Spaces:
Sleeping
Sleeping
File size: 22,920 Bytes
b175ee9 140ad27 b175ee9 c7866f1 b175ee9 c7866f1 b45dcb0 c7866f1 a3e84f7 c7866f1 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 |
import torch
import torchaudio
import numpy as np
import scipy.signal
import scipy.stats
import pyloudnorm as pyln
from torchvision.transforms import Compose, RandomApply
from typing import List
from pedalboard import (
Pedalboard,
Chorus,
Reverb,
Compressor,
Phaser,
Delay,
Distortion,
Limiter,
)
__all__ = []
def loguniform(low=0, high=1):
return scipy.stats.loguniform.rvs(low, high)
def rand(low=0, high=1):
return (torch.rand(1).numpy()[0] * (high - low)) + low
def randint(low=0, high=1):
return torch.randint(low, high + 1, (1,)).numpy()[0]
def biqaud(
gain_db: float,
cutoff_freq: float,
q_factor: float,
sample_rate: float,
filter_type: str,
):
"""Use design parameters to generate coeffieicnets for a specific filter type.
Args:
gain_db (float): Shelving filter gain in dB.
cutoff_freq (float): Cutoff frequency in Hz.
q_factor (float): Q factor.
sample_rate (float): Sample rate in Hz.
filter_type (str): Filter type.
One of "low_shelf", "high_shelf", or "peaking"
Returns:
b (np.ndarray): Numerator filter coefficients stored as [b0, b1, b2]
a (np.ndarray): Denominator filter coefficients stored as [a0, a1, a2]
"""
A = 10 ** (gain_db / 40.0)
w0 = 2.0 * np.pi * (cutoff_freq / sample_rate)
alpha = np.sin(w0) / (2.0 * q_factor)
cos_w0 = np.cos(w0)
sqrt_A = np.sqrt(A)
if filter_type == "high_shelf":
b0 = A * ((A + 1) + (A - 1) * cos_w0 + 2 * sqrt_A * alpha)
b1 = -2 * A * ((A - 1) + (A + 1) * cos_w0)
b2 = A * ((A + 1) + (A - 1) * cos_w0 - 2 * sqrt_A * alpha)
a0 = (A + 1) - (A - 1) * cos_w0 + 2 * sqrt_A * alpha
a1 = 2 * ((A - 1) - (A + 1) * cos_w0)
a2 = (A + 1) - (A - 1) * cos_w0 - 2 * sqrt_A * alpha
elif filter_type == "low_shelf":
b0 = A * ((A + 1) - (A - 1) * cos_w0 + 2 * sqrt_A * alpha)
b1 = 2 * A * ((A - 1) - (A + 1) * cos_w0)
b2 = A * ((A + 1) - (A - 1) * cos_w0 - 2 * sqrt_A * alpha)
a0 = (A + 1) + (A - 1) * cos_w0 + 2 * sqrt_A * alpha
a1 = -2 * ((A - 1) + (A + 1) * cos_w0)
a2 = (A + 1) + (A - 1) * cos_w0 - 2 * sqrt_A * alpha
elif filter_type == "peaking":
b0 = 1 + alpha * A
b1 = -2 * cos_w0
b2 = 1 - alpha * A
a0 = 1 + alpha / A
a1 = -2 * cos_w0
a2 = 1 - alpha / A
else:
pass
b = np.array([b0, b1, b2]) / a0
a = np.array([a0, a1, a2]) / a0
return b, a
def parametric_eq(
x: np.ndarray,
sample_rate: float,
low_shelf_gain_db: float = 0.0,
low_shelf_cutoff_freq: float = 80.0,
low_shelf_q_factor: float = 0.707,
band_gains_db: List[float] = [0.0],
band_cutoff_freqs: List[float] = [300.0],
band_q_factors: List[float] = [0.707],
high_shelf_gain_db: float = 0.0,
high_shelf_cutoff_freq: float = 1000.0,
high_shelf_q_factor: float = 0.707,
dtype=np.float32,
):
"""Multiband parametric EQ.
