File size: 37,342 Bytes
2777fde |
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 |
"""
Data Loaders for
1. contrastive learning of audio effects
2. music mixing style transfer
introduced in "Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects"
"""
import numpy as np
import wave
import soundfile as sf
import time
import random
from glob import glob
import torch
import torch.utils.data as data
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
import os
import sys
currentdir = os.path.dirname(os.path.realpath(__file__))
sys.path.append(currentdir)
sys.path.append(os.path.dirname(currentdir))
sys.path.append(os.path.dirname(os.path.dirname(currentdir)))
from loader_utils import *
from mixing_manipulator import *
'''
Collate Functions
'''
class Collate_Variable_Length_Segments:
def __init__(self, args):
self.segment_length = args.segment_length
self.random_length = args.reference_length
self.num_strong_negatives = args.num_strong_negatives
if 'musdb' in args.using_dataset.lower():
self.instruments = ["drums", "bass", "other", "vocals"]
else:
raise NotImplementedError
# collate function to trim segments A and B to random duration
# this function can handle different number of 'strong negative' inputs
def random_duration_segments_strong_negatives(self, batch):
num_inst = len(self.instruments)
# randomize current input length
max_length = batch[0][0].shape[-1]
min_length = max_length//2
input_length_a, input_length_b = torch.randint(low=min_length, high=max_length, size=(2,))
output_dict_A = {}
output_dict_B = {}
for cur_inst in self.instruments:
output_dict_A[cur_inst] = []
output_dict_B[cur_inst] = []
for cur_item in batch:
# set starting points
start_point_a = torch.randint(low=0, high=max_length-input_length_a, size=(1,))[0]
start_point_b = torch.randint(low=0, high=max_length-input_length_b, size=(1,))[0]
# append to output dictionary
for cur_i, cur_inst in enumerate(self.instruments):
# append A# and B# with its strong negative samples
for cur_neg_idx in range(self.num_strong_negatives+1):
output_dict_A[cur_inst].append(cur_item[cur_i*(self.num_strong_negatives+1)*2+2*cur_neg_idx][:, start_point_a : start_point_a+input_length_a])
output_dict_B[cur_inst].append(cur_item[cur_i*(self.num_strong_negatives+1)*2+1+2*cur_neg_idx][:, start_point_b : start_point_b+input_length_b])
'''
Output format :
[drums_A, bass_A, other_A, vocals_A],
[drums_B, bass_B, other_B, vocals_B]
'''
return [torch.stack(cur_segments, dim=0) for cur_inst, cur_segments in output_dict_A.items()], \
[torch.stack(cur_segments, dim=0) for cur_inst, cur_segments in output_dict_B.items()]
# collate function for training mixing style transfer
def style_transfer_collate(self, batch):
output_dict_A1 = {}
output_dict_A2 = {}
output_dict_B2 = {}
for cur_inst in self.instruments:
output_dict_A1[cur_inst] = []
output_dict_A2[cur_inst] = []
output_dict_B2[cur_inst] = []
for cur_item in batch:
# append to output dictionary
for cur_i, cur_inst in enumerate(self.instruments):
output_dict_A1[cur_inst].append(cur_item[cur_i*3])
output_dict_A2[cur_inst].append(cur_item[cur_i*3+1])
output_dict_B2[cur_inst].append(cur_item[cur_i*3+2])
'''
Output format :
[drums_A1, bass_A1, other_A1, vocals_A1],
[drums_A2, bass_A2, other_A2, vocals_A2],
[drums_B2, bass_B2, other_B2, vocals_B2]
'''
return [torch.stack(cur_segments, dim=0) for cur_inst, cur_segments in output_dict_A1.items()], \
[torch.stack(cur_segments, dim=0) for cur_inst, cur_segments in output_dict_A2.items()], \
[torch.stack(cur_segments, dim=0) for cur_inst, cur_segments in output_dict_B2.items()]
'''
Data Loaders
'''
