Spaces:
Running
Running
File size: 23,915 Bytes
5565d9c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 |
import librosa
import numpy as np
import soundfile as sf
import math
import random
import math
import platform
import traceback
from . import pyrb
#cur
OPERATING_SYSTEM = platform.system()
SYSTEM_ARCH = platform.platform()
SYSTEM_PROC = platform.processor()
ARM = 'arm'
if OPERATING_SYSTEM == 'Windows':
from pyrubberband import pyrb
else:
from . import pyrb
if OPERATING_SYSTEM == 'Darwin':
wav_resolution = "polyphase" if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else "sinc_fastest"
else:
wav_resolution = "sinc_fastest"
MAX_SPEC = 'Max Spec'
MIN_SPEC = 'Min Spec'
AVERAGE = 'Average'
def crop_center(h1, h2):
h1_shape = h1.size()
h2_shape = h2.size()
if h1_shape[3] == h2_shape[3]:
return h1
elif h1_shape[3] < h2_shape[3]:
raise ValueError('h1_shape[3] must be greater than h2_shape[3]')
s_time = (h1_shape[3] - h2_shape[3]) // 2
e_time = s_time + h2_shape[3]
h1 = h1[:, :, :, s_time:e_time]
return h1
def preprocess(X_spec):
X_mag = np.abs(X_spec)
X_phase = np.angle(X_spec)
return X_mag, X_phase
def make_padding(width, cropsize, offset):
left = offset
roi_size = cropsize - offset * 2
if roi_size == 0:
roi_size = cropsize
right = roi_size - (width % roi_size) + left
return left, right, roi_size
def wave_to_spectrogram(wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False):
if reverse:
wave_left = np.flip(np.asfortranarray(wave[0]))
wave_right = np.flip(np.asfortranarray(wave[1]))
elif mid_side:
wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
elif mid_side_b2:
wave_left = np.asfortranarray(np.add(wave[1], wave[0] * .5))
wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .5))
else:
wave_left = np.asfortranarray(wave[0])
wave_right = np.asfortranarray(wave[1])
spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
spec = np.asfortranarray([spec_left, spec_right])
return spec
def wave_to_spectrogram_mt(wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False):
import threading
if reverse:
wave_left = np.flip(np.asfortranarray(wave[0]))
wave_right = np.flip(np.asfortranarray(wave[1]))
elif mid_side:
wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
elif mid_side_b2:
wave_left = np.asfortranarray(np.add(wave[1], wave[0] * .5))
wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .5))
else:
wave_left = np.asfortranarray(wave[0])
wave_right = np.asfortranarray(wave[1])
def run_thread(**kwargs):
global spec_left
spec_left = librosa.stft(**kwargs)
thread = threading.Thread(target=run_thread, kwargs={'y': wave_left, 'n_fft': n_fft, 'hop_length': hop_length})
thread.start()
spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
thread.join()
spec = np.asfortranarray([spec_left, spec_right])
return spec
def normalize(wave, is_normalize=False):
"""Save output music files"""
maxv = np.abs(wave).max()
if maxv > 1.0:
print(f"\nNormalization Set {is_normalize}: Input above threshold for clipping. Max:{maxv}")
if is_normalize:
print(f"The result was normalized.")
wave /= maxv
else:
print(f"The result was not normalized.")
else:
print(f"\nNormalization Set {is_normalize}: Input not above threshold for clipping. Max:{maxv}")
return wave
def normalize_two_stem(wave, mix, is_normalize=False):
"""Save output music files"""
maxv = np.abs(wave).max()
max_mix = np.abs(mix).max()
if maxv > 1.0:
print(f"\nNormalization Set {is_normalize}: Primary source above threshold for clipping. Max:{maxv}")
print(f"\nNormalization Set {is_normalize}: Mixture above threshold for clipping. Max:{max_mix}")
if is_normalize:
print(f"The result was normalized.")
wave /= maxv
mix /= maxv
else:
print(f"The result was not normalized.")