Low-shelf -> Band 1 -> ... -> Band N -> High-shelf
Args:
"""
assert (
len(band_gains_db) == len(band_cutoff_freqs) == len(band_q_factors)
) # must define for all bands
# -------- apply low-shelf filter --------
b, a = biqaud(
low_shelf_gain_db,
low_shelf_cutoff_freq,
low_shelf_q_factor,
sample_rate,
"low_shelf",
)
x = scipy.signal.lfilter(b, a, x)
# -------- apply peaking filters --------
for gain_db, cutoff_freq, q_factor in zip(
band_gains_db, band_cutoff_freqs, band_q_factors
):
b, a = biqaud(
gain_db,
cutoff_freq,
q_factor,
sample_rate,
"peaking",
)
x = scipy.signal.lfilter(b, a, x)
# -------- apply high-shelf filter --------
b, a = biqaud(
high_shelf_gain_db,
high_shelf_cutoff_freq,
high_shelf_q_factor,
sample_rate,
"high_shelf",
)
sos5 = np.concatenate((b, a))
x = scipy.signal.lfilter(b, a, x)
return x.astype(dtype)
class RandomParametricEQ(torch.nn.Module):
def __init__(
self,
sample_rate: float,
num_bands: int = 3,
min_gain_db: float = -6.0,
max_gain_db: float = +6.0,
min_cutoff_freq: float = 1000.0,
max_cutoff_freq: float = 10000.0,
min_q_factor: float = 0.1,
max_q_factor: float = 4.0,
):
super().__init__()
self.sample_rate = sample_rate
self.num_bands = num_bands
self.min_gain_db = min_gain_db
self.max_gain_db = max_gain_db
self.min_cutoff_freq = min_cutoff_freq
self.max_cutoff_freq = max_cutoff_freq
self.min_q_factor = min_q_factor
self.max_q_factor = max_q_factor
def forward(self, x: torch.Tensor):
"""
Args:
x: (torch.Tensor): Array of audio samples with shape (chs, seq_leq).
The filter will be applied the final dimension, and by default the same
filter will be applied to all channels.
"""
low_shelf_gain_db = rand(self.min_gain_db, self.max_gain_db)
low_shelf_cutoff_freq = loguniform(20.0, 200.0)
low_shelf_q_factor = rand(self.min_q_factor, self.max_q_factor)
high_shelf_gain_db = rand(self.min_gain_db, self.max_gain_db)
high_shelf_cutoff_freq = loguniform(8000.0, 16000.0)
high_shelf_q_factor = rand(self.min_q_factor, self.max_q_factor)
band_gain_dbs = []
band_cutoff_freqs = []
band_q_factors = []
for _ in range(self.num_bands):
band_gain_dbs.append(rand(self.min_gain_db, self.max_gain_db))
band_cutoff_freqs.append(
loguniform(self.min_cutoff_freq, self.max_cutoff_freq)
)
band_q_factors.append(rand(self.min_q_factor, self.max_q_factor))
y = parametric_eq(
x.numpy(),
self.sample_rate,
low_shelf_gain_db=low_shelf_gain_db,
low_shelf_cutoff_freq=low_shelf_cutoff_freq,
low_shelf_q_factor=low_shelf_q_factor,
band_gains_db=band_gain_dbs,
band_cutoff_freqs=band_cutoff_freqs,
band_q_factors=band_q_factors,
high_shelf_gain_db=high_shelf_gain_db,
high_shelf_cutoff_freq=high_shelf_cutoff_freq,
high_shelf_q_factor=high_shelf_q_factor,
)
return torch.from_numpy(y)
def stereo_widener(x: torch.Tensor, width: torch.Tensor):
sqrt2 = np.sqrt(2)
left = x[0, ...]
right = x[1, ...]
mid = (left + right) / sqrt2
side = (left - right) / sqrt2
# amplify mid and side signal seperately:
mid *= 2 * (1 - width)
side *= 2 * width
left = (mid + side) / sqrt2
right = (mid - side) / sqrt2
x = torch.stack((left, right), dim=0)
return x
class RandomStereoWidener(torch.nn.Module):
def __init__(
self,
sample_rate: float,
min_width: float = 0.0,
max_width: float = 1.0,
) -> None:
super().__init__()
self.sample_rate = sample_rate
self.min_width = min_width
self.max_width = max_width
def forward(self, x: torch.Tensor):
width = rand(self.min_width, self.max_width)
return stereo_widener(x, width)
class RandomVolumeAutomation(torch.nn.Module):
def __init__(
self,
sample_rate: float,
min_segments: int = 1,
max_segments: int = 3,
min_gain_db: float = -6.0,
max_gain_db: float = 6.0,
) -> None:
super().__init__()
self.sample_rate = sample_rate
self.min_segments = min_segments
self.max_segments = max_segments
self.min_gain_db = min_gain_db