# Data loader for training the 'FXencoder'
# randomly loads two segments (A and B) from the dataset
# both segments are manipulated via FXmanipulator using (1+number of strong negative samples) sets of parameters (resulting A1, A2, ..., A#, and B1, B2, ..., B#) (# = number of strong negative samples)
# segments with the same effects applied (A1 and B1) are assigned as the positive pair during the training
# segments with the same content but with different effects applied (A2, A3, ..., A3 for A1) are also formed in a batch as 'strong negative' samples
# in the paper, we use strong negative samples = 1
class MUSDB_Dataset_Mixing_Manipulated_FXencoder(Dataset):
def __init__(self, args, \
mode, \
applying_effects='full', \
apply_prob_dict=None):
self.args = args
self.data_dir = args.data_dir + mode + "/"
self.mode = mode
self.applying_effects = applying_effects
self.normalization_order = args.normalization_order
self.fixed_random_seed = args.random_seed
self.pad_b4_manipulation = args.pad_b4_manipulation
self.pad_length = 2048
if 'musdb' in args.using_dataset.lower():
self.instruments = ["drums", "bass", "other", "vocals"]
else:
raise NotImplementedError
# path to contents
self.data_paths = {}
self.data_length_ratio_list = {}
# load data paths for each instrument
for cur_inst in self.instruments:
self.data_paths[cur_inst] = glob(f'{self.data_dir}{cur_inst}_normalized_{self.normalization_order}_silence_trimmed*.wav') \
if args.use_normalized else glob(f'{self.data_dir}{cur_inst}_silence_trimmed*.wav')
self.data_length_ratio_list[cur_inst] = []
# compute audio duration and its ratio
for cur_file_path in self.data_paths[cur_inst]:
cur_wav_length = load_wav_length(cur_file_path)
cur_inst_length_ratio = cur_wav_length / get_total_audio_length(self.data_paths[cur_inst])
self.data_length_ratio_list[cur_inst].append(cur_inst_length_ratio)
# load effects chain
if applying_effects=='full':
if apply_prob_dict==None:
# initial (default) applying probabilities of each FX
apply_prob_dict = {'eq' : 0.9, \
'comp' : 0.9, \
'pan' : 0.3, \
'imager' : 0.8, \
'gain': 0.5}
reverb_prob = {'drums' : 0.5, \
'bass' : 0.01, \
'vocals' : 0.9, \
'other' : 0.7}
self.mixing_manipulator = {}
for cur_inst in self.data_paths.keys():
if 'reverb' in apply_prob_dict.keys():
if cur_inst=='drums':
cur_reverb_weight = 0.5
elif cur_inst=='bass':
cur_reverb_weight = 0.1
else:
cur_reverb_weight = 1.0
apply_prob_dict['reverb'] *= cur_reverb_weight
else:
apply_prob_dict['reverb'] = reverb_prob[cur_inst]
# create FXmanipulator for current instrument
self.mixing_manipulator[cur_inst] = create_inst_effects_augmentation_chain_(cur_inst, \
apply_prob_dict=apply_prob_dict, \
ir_dir_path=args.ir_dir_path, \
sample_rate=args.sample_rate)
# for single effects
else:
self.mixing_manipulator = {}
if not isinstance(applying_effects, list):
applying_effects = [applying_effects]
for cur_inst in self.data_paths.keys():
self.mixing_manipulator[cur_inst] = create_effects_augmentation_chain(applying_effects, \
ir_dir_path=args.ir_dir_path)
def __len__(self):
if self.mode=='train':
return self.args.batch_size_total * 40
else:
return self.args.batch_size_total
def __getitem__(self, idx):
if self.mode=="train":
torch.manual_seed(int(time.time())*(idx+1) % (2**32-1))
np.random.seed(int(time.time())*(idx+1) % (2**32-1))
random.seed(int(time.time())*(idx+1) % (2**32-1))
else:
# fixed random seed for evaluation
torch.manual_seed(idx*self.fixed_random_seed)
np.random.seed(idx*self.fixed_random_seed)
random.seed(idx*self.fixed_random_seed)
manipulated_segments = {}
for cur_neg_idx in range(self.args.num_strong_negatives+1):
manipulated_segments[cur_neg_idx] = {}
# load already-saved data to save time for on-the-fly manipulation
cur_data_dir_path = f"{self.data_dir}manipulated_encoder/{self.args.data_save_name}/{self.applying_effects}/{idx}/"
if self.mode=="val" and os.path.exists(cur_data_dir_path):