else:
print(f"\nNormalization Set {is_normalize}: Input not above threshold for clipping. Max:{maxv}")
print(f"\nNormalization Set {is_normalize}: Primary source - Max:{np.abs(wave).max()}")
print(f"\nNormalization Set {is_normalize}: Mixture - Max:{np.abs(mix).max()}")
return wave, mix
def combine_spectrograms(specs, mp):
l = min([specs[i].shape[2] for i in specs])
spec_c = np.zeros(shape=(2, mp.param['bins'] + 1, l), dtype=np.complex64)
offset = 0
bands_n = len(mp.param['band'])
for d in range(1, bands_n + 1):
h = mp.param['band'][d]['crop_stop'] - mp.param['band'][d]['crop_start']
spec_c[:, offset:offset+h, :l] = specs[d][:, mp.param['band'][d]['crop_start']:mp.param['band'][d]['crop_stop'], :l]
offset += h
if offset > mp.param['bins']:
raise ValueError('Too much bins')
# lowpass fiter
if mp.param['pre_filter_start'] > 0: # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']:
if bands_n == 1:
spec_c = fft_lp_filter(spec_c, mp.param['pre_filter_start'], mp.param['pre_filter_stop'])
else:
gp = 1
for b in range(mp.param['pre_filter_start'] + 1, mp.param['pre_filter_stop']):
g = math.pow(10, -(b - mp.param['pre_filter_start']) * (3.5 - gp) / 20.0)
gp = g
spec_c[:, b, :] *= g
return np.asfortranarray(spec_c)
def spectrogram_to_image(spec, mode='magnitude'):
if mode == 'magnitude':
if np.iscomplexobj(spec):
y = np.abs(spec)
else:
y = spec
y = np.log10(y ** 2 + 1e-8)
elif mode == 'phase':
if np.iscomplexobj(spec):
y = np.angle(spec)
else:
y = spec
y -= y.min()
y *= 255 / y.max()
img = np.uint8(y)
if y.ndim == 3:
img = img.transpose(1, 2, 0)
img = np.concatenate([
np.max(img, axis=2, keepdims=True), img
], axis=2)
return img
def reduce_vocal_aggressively(X, y, softmask):
v = X - y
y_mag_tmp = np.abs(y)
v_mag_tmp = np.abs(v)
v_mask = v_mag_tmp > y_mag_tmp
y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf)
return y_mag * np.exp(1.j * np.angle(y))
def merge_artifacts(y_mask, thres=0.01, min_range=64, fade_size=32):
mask = y_mask
try:
if min_range < fade_size * 2:
raise ValueError('min_range must be >= fade_size * 2')
idx = np.where(y_mask.min(axis=(0, 1)) > thres)[0]
start_idx = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
end_idx = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
artifact_idx = np.where(end_idx - start_idx > min_range)[0]
weight = np.zeros_like(y_mask)
if len(artifact_idx) > 0:
start_idx = start_idx[artifact_idx]
end_idx = end_idx[artifact_idx]
old_e = None
for s, e in zip(start_idx, end_idx):
if old_e is not None and s - old_e < fade_size:
s = old_e - fade_size * 2
if s != 0:
weight[:, :, s:s + fade_size] = np.linspace(0, 1, fade_size)
else:
s -= fade_size
if e != y_mask.shape[2]:
weight[:, :, e - fade_size:e] = np.linspace(1, 0, fade_size)
else:
e += fade_size
weight[:, :, s + fade_size:e - fade_size] = 1
old_e = e
v_mask = 1 - y_mask
y_mask += weight * v_mask
mask = y_mask
except Exception as e:
error_name = f'{type(e).__name__}'
traceback_text = ''.join(traceback.format_tb(e.__traceback__))
message = f'{error_name}: "{e}"\n{traceback_text}"'
print('Post Process Failed: ', message)
return mask
def align_wave_head_and_tail(a, b):
l = min([a[0].size, b[0].size])
return a[:l,:l], b[:l,:l]
def spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, reverse, clamp=False):
spec_left = np.asfortranarray(spec[0])
spec_right = np.asfortranarray(spec[1])
wave_left = librosa.istft(spec_left, hop_length=hop_length)
wave_right = librosa.istft(spec_right, hop_length=hop_length)
if reverse:
return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
elif mid_side:
return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
elif mid_side_b2:
return np.asfortranarray([np.add(wave_right / 1.25, .4 * wave_left), np.subtract(wave_left / 1.25, .4 * wave_right)])
else:
return np.asfortranarray([wave_left, wave_right])
def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse, mid_side_b2):
import threading
spec_left = np.asfortranarray(spec[0])
