_settings[src]['n_mels'],
true_peak=self.COMP_TRUE_PEAK,
percentile=self.COMP_PERCENTILE,
expander=self.COMP_USE_EXPANDER)
np.copyto(output_audio[:,ch], audio_comp_matched[:, 0])
except:
break
elif effect == 'loudness':
output_audio = lufs_normalize(output_audio, self.SR, self.features_mean[effect][src], log=False)
elif effect == 'imager':
# threshold of applying Haas effects
mono_threshold = 0.99 if src=='bass' else 0.975
audio_imager_matched = normalize_imager(output_audio, \
target_side_mid_bal=self.features_mean[effect][src][0], \
mono_threshold=mono_threshold, \
sr=self.SR)
np.copyto(output_audio, audio_imager_matched)
output_audio = output_audio[self.FFT_SIZE:self.FFT_SIZE+audio.shape[0]]
return output_audio
def smooth_feature(self, feature_dict_):
for effect in self.EFFECTS:
for key in self.STEMS:
if effect == 'eq':
if key in ['other', 'vocals', 'mixture']:
f = 401
else:
f = 151
feature_dict_[effect][key] = scipy.signal.savgol_filter(feature_dict_[effect][key],
f, 1, mode='mirror')
elif effect == 'panning':
feature_dict_[effect][key] = scipy.signal.savgol_filter(feature_dict_[effect][key],
501, 1, mode='mirror')
return feature_dict_
# compute "normalization" based on a single sample
def feature_matching(self, src_aud_path, ref_aud_path):
# compute mean features from reference audio
mean_feature = self.compute_mean(ref_aud_path, save_feat=False, single_file=True)
print(mean_feature)
src_aud, sr = sf.read(src_aud_path)
normalized_audio = self.normalize_audio(src_aud, 'mixture')
return normalized_audio
def lufs_normalize(x, sr, lufs, log=True):
# measure the loudness first
meter = pyloudnorm.Meter(sr) # create BS.1770 meter
loudness = meter.integrated_loudness(x+1e-10)
if log:
print("original loudness: ", loudness," max value: ", np.max(np.abs(x)))
loudness_normalized_audio = pyloudnorm.normalize.loudness(x, loudness, lufs)
maxabs_amp = np.maximum(1.0, 1e-6 + np.max(np.abs(loudness_normalized_audio)))
loudness_normalized_audio /= maxabs_amp
loudness = meter.integrated_loudness(loudness_normalized_audio)
if log:
print("new loudness: ", loudness," max value: ", np.max(np.abs(loudness_normalized_audio)))
return loudness_normalized_audio
|