for cur_inst in self.instruments:
for cur_neg_idx in range(self.args.num_strong_negatives+1):
cur_A_file_path = f"{cur_data_dir_path}{cur_inst}_A{cur_neg_idx+1}.wav"
cur_B_file_path = f"{cur_data_dir_path}{cur_inst}_B{cur_neg_idx+1}.wav"
cur_A = load_wav_segment(cur_A_file_path, axis=0, sample_rate=self.args.sample_rate)
cur_B = load_wav_segment(cur_B_file_path, axis=0, sample_rate=self.args.sample_rate)
manipulated_segments[cur_neg_idx][cur_inst] = [torch.from_numpy(cur_A).float(), torch.from_numpy(cur_B).float()]
else:
# repeat for number of instruments
for cur_inst, cur_paths in self.data_paths.items():
# choose file_path to be loaded
cur_chosen_paths = np.random.choice(cur_paths, 2, p = self.data_length_ratio_list[cur_inst])
# get random 2 starting points for each instrument
last_point_A = load_wav_length(cur_chosen_paths[0])-self.args.segment_length_ref
last_point_B = load_wav_length(cur_chosen_paths[1])-self.args.segment_length_ref
# simply load more data to prevent artifacts likely to be caused by the manipulator
if self.pad_b4_manipulation:
last_point_A -= self.pad_length*2
last_point_B -= self.pad_length*2
cur_inst_start_point_A = torch.randint(low=0, \
high=last_point_A, \
size=(1,))[0]
cur_inst_start_point_B = torch.randint(low=0, \
high=last_point_B, \
size=(1,))[0]
# load wav segments from the selected starting points
load_duration = self.args.segment_length_ref+self.pad_length*2 if self.pad_b4_manipulation else self.args.segment_length_ref
cur_inst_segment_A = load_wav_segment(cur_chosen_paths[0], \
start_point=cur_inst_start_point_A, \
duration=load_duration, \
axis=1, \
sample_rate=self.args.sample_rate)
cur_inst_segment_B = load_wav_segment(cur_chosen_paths[1], \
start_point=cur_inst_start_point_B, \
duration=load_duration, \
axis=1, \
sample_rate=self.args.sample_rate)
# mixing manipulation
# append A# and B# with its strong negative samples
for cur_neg_idx in range(self.args.num_strong_negatives+1):
cur_manipulated_segment_A, cur_manipulated_segment_B = self.mixing_manipulator[cur_inst]([cur_inst_segment_A, cur_inst_segment_B])
# remove over-loaded area
if self.pad_b4_manipulation:
cur_manipulated_segment_A = cur_manipulated_segment_A[self.pad_length:-self.pad_length]
cur_manipulated_segment_B = cur_manipulated_segment_B[self.pad_length:-self.pad_length]
manipulated_segments[cur_neg_idx][cur_inst] = [torch.clamp(torch.transpose(torch.from_numpy(cur_manipulated_segment_A).float(), 1, 0), min=-1, max=1), \
torch.clamp(torch.transpose(torch.from_numpy(cur_manipulated_segment_B).float(), 1, 0), min=-1, max=1)]
# check manipulated data by saving them
if self.mode=="val" and not os.path.exists(cur_data_dir_path):
os.makedirs(cur_dir_path, exist_ok=True)
for cur_inst in manipulated_segments[0].keys():
for cur_manipulated_key, cur_manipualted_dict in manipulated_segments.items():
sf.write(f"{cur_dir_path}{cur_inst}_A{cur_manipulated_key+1}.wav", torch.transpose(cur_manipualted_dict[cur_inst][0], 1, 0), self.args.sample_rate, 'PCM_16')
sf.write(f"{cur_dir_path}{cur_inst}_B{cur_manipulated_key+1}.wav", torch.transpose(cur_manipualted_dict[cur_inst][1], 1, 0), self.args.sample_rate, 'PCM_16')
output_list = []
output_list_param = []
for cur_inst in manipulated_segments[0].keys():
for cur_manipulated_key, cur_manipualted_dict in manipulated_segments.items():
output_list.extend(cur_manipualted_dict[cur_inst])
'''
Output format:
list of effects manipulated stems of each instrument
drums_A1, drums_B1, drums_A2, drums_B2, drums_A3, drums_B3, ... ,
bass_A1, bass_B1, bass_A2, bass_B2, bass_A3, bass_B3, ... ,
other_A1, other_B1, other_A2, other_B2, other_A3, other_B3, ... ,
vocals_A1, vocals_B1, vocals_A2, vocals_B2, vocals_A3, vocals_B3, ...