spec_right = np.asfortranarray(spec[1])
def run_thread(**kwargs):
global wave_left
wave_left = librosa.istft(**kwargs)
thread = threading.Thread(target=run_thread, kwargs={'stft_matrix': spec_left, 'hop_length': hop_length})
thread.start()
wave_right = librosa.istft(spec_right, hop_length=hop_length)
thread.join()
if reverse:
return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
elif mid_side:
return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
elif mid_side_b2:
return np.asfortranarray([np.add(wave_right / 1.25, .4 * wave_left), np.subtract(wave_left / 1.25, .4 * wave_right)])
else:
return np.asfortranarray([wave_left, wave_right])
def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
bands_n = len(mp.param['band'])
offset = 0
for d in range(1, bands_n + 1):
bp = mp.param['band'][d]
spec_s = np.ndarray(shape=(2, bp['n_fft'] // 2 + 1, spec_m.shape[2]), dtype=complex)
h = bp['crop_stop'] - bp['crop_start']
spec_s[:, bp['crop_start']:bp['crop_stop'], :] = spec_m[:, offset:offset+h, :]
offset += h
if d == bands_n: # higher
if extra_bins_h: # if --high_end_process bypass
max_bin = bp['n_fft'] // 2
spec_s[:, max_bin-extra_bins_h:max_bin, :] = extra_bins[:, :extra_bins_h, :]
if bp['hpf_start'] > 0:
spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
if bands_n == 1:
wave = spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse'])
else:
wave = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']))
else:
sr = mp.param['band'][d+1]['sr']
if d == 1: # lower
spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
wave = librosa.resample(spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']), bp['sr'], sr, res_type=wav_resolution)
else: # mid
spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
wave2 = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']))
wave = librosa.resample(wave2, bp['sr'], sr, res_type=wav_resolution)
return wave
def fft_lp_filter(spec, bin_start, bin_stop):
g = 1.0
for b in range(bin_start, bin_stop):
g -= 1 / (bin_stop - bin_start)
spec[:, b, :] = g * spec[:, b, :]
spec[:, bin_stop:, :] *= 0
return spec
def fft_hp_filter(spec, bin_start, bin_stop):
g = 1.0
for b in range(bin_start, bin_stop, -1):
g -= 1 / (bin_start - bin_stop)
spec[:, b, :] = g * spec[:, b, :]
spec[:, 0:bin_stop+1, :] *= 0
return spec
def mirroring(a, spec_m, input_high_end, mp):
if 'mirroring' == a:
mirror = np.flip(np.abs(spec_m[:, mp.param['pre_filter_start']-10-input_high_end.shape[1]:mp.param['pre_filter_start']-10, :]), 1)
mirror = mirror * np.exp(1.j * np.angle(input_high_end))
return np.where(np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror)
if 'mirroring2' == a:
mirror = np.flip(np.abs(spec_m[:, mp.param['pre_filter_start']-10-input_high_end.shape[1]:mp.param['pre_filter_start']-10, :]), 1)
mi = np.multiply(mirror, input_high_end * 1.7)
return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)
def adjust_aggr(mask, is_non_accom_stem, aggressiveness):
aggr = aggressiveness['value']
if aggr != 0:
if is_non_accom_stem:
aggr = 1 - aggr
aggr = [aggr, aggr]
if aggressiveness['aggr_correction'] is not None:
aggr[0] += aggressiveness['aggr_correction']['left']
aggr[1] += aggressiveness['aggr_correction']['right']
for ch in range(2):
mask[ch, :aggressiveness['split_bin']] = np.power(mask[ch, :aggressiveness['split_bin']], 1 + aggr[ch] / 3)
mask[ch, aggressiveness['split_bin']:] = np.power(mask[ch, aggressiveness['split_bin']:], 1 + aggr[ch])
# if is_non_accom_stem:
# mask = (1.0 - mask)
return mask
def stft(wave, nfft, hl):
wave_left = np.asfortranarray(wave[0])
wave_right = np.asfortranarray(wave[1])
spec_left = librosa.stft(wave_left, nfft, hop_length=hl)
spec_right = librosa.stft(wave_right, nfft, hop_length=hl)
spec = np.asfortranarray([spec_left, spec_right])
return spec
def istft(spec, hl):
spec_left = np.asfortranarray(spec[0])
spec_right = np.asfortranarray(spec[1])