self.max_gain_db = max_gain_db
def forward(self, x: torch.Tensor):
gain_db = torch.zeros(x.shape[-1]).type_as(x)
num_segments = randint(self.min_segments, self.max_segments)
segment_lengths = (
x.shape[-1]
* np.random.dirichlet([rand(0, 10) for _ in range(num_segments)], 1)
).astype("int")[0]
samples_filled = 0
start_gain_db = 0
for idx in range(num_segments):
segment_samples = segment_lengths[idx]
if idx != 0:
start_gain_db = end_gain_db
# sample random end gain
end_gain_db = rand(self.min_gain_db, self.max_gain_db)
fade = torch.linspace(start_gain_db, end_gain_db, steps=segment_samples)
gain_db[samples_filled : samples_filled + segment_samples] = fade
samples_filled = samples_filled + segment_samples
x *= 10 ** (gain_db / 20.0)
return x
class RandomPedalboardCompressor(torch.nn.Module):
def __init__(
self,
sample_rate: float,
min_threshold_db: float = -42.0,
max_threshold_db: float = -6.0,
min_ratio: float = 1.5,
max_ratio: float = 4.0,
min_attack_ms: float = 1.0,
max_attack_ms: float = 50.0,
min_release_ms: float = 10.0,
max_release_ms: float = 250.0,
) -> None:
super().__init__()
self.sample_rate = sample_rate
self.min_threshold_db = min_threshold_db
self.max_threshold_db = max_threshold_db
self.min_ratio = min_ratio
self.max_ratio = max_ratio
self.min_attack_ms = min_attack_ms
self.max_attack_ms = max_attack_ms
self.min_release_ms = min_release_ms
self.max_release_ms = max_release_ms
def forward(self, x: torch.Tensor):
board = Pedalboard()
threshold_db = rand(self.min_threshold_db, self.max_threshold_db)
ratio = rand(self.min_ratio, self.max_ratio)
attack_ms = rand(self.min_attack_ms, self.max_attack_ms)
release_ms = rand(self.min_release_ms, self.max_release_ms)
board.append(
Compressor(
threshold_db=threshold_db,
ratio=ratio,
attack_ms=attack_ms,
release_ms=release_ms,
)
)
# process audio using the pedalboard
return torch.from_numpy(board(x.numpy(), self.sample_rate))
class RandomPedalboardDelay(torch.nn.Module):
def __init__(
self,
sample_rate: float,
min_delay_seconds: float = 0.1,
max_delay_sconds: float = 1.0,
min_feedback: float = 0.05,
max_feedback: float = 0.6,
min_mix: float = 0.0,
max_mix: float = 0.7,
) -> None:
super().__init__()
self.sample_rate = sample_rate
self.min_delay_seconds = min_delay_seconds
self.max_delay_seconds = max_delay_sconds
self.min_feedback = min_feedback
self.max_feedback = max_feedback
self.min_mix = min_mix
self.max_mix = max_mix
def forward(self, x: torch.Tensor):
board = Pedalboard()
delay_seconds = loguniform(self.min_delay_seconds, self.max_delay_seconds)
feedback = rand(self.min_feedback, self.max_feedback)
mix = rand(self.min_mix, self.max_mix)
board.append(Delay(delay_seconds=delay_seconds, feedback=feedback, mix=mix))
return torch.from_numpy(board(x.numpy(), self.sample_rate))
class RandomPedalboardChorus(torch.nn.Module):
def __init__(
self,
sample_rate: float,
min_rate_hz: float = 0.25,
max_rate_hz: float = 4.0,
min_depth: float = 0.0,
max_depth: float = 0.6,
min_centre_delay_ms: float = 5.0,
max_centre_delay_ms: float = 10.0,
min_feedback: float = 0.1,
max_feedback: float = 0.6,
min_mix: float = 0.1,
max_mix: float = 0.7,
) -> None:
super().__init__()
self.sample_rate = sample_rate
self.min_rate_hz = min_rate_hz
self.max_rate_hz = max_rate_hz
self.min_depth = min_depth
self.max_depth = max_depth
self.min_centre_delay_ms = min_centre_delay_ms
self.max_centre_delay_ms = max_centre_delay_ms
self.min_feedback = min_feedback
self.max_feedback = max_feedback
self.min_mix = min_mix
self.max_mix = max_mix
def forward(self, x: torch.Tensor):
board = Pedalboard()
rate_hz = rand(self.min_rate_hz, self.max_rate_hz)
depth = rand(self.min_depth, self.max_depth)
centre_delay_ms = rand(self.min_centre_delay_ms, self.max_centre_delay_ms)
feedback = rand(self.min_feedback, self.max_feedback)