each stem has the shape of (number of channels, segment duration)
'''
return output_list
# generate random manipulated results for evaluation
def generate_contents_w_effects(self, num_content, num_effects, out_dir):
print(f"start generating random effects of {self.applying_effects} applied contents")
os.makedirs(out_dir, exist_ok=True)
manipulated_segments = {}
for cur_fx_idx in range(num_effects):
manipulated_segments[cur_fx_idx] = {}
# repeat for number of instruments
for cur_inst, cur_paths in self.data_paths.items():
# choose file_path to be loaded
cur_path = np.random.choice(cur_paths, 1, p = self.data_length_ratio_list[cur_inst])[0]
print(f"\tgenerating instrument : {cur_inst}")
# get random 2 starting points for each instrument
last_point = load_wav_length(cur_path)-self.args.segment_length_ref
# simply load more data to prevent artifacts likely to be caused by the manipulator
if self.pad_b4_manipulation:
last_point -= self.pad_length*2
cur_inst_start_points = torch.randint(low=0, \
high=last_point, \
size=(num_content,))
# load wav segments from the selected starting points
cur_inst_segments = []
for cur_num_content in range(num_content):
cur_ori_sample = load_wav_segment(cur_path, \
start_point=cur_inst_start_points[cur_num_content], \
duration=self.args.segment_length_ref, \
axis=1, \
sample_rate=self.args.sample_rate)
cur_inst_segments.append(cur_ori_sample)
sf.write(f"{out_dir}{cur_inst}_ori_{cur_num_content}.wav", cur_ori_sample, self.args.sample_rate, 'PCM_16')
# mixing manipulation
for cur_fx_idx in range(num_effects):
cur_manipulated_segments = self.mixing_manipulator[cur_inst](cur_inst_segments)
# remove over-loaded area
if self.pad_b4_manipulation:
for cur_man_idx in range(len(cur_manipulated_segments)):
cur_segment_trimmed = cur_manipulated_segments[cur_man_idx][self.pad_length:-self.pad_length]
cur_manipulated_segments[cur_man_idx] = torch.clamp(torch.transpose(torch.from_numpy(cur_segment_trimmed).float(), 1, 0), min=-1, max=1)
manipulated_segments[cur_fx_idx][cur_inst] = cur_manipulated_segments
# write generated data
# save each instruments
for cur_inst in manipulated_segments[0].keys():
for cur_manipulated_key, cur_manipualted_dict in manipulated_segments.items():
for cur_content_idx in range(num_content):
sf.write(f"{out_dir}{cur_inst}_{chr(65+cur_content_idx//26)}{chr(65+cur_content_idx%26)}{cur_manipulated_key+1}.wav", torch.transpose(cur_manipualted_dict[cur_inst][cur_content_idx], 1, 0), self.args.sample_rate, 'PCM_16')
# save mixture
for cur_manipulated_key, cur_manipualted_dict in manipulated_segments.items():
for cur_content_idx in range(num_content):
for cur_idx, cur_inst in enumerate(manipulated_segments[0].keys()):
if cur_idx==0:
cur_mixture = cur_manipualted_dict[cur_inst][cur_content_idx]
else:
cur_mixture += cur_manipualted_dict[cur_inst][cur_content_idx]
sf.write(f"{out_dir}mixture_{chr(65+cur_content_idx//26)}{chr(65+cur_content_idx%26)}{cur_manipulated_key+1}.wav", torch.transpose(cur_mixture, 1, 0), self.args.sample_rate, 'PCM_16')
return
# Data loader for training the 'Mastering Style Converter'
# loads two segments (A and B) from the dataset
# both segments are manipulated via Mastering Effects Manipulator (resulting A1, A2, and B2)
# one of the manipulated segment is used as a reference segment (B2), which is randomly manipulated the same as the ground truth segment (A2)
class MUSDB_Dataset_Mixing_Manipulated_Style_Transfer(Dataset):