wave_left = librosa.istft(spec_left, hop_length=hl)
wave_right = librosa.istft(spec_right, hop_length=hl)
wave = np.asfortranarray([wave_left, wave_right])
return wave
def spec_effects(wave, algorithm='Default', value=None):
spec = [stft(wave[0],2048,1024), stft(wave[1],2048,1024)]
if algorithm == 'Min_Mag':
v_spec_m = np.where(np.abs(spec[1]) <= np.abs(spec[0]), spec[1], spec[0])
wave = istft(v_spec_m,1024)
elif algorithm == 'Max_Mag':
v_spec_m = np.where(np.abs(spec[1]) >= np.abs(spec[0]), spec[1], spec[0])
wave = istft(v_spec_m,1024)
elif algorithm == 'Default':
wave = (wave[1] * value) + (wave[0] * (1-value))
elif algorithm == 'Invert_p':
X_mag = np.abs(spec[0])
y_mag = np.abs(spec[1])
max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
v_spec = spec[1] - max_mag * np.exp(1.j * np.angle(spec[0]))
wave = istft(v_spec,1024)
return wave
def spectrogram_to_wave_no_mp(spec, n_fft=2048, hop_length=1024):
wave = librosa.istft(spec, n_fft=n_fft, hop_length=hop_length)
if wave.ndim == 1:
wave = np.asfortranarray([wave,wave])
return wave
def wave_to_spectrogram_no_mp(wave):
spec = librosa.stft(wave, n_fft=2048, hop_length=1024)
if spec.ndim == 1:
spec = np.asfortranarray([spec,spec])
return spec
def invert_audio(specs, invert_p=True):
ln = min([specs[0].shape[2], specs[1].shape[2]])
specs[0] = specs[0][:,:,:ln]
specs[1] = specs[1][:,:,:ln]
if invert_p:
X_mag = np.abs(specs[0])
y_mag = np.abs(specs[1])
max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
v_spec = specs[1] - max_mag * np.exp(1.j * np.angle(specs[0]))
else:
specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2)
v_spec = specs[0] - specs[1]
return v_spec
def invert_stem(mixture, stem):
mixture = wave_to_spectrogram_no_mp(mixture)
stem = wave_to_spectrogram_no_mp(stem)
output = spectrogram_to_wave_no_mp(invert_audio([mixture, stem]))
return -output.T
def ensembling(a, specs):
for i in range(1, len(specs)):
if i == 1:
spec = specs[0]
ln = min([spec.shape[2], specs[i].shape[2]])
spec = spec[:,:,:ln]
specs[i] = specs[i][:,:,:ln]
if MIN_SPEC == a:
spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec)
if MAX_SPEC == a:
spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec)
if AVERAGE == a:
spec = np.where(np.abs(specs[i]) == np.abs(spec), specs[i], spec)
return spec
def ensemble_inputs(audio_input, algorithm, is_normalization, wav_type_set, save_path):
wavs_ = []
if algorithm == AVERAGE:
output = average_audio(audio_input)
samplerate = 44100
else:
specs = []
for i in range(len(audio_input)):
wave, samplerate = librosa.load(audio_input[i], mono=False, sr=44100)
wavs_.append(wave)
spec = wave_to_spectrogram_no_mp(wave)
specs.append(spec)
wave_shapes = [w.shape[1] for w in wavs_]
target_shape = wavs_[wave_shapes.index(max(wave_shapes))]
output = spectrogram_to_wave_no_mp(ensembling(algorithm, specs))
output = to_shape(output, target_shape.shape)
sf.write(save_path, normalize(output.T, is_normalization), samplerate, subtype=wav_type_set)
def to_shape(x, target_shape):
padding_list = []
for x_dim, target_dim in zip(x.shape, target_shape):
pad_value = (target_dim - x_dim)
pad_tuple = ((0, pad_value))
padding_list.append(pad_tuple)
return np.pad(x, tuple(padding_list), mode='constant')
def to_shape_minimize(x: np.ndarray, target_shape):
padding_list = []
for x_dim, target_dim in zip(x.shape, target_shape):
pad_value = (target_dim - x_dim)
pad_tuple = ((0, pad_value))
padding_list.append(pad_tuple)
return np.pad(x, tuple(padding_list), mode='constant')
def augment_audio(export_path, audio_file, rate, is_normalization, wav_type_set, save_format=None, is_pitch=False):
wav, sr = librosa.load(audio_file, sr=44100, mono=False)
if wav.ndim == 1:
wav = np.asfortranarray([wav,wav])
if is_pitch:
wav_1 = pyrb.pitch_shift(wav[0], sr, rate, rbargs=None)
wav_2 = pyrb.pitch_shift(wav[1], sr, rate, rbargs=None)
else:
wav_1 = pyrb.time_stretch(wav[0], sr, rate, rbargs=None)
wav_2 = pyrb.time_stretch(wav[1], sr, rate, rbargs=None)