mix = rand(self.min_mix, self.max_mix)
board.append(
Chorus(
rate_hz=rate_hz,
depth=depth,
centre_delay_ms=centre_delay_ms,
feedback=feedback,
mix=mix,
)
)
# process audio using the pedalboard
return torch.from_numpy(board(x.numpy(), self.sample_rate))
class RandomPedalboardPhaser(torch.nn.Module):
def __init__(
self,
sample_rate: float,
min_rate_hz: float = 0.25,
max_rate_hz: float = 5.0,
min_depth: float = 0.1,
max_depth: float = 0.6,
min_centre_frequency_hz: float = 200.0,
max_centre_frequency_hz: float = 600.0,
min_feedback: float = 0.1,
max_feedback: float = 0.6,
min_mix: float = 0.1,
max_mix: float = 0.7,
) -> None:
super().__init__()
self.sample_rate = sample_rate
self.min_rate_hz = min_rate_hz
self.max_rate_hz = max_rate_hz
self.min_depth = min_depth
self.max_depth = max_depth
self.min_centre_frequency_hz = min_centre_frequency_hz
self.max_centre_frequency_hz = max_centre_frequency_hz
self.min_feedback = min_feedback
self.max_feedback = max_feedback
self.min_mix = min_mix
self.max_mix = max_mix
def forward(self, x: torch.Tensor):
board = Pedalboard()
rate_hz = rand(self.min_rate_hz, self.max_rate_hz)
depth = rand(self.min_depth, self.max_depth)
centre_frequency_hz = rand(
self.min_centre_frequency_hz, self.min_centre_frequency_hz
)
feedback = rand(self.min_feedback, self.max_feedback)
mix = rand(self.min_mix, self.max_mix)
board.append(
Phaser(
rate_hz=rate_hz,
depth=depth,
centre_frequency_hz=centre_frequency_hz,
feedback=feedback,
mix=mix,
)
)
# process audio using the pedalboard
return torch.from_numpy(board(x.numpy(), self.sample_rate))
class RandomPedalboardLimiter(torch.nn.Module):
def __init__(
self,
sample_rate: float,
min_threshold_db: float = -32.0,
max_threshold_db: float = -6.0,
min_release_ms: float = 10.0,
max_release_ms: float = 300.0,
) -> None:
super().__init__()
self.sample_rate = sample_rate
self.min_threshold_db = min_threshold_db
self.max_threshold_db = max_threshold_db
self.min_release_ms = min_release_ms
self.max_release_ms = max_release_ms
def forward(self, x: torch.Tensor):
board = Pedalboard()
threshold_db = rand(self.min_threshold_db, self.max_threshold_db)
release_ms = rand(self.min_release_ms, self.max_release_ms)
board.append(
Limiter(
threshold_db=threshold_db,
release_ms=release_ms,
)
)
return torch.from_numpy(board(x.numpy(), self.sample_rate))
class RandomPedalboardDistortion(torch.nn.Module):
def __init__(
self,
sample_rate: float,
min_drive_db: float = -20.0,
max_drive_db: float = 12.0,
):
super().__init__()
self.sample_rate = sample_rate
self.min_drive_db = min_drive_db
self.max_drive_db = max_drive_db
def forward(self, x: torch.Tensor):
board = Pedalboard()
drive_db = rand(self.min_drive_db, self.max_drive_db)
board.append(Distortion(drive_db=drive_db))
return torch.from_numpy(board(x.numpy(), self.sample_rate))
class RandomSoxReverb(torch.nn.Module):
def __init__(
self,
sample_rate: float,
min_reverberance: float = 10.0,
max_reverberance: float = 100.0,
min_high_freq_damping: float = 0.0,
max_high_freq_damping: float = 100.0,
min_wet_dry: float = 0.0,
max_wet_dry: float = 1.0,
min_room_scale: float = 5.0,
max_room_scale: float = 100.0,
min_stereo_depth: float = 20.0,
max_stereo_depth: float = 100.0,
min_pre_delay: float = 0.0,
max_pre_delay: float = 100.0,
) -> None:
super().__init__()
self.sample_rate = sample_rate
self.min_reverberance = min_reverberance
self.max_reverberance = max_reverberance
self.min_high_freq_damping = min_high_freq_damping
self.max_high_freq_damping = max_high_freq_damping
self.min_wet_dry = min_wet_dry
self.max_wet_dry = max_wet_dry
self.min_room_scale = min_room_scale
self.max_room_scale = max_room_scale
self.min_stereo_depth = min_stereo_depth
self.max_stereo_depth = max_stereo_depth
self.min_pre_delay = min_pre_delay
self.max_pre_delay = max_pre_delay