def __init__(self, args, \
mode, \
applying_effects='full', \
apply_prob_dict=None):
self.args = args
self.data_dir = args.data_dir + mode + "/"
self.mode = mode
self.applying_effects = applying_effects
self.fixed_random_seed = args.random_seed
self.pad_b4_manipulation = args.pad_b4_manipulation
self.pad_length = 2048
if 'musdb' in args.using_dataset.lower():
self.instruments = ["drums", "bass", "other", "vocals"]
else:
raise NotImplementedError
# load data paths for each instrument
self.data_paths = {}
self.data_length_ratio_list = {}
for cur_inst in self.instruments:
self.data_paths[cur_inst] = glob(f'{self.data_dir}{cur_inst}_normalized_{self.args.normalization_order}_silence_trimmed*.wav') \
if args.use_normalized else glob(f'{self.data_dir}{cur_inst}_silence_trimmed.wav')
self.data_length_ratio_list[cur_inst] = []
# compute audio duration and its ratio
for cur_file_path in self.data_paths[cur_inst]:
cur_wav_length = load_wav_length(cur_file_path)
cur_inst_length_ratio = cur_wav_length / get_total_audio_length(self.data_paths[cur_inst])
self.data_length_ratio_list[cur_inst].append(cur_inst_length_ratio)
self.mixing_manipulator = {}
if applying_effects=='full':
if apply_prob_dict==None:
# initial (default) applying probabilities of each FX
# we don't update these probabilities for training the MixFXcloner
apply_prob_dict = {'eq' : 0.9, \
'comp' : 0.9, \
'pan' : 0.3, \
'imager' : 0.8, \
'gain': 0.5}
reverb_prob = {'drums' : 0.5, \
'bass' : 0.01, \
'vocals' : 0.9, \
'other' : 0.7}
for cur_inst in self.data_paths.keys():
if 'reverb' in apply_prob_dict.keys():
if cur_inst=='drums':
cur_reverb_weight = 0.5
elif cur_inst=='bass':
cur_reverb_weight = 0.1
else:
cur_reverb_weight = 1.0
apply_prob_dict['reverb'] *= cur_reverb_weight
else:
apply_prob_dict['reverb'] = reverb_prob[cur_inst]
self.mixing_manipulator[cur_inst] = create_inst_effects_augmentation_chain(cur_inst, \
apply_prob_dict=apply_prob_dict, \
ir_dir_path=args.ir_dir_path, \
sample_rate=args.sample_rate)
# for single effects
else:
if not isinstance(applying_effects, list):
applying_effects = [applying_effects]
for cur_inst in self.data_paths.keys():
self.mixing_manipulator[cur_inst] = create_effects_augmentation_chain(applying_effects, \
ir_dir_path=args.ir_dir_path)
def __len__(self):
min_length = get_total_audio_length(glob(f'{self.data_dir}vocals_normalized_{self.args.normalization_order}*.wav'))
data_len = min_length // self.args.segment_length
return data_len
def __getitem__(self, idx):
if self.mode=="train":
torch.manual_seed(int(time.time())*(idx+1) % (2**32-1))
np.random.seed(int(time.time())*(idx+1) % (2**32-1))
random.seed(int(time.time())*(idx+1) % (2**32-1))
else:
# fixed random seed for evaluation
torch.manual_seed(idx*self.fixed_random_seed)
np.random.seed(idx*self.fixed_random_seed)
random.seed(idx*self.fixed_random_seed)
manipulated_segments = {}
# load already-saved data to save time for on-the-fly manipulation
cur_data_dir_path = f"{self.data_dir}manipulated_converter/{self.args.data_save_name}/{self.applying_effects}/{idx}/"
if self.mode=="val" and os.path.exists(cur_data_dir_path):