if wav_1.shape > wav_2.shape:
wav_2 = to_shape(wav_2, wav_1.shape)
if wav_1.shape < wav_2.shape:
wav_1 = to_shape(wav_1, wav_2.shape)
wav_mix = np.asfortranarray([wav_1, wav_2])
sf.write(export_path, normalize(wav_mix.T, is_normalization), sr, subtype=wav_type_set)
save_format(export_path)
def average_audio(audio):
waves = []
wave_shapes = []
final_waves = []
for i in range(len(audio)):
wave = librosa.load(audio[i], sr=44100, mono=False)
waves.append(wave[0])
wave_shapes.append(wave[0].shape[1])
wave_shapes_index = wave_shapes.index(max(wave_shapes))
target_shape = waves[wave_shapes_index]
waves.pop(wave_shapes_index)
final_waves.append(target_shape)
for n_array in waves:
wav_target = to_shape(n_array, target_shape.shape)
final_waves.append(wav_target)
waves = sum(final_waves)
waves = waves/len(audio)
return waves
def average_dual_sources(wav_1, wav_2, value):
if wav_1.shape > wav_2.shape:
wav_2 = to_shape(wav_2, wav_1.shape)
if wav_1.shape < wav_2.shape:
wav_1 = to_shape(wav_1, wav_2.shape)
wave = (wav_1 * value) + (wav_2 * (1-value))
return wave
def reshape_sources(wav_1: np.ndarray, wav_2: np.ndarray):
if wav_1.shape > wav_2.shape:
wav_2 = to_shape(wav_2, wav_1.shape)
if wav_1.shape < wav_2.shape:
ln = min([wav_1.shape[1], wav_2.shape[1]])
wav_2 = wav_2[:,:ln]
ln = min([wav_1.shape[1], wav_2.shape[1]])
wav_1 = wav_1[:,:ln]
wav_2 = wav_2[:,:ln]
return wav_2
def align_audio(file1, file2, file2_aligned, file_subtracted, wav_type_set, is_normalization, command_Text, progress_bar_main_var, save_format):
def get_diff(a, b):
corr = np.correlate(a, b, "full")
diff = corr.argmax() - (b.shape[0] - 1)
return diff
progress_bar_main_var.set(10)
# read tracks
wav1, sr1 = librosa.load(file1, sr=44100, mono=False)
wav2, sr2 = librosa.load(file2, sr=44100, mono=False)
wav1 = wav1.transpose()
wav2 = wav2.transpose()
command_Text(f"Audio file shapes: {wav1.shape} / {wav2.shape}\n")
wav2_org = wav2.copy()
progress_bar_main_var.set(20)
command_Text("Processing files... \n")
# pick random position and get diff
counts = {} # counting up for each diff value
progress = 20
check_range = 64
base = (64 / check_range)
for i in range(check_range):
index = int(random.uniform(44100 * 2, min(wav1.shape[0], wav2.shape[0]) - 44100 * 2))
shift = int(random.uniform(-22050,+22050))
samp1 = wav1[index :index +44100, 0] # currently use left channel
samp2 = wav2[index+shift:index+shift+44100, 0]
progress += 1 * base
progress_bar_main_var.set(progress)
diff = get_diff(samp1, samp2)
diff -= shift
if abs(diff) < 22050:
if not diff in counts:
counts[diff] = 0
counts[diff] += 1
# use max counted diff value
max_count = 0
est_diff = 0
for diff in counts.keys():
if counts[diff] > max_count:
max_count = counts[diff]
est_diff = diff
command_Text(f"Estimated difference is {est_diff} (count: {max_count})\n")
progress_bar_main_var.set(90)
audio_files = []
def save_aligned_audio(wav2_aligned):
command_Text(f"Aligned File 2 with File 1.\n")
command_Text(f"Saving files... ")
sf.write(file2_aligned, normalize(wav2_aligned, is_normalization), sr2, subtype=wav_type_set)
save_format(file2_aligned)
min_len = min(wav1.shape[0], wav2_aligned.shape[0])
wav_sub = wav1[:min_len] - wav2_aligned[:min_len]
audio_files.append(file2_aligned)
return min_len, wav_sub
# make aligned track 2
if est_diff > 0:
wav2_aligned = np.append(np.zeros((est_diff, 2)), wav2_org, axis=0)
min_len, wav_sub = save_aligned_audio(wav2_aligned)
elif est_diff < 0:
wav2_aligned = wav2_org[-est_diff:]
min_len, wav_sub = save_aligned_audio(wav2_aligned)
else:
command_Text(f"Audio files already aligned.\n")
command_Text(f"Saving inverted track... ")
min_len = min(wav1.shape[0], wav2.shape[0])
wav_sub = wav1[:min_len] - wav2[:min_len]
wav_sub = np.clip(wav_sub, -1, +1)
sf.write(file_subtracted, normalize(wav_sub, is_normalization), sr1, subtype=wav_type_set)
save_format(file_subtracted)
progress_bar_main_var.set(95) |