def forward(self, x: torch.Tensor):
reverberance = rand(self.min_reverberance, self.max_reverberance)
high_freq_damping = rand(self.min_high_freq_damping, self.max_high_freq_damping)
room_scale = rand(self.min_room_scale, self.max_room_scale)
stereo_depth = rand(self.min_stereo_depth, self.max_stereo_depth)
wet_dry = rand(self.min_wet_dry, self.max_wet_dry)
pre_delay = rand(self.min_pre_delay, self.max_pre_delay)
effects = [
[
"reverb",
f"{reverberance}",
f"{high_freq_damping}",
f"{room_scale}",
f"{stereo_depth}",
f"{pre_delay}",
"--wet-only",
]
]
y, _ = torchaudio.sox_effects.apply_effects_tensor(
x, self.sample_rate, effects, channels_first=True
)
# manual wet/dry mix
return (x * (1 - wet_dry)) + (y * wet_dry)
class RandomPedalboardReverb(torch.nn.Module):
def __init__(
self,
sample_rate: float,
min_room_size: float = 0.0,
max_room_size: float = 1.0,
min_damping: float = 0.0,
max_damping: float = 1.0,
min_wet_dry: float = 0.0,
max_wet_dry: float = 0.7,
min_width: float = 0.0,
max_width: float = 1.0,
) -> None:
super().__init__()
self.sample_rate = sample_rate
self.min_room_size = min_room_size
self.max_room_size = max_room_size
self.min_damping = min_damping
self.max_damping = max_damping
self.min_wet_dry = min_wet_dry
self.max_wet_dry = max_wet_dry
self.min_width = min_width
self.max_width = max_width
def forward(self, x: torch.Tensor):
board = Pedalboard()
room_size = rand(self.min_room_size, self.max_room_size)
damping = rand(self.min_damping, self.max_damping)
wet_dry = rand(self.min_wet_dry, self.max_wet_dry)
width = rand(self.min_width, self.max_width)
board.append(
Reverb(
room_size=room_size,
damping=damping,
wet_level=wet_dry,
dry_level=(1 - wet_dry),
width=width,
)
)
return torch.from_numpy(board(x.numpy(), self.sample_rate))
class LoudnessNormalize(torch.nn.Module):
def __init__(self, sample_rate: float, target_lufs_db: float = -32.0) -> None:
super().__init__()
self.meter = pyln.Meter(sample_rate)
self.target_lufs_db = target_lufs_db
def forward(self, x: torch.Tensor):
x_lufs_db = self.meter.integrated_loudness(x.permute(1, 0).numpy())
delta_lufs_db = torch.tensor([self.target_lufs_db - x_lufs_db]).float()
gain_lin = 10.0 ** (delta_lufs_db.clamp(-120, 40.0) / 20.0)
return gain_lin * x
class RandomAudioEffectsChannel(torch.nn.Module):
def __init__(
self,
sample_rate: float,
parametric_eq_prob: float = 0.7,
distortion_prob: float = 0.01,
delay_prob: float = 0.1,
chorus_prob: float = 0.01,
phaser_prob: float = 0.01,
compressor_prob: float = 0.4,
reverb_prob: float = 0.2,
stereo_widener_prob: float = 0.3,
limiter_prob: float = 0.3,
vol_automation_prob: float = 0.7,
target_lufs_db: float = -32.0,
) -> None:
super().__init__()
self.transforms = Compose(
[
RandomApply(
[RandomParametricEQ(sample_rate)],
p=parametric_eq_prob,
),
RandomApply(
[RandomPedalboardDistortion(sample_rate)],
p=distortion_prob,
),
RandomApply(
[RandomPedalboardDelay(sample_rate)],
p=delay_prob,
),
RandomApply(
[RandomPedalboardChorus(sample_rate)],
p=chorus_prob,
),
RandomApply(
[RandomPedalboardPhaser(sample_rate)],
p=phaser_prob,
),
RandomApply(
[RandomPedalboardCompressor(sample_rate)],
p=compressor_prob,
),
RandomApply(
[RandomPedalboardReverb(sample_rate)],
p=reverb_prob,
),
RandomApply(
[RandomStereoWidener(sample_rate)],
p=stereo_widener_prob,
),
RandomApply(
[RandomPedalboardLimiter(sample_rate)],
p=limiter_prob,
),
RandomApply(
[RandomVolumeAutomation(sample_rate)],
p=vol_automation_prob,
),
LoudnessNormalize(sample_rate, target_lufs_db=target_lufs_db),
]
)
def forward(self, x: torch.Tensor):
return self.transforms(x)
Pedalboard_Effects = [
RandomPedalboardReverb,
RandomPedalboardChorus,
RandomPedalboardDelay,
RandomPedalboardDistortion,
RandomPedalboardCompressor,
# RandomPedalboardPhaser,
# RandomPedalboardLimiter,
]
|