for cur_inst in self.instruments:
cur_A1_file_path = f"{cur_data_dir_path}{cur_inst}_A1.wav"
cur_A2_file_path = f"{cur_data_dir_path}{cur_inst}_A2.wav"
cur_B2_file_path = f"{cur_data_dir_path}{cur_inst}_B2.wav"
cur_manipulated_segment_A1 = load_wav_segment(cur_A1_file_path, axis=0, sample_rate=self.args.sample_rate)
cur_manipulated_segment_A2 = load_wav_segment(cur_A2_file_path, axis=0, sample_rate=self.args.sample_rate)
cur_manipulated_segment_B2 = load_wav_segment(cur_B2_file_path, axis=0, sample_rate=self.args.sample_rate)
manipulated_segments[cur_inst] = [torch.from_numpy(cur_manipulated_segment_A1).float(), \
torch.from_numpy(cur_manipulated_segment_A2).float(), \
torch.from_numpy(cur_manipulated_segment_B2).float()]
else:
# repeat for number of instruments
for cur_inst, cur_paths in self.data_paths.items():
# choose file_path to be loaded
cur_chosen_paths = np.random.choice(cur_paths, 2, p = self.data_length_ratio_list[cur_inst])
# cur_chosen_paths = [cur_paths[idx], cur_paths[idx+1]]
# get random 2 starting points for each instrument
last_point_A = load_wav_length(cur_chosen_paths[0])-self.args.segment_length_ref
last_point_B = load_wav_length(cur_chosen_paths[1])-self.args.segment_length_ref
# simply load more data to prevent artifacts likely to be caused by the manipulator
if self.pad_b4_manipulation:
last_point_A -= self.pad_length*2
last_point_B -= self.pad_length*2
cur_inst_start_point_A = torch.randint(low=0, \
high=last_point_A, \
size=(1,))[0]
cur_inst_start_point_B = torch.randint(low=0, \
high=last_point_B, \
size=(1,))[0]
# load wav segments from the selected starting points
load_duration = self.args.segment_length_ref+self.pad_length*2 if self.pad_b4_manipulation else self.args.segment_length_ref
cur_inst_segment_A = load_wav_segment(cur_chosen_paths[0], \
start_point=cur_inst_start_point_A, \
duration=load_duration, \
axis=1, \
sample_rate=self.args.sample_rate)
cur_inst_segment_B = load_wav_segment(cur_chosen_paths[1], \
start_point=cur_inst_start_point_B, \
duration=load_duration, \
axis=1, \
sample_rate=self.args.sample_rate)
''' mixing manipulation '''
# manipulate segment A and B to produce
# input : A1 (normalized sample)
# ground truth : A2
# reference : B2
cur_manipulated_segment_A1 = cur_inst_segment_A
cur_manipulated_segment_A2, cur_manipulated_segment_B2 = self.mixing_manipulator[cur_inst]([cur_inst_segment_A, cur_inst_segment_B])
# remove over-loaded area
if self.pad_b4_manipulation:
cur_manipulated_segment_A1 = cur_manipulated_segment_A1[self.pad_length:-self.pad_length]
cur_manipulated_segment_A2 = cur_manipulated_segment_A2[self.pad_length:-self.pad_length]
cur_manipulated_segment_B2 = cur_manipulated_segment_B2[self.pad_length:-self.pad_length]
manipulated_segments[cur_inst] = [torch.clamp(torch.transpose(torch.from_numpy(cur_manipulated_segment_A1).float(), 1, 0), min=-1, max=1), \
torch.clamp(torch.transpose(torch.from_numpy(cur_manipulated_segment_A2).float(), 1, 0), min=-1, max=1), \
torch.clamp(torch.transpose(torch.from_numpy(cur_manipulated_segment_B2).float(), 1, 0), min=-1, max=1)]
# check manipulated data by saving them
if (self.mode=="val" and not os.path.exists(cur_data_dir_path)):
mixture_dict = {}
for cur_inst in manipulated_segments.keys():
cur_inst_dir_path = f"{cur_data_dir_path}{idx}/{cur_inst}/"
os.makedirs(cur_inst_dir_path, exist_ok=True)
sf.write(f"{cur_inst_dir_path}A1.wav", torch.transpose(manipulated_segments[cur_inst][0], 1, 0), self.args.sample_rate, 'PCM_16')
sf.write(f"{cur_inst_dir_path}A2.wav", torch.transpose(manipulated_segments[cur_inst][1], 1, 0), self.args.sample_rate, 'PCM_16')
sf.write(f"{cur_inst_dir_path}B2.wav", torch.transpose(manipulated_segments[cur_inst][2], 1, 0), self.args.sample_rate, 'PCM_16')
mixture_dict['A1'] = torch.transpose(manipulated_segments[cur_inst][0], 1, 0)
mixture_dict['A2'] = torch.transpose(manipulated_segments[cur_inst][1], 1, 0)
mixture_dict['B2'] = torch.transpose(manipulated_segments[cur_inst][2], 1, 0)
cur_mix_dir_path = f"{cur_data_dir_path}{idx}/mixture/"
os.makedirs(cur_mix_dir_path, exist_ok=True)
sf.write(f"{cur_mix_dir_path}A1.wav", mixture_dict['A1'], self.args.sample_rate, 'PCM_16')
sf.write(f"{cur_mix_dir_path}A2.wav", mixture_dict['A2'], self.args.sample_rate, 'PCM_16')
sf.write(f"{cur_mix_dir_path}B2.wav", mixture_dict['B2'], self.args.sample_rate, 'PCM_16')
output_list = []
for cur_inst in manipulated_segments.keys():
output_list.extend(manipulated_segments[cur_inst])
'''
Output format:
list of effects manipulated stems of each instrument
drums_A1, drums_A2, drums_B2,
bass_A1, bass_A2, bass_B2,
other_A1, other_A2, other_B2,
vocals_A1, vocals_A2, vocals_B2,
each stem has the shape of (number of channels, segment duration)
Notation :
A1 = input of the network
A2 = ground truth
B2 = reference track
'''
return output_list
# Data loader for inferencing the task 'Mixing Style Transfer'
### loads whole mixture or stems from the target directory
class Song_Dataset_Inference(Dataset):
def __init__(self, args):
self.args = args
self.data_dir = args.target_dir
self.interpolate = args.interpolation
self.instruments = args.instruments
self.data_dir_paths = sorted(glob(f"{self.data_dir}*/"))
self.input_name = args.input_file_name
self.reference_name = args.reference_file_name
self.stem_level_directory_name = args.stem_level_directory_name \
if self.args.do_not_separate else os.path.join(args.stem_level_directory_name, args.separation_model)
if self.interpolate:
self.reference_name_B = args.reference_file_name_2interpolate
# audio effects normalizer
if args.normalize_input:
self.normalization_chain = Audio_Effects_Normalizer(precomputed_feature_path=args.precomputed_normalization_feature, \
STEMS=args.instruments, \
EFFECTS=args.normalization_order)
def __len__(self):
return len(self.data_dir_paths)
def __getitem__(self, idx):
''' stem-level conversion '''
input_stems = []
reference_stems = []
reference_B_stems = []
for cur_inst in self.instruments:
cur_input_file_path = os.path.join(self.data_dir_paths[idx], self.stem_level_directory_name, self.input_name, cur_inst+'.wav')
cur_reference_file_path = os.path.join(self.data_dir_paths[idx], self.stem_level_directory_name, self.reference_name, cur_inst+'.wav')
# load wav
cur_input_wav = load_wav_segment(cur_input_file_path, axis=0, sample_rate=self.args.sample_rate)
cur_reference_wav = load_wav_segment(cur_reference_file_path, axis=0, sample_rate=self.args.sample_rate)
if self.args.normalize_input:
cur_input_wav = self.normalization_chain.normalize_audio(cur_input_wav.transpose(), src=cur_inst).transpose()
input_stems.append(torch.clamp(torch.from_numpy(cur_input_wav).float(), min=-1, max=1))
reference_stems.append(torch.clamp(torch.from_numpy(cur_reference_wav).float(), min=-1, max=1))
# for interpolation
if self.interpolate:
cur_reference_B_file_path = os.path.join(self.data_dir_paths[idx], self.stem_level_directory_name, self.reference_name_B, cur_inst+'.wav')
cur_reference_B_wav = load_wav_segment(cur_reference_B_file_path, axis=0, sample_rate=self.args.sample_rate)
reference_B_stems.append(torch.clamp(torch.from_numpy(cur_reference_B_wav).float(), min=-1, max=1))
dir_name = os.path.dirname(self.data_dir_paths[idx])
if self.interpolate:
return torch.stack(input_stems, dim=0), torch.stack(reference_stems, dim=0), torch.stack(reference_B_stems, dim=0), dir_name
else:
return torch.stack(input_stems, dim=0), torch.stack(reference_stems, dim=0), dir_name
# check dataset
if __name__ == '__main__':
"""
Test code of data loaders
"""
import time
print('checking dataset...')
total_epochs = 1
bs = 5
check_step_size = 3
collate_class = Collate_Variable_Length_Segments(args)
print('\n========== Effects Encoder ==========')
from config import args
##### generate samples with ranfom configuration
# args.normalization_order = 'eqcompimagegain'
# for cur_effect in ['full', 'gain', 'comp', 'reverb', 'eq', 'imager', 'pan']:
# start_time = time.time()
# dataset = MUSDB_Dataset_Mixing_Manipulated_FXencoder(args, mode='val', applying_effects=cur_effect, check_data=True)
# dataset.generate_contents_w_effects(num_content=25, num_effects=10)
# print(f'\t---time taken : {time.time()-start_time}---')
### training data loder
dataset = MUSDB_Dataset_Mixing_Manipulated_FXencoder(args, mode='train', applying_effects=['comp'])
data_loader = DataLoader(dataset, \
batch_size=bs, \
shuffle=False, \
collate_fn=collate_class.random_duration_segments_strong_negatives, \
drop_last=False, \
num_workers=0)
for epoch in range(total_epochs):
start_time_loader = time.time()
for step, output_list in enumerate(data_loader):
if step==check_step_size:
break
print(f'Epoch {epoch+1}/{total_epochs}\tStep {step+1}/{len(data_loader)}')
print(f'num contents : {len(output_list)}\tnum instruments : {len(output_list[0])}\tcontent A shape : {output_list[0][0].shape}\t content B shape : {output_list[1][0].shape} \ttime taken: {time.time()-start_time_loader:.4f}')
start_time_loader = time.time()
print('\n========== Mixing Style Transfer ==========')
from trainer_mixing_transfer.config_conv import args
### training data loder
dataset = MUSDB_Dataset_Mixing_Manipulated_Style_Transfer(args, mode='train')
data_loader = DataLoader(dataset, \
batch_size=bs, \
shuffle=False, \
collate_fn=collate_class.style_transfer_collate, \
drop_last=False, \
num_workers=0)
for epoch in range(total_epochs):
start_time_loader = time.time()
for step, output_list in enumerate(data_loader):
if step==check_step_size:
break
print(f'Epoch {epoch+1}/{total_epochs}\tStep {step+1}/{len(data_loader)}')
print(f'num contents : {len(output_list)}\tnum instruments : {len(output_list[0])}\tA1 shape : {output_list[0][0].shape}\tA2 shape : {output_list[1][0].shape}\tA3 shape : {output_list[2][0].shape}\ttime taken: {time.time()-start_time_loader:.4f}')
start_time_loader = time.time()
print('\n--- checking dataset completed ---')
|