Spaces:
Running
Running
File size: 12,410 Bytes
f32cd36 |
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 |
import typing
from typing import List
import torch
import torch.nn.functional as F
from audiotools import AudioSignal
from audiotools import STFTParams
from torch import nn
class L1Loss(nn.L1Loss):
"""L1 Loss between AudioSignals. Defaults
to comparing ``audio_data``, but any
attribute of an AudioSignal can be used.
Parameters
----------
attribute : str, optional
Attribute of signal to compare, defaults to ``audio_data``.
weight : float, optional
Weight of this loss, defaults to 1.0.
Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/distance.py
"""
def __init__(self, attribute: str = "audio_data", weight: float = 1.0, **kwargs):
self.attribute = attribute
self.weight = weight
super().__init__(**kwargs)
def forward(self, x: AudioSignal, y: AudioSignal):
"""
Parameters
----------
x : AudioSignal
Estimate AudioSignal
y : AudioSignal
Reference AudioSignal
Returns
-------
torch.Tensor
L1 loss between AudioSignal attributes.
"""
if isinstance(x, AudioSignal):
x = getattr(x, self.attribute)
y = getattr(y, self.attribute)
return super().forward(x, y)
class SISDRLoss(nn.Module):
"""
Computes the Scale-Invariant Source-to-Distortion Ratio between a batch
of estimated and reference audio signals or aligned features.
Parameters
----------
scaling : int, optional
Whether to use scale-invariant (True) or
signal-to-noise ratio (False), by default True
reduction : str, optional
How to reduce across the batch (either 'mean',
'sum', or none).], by default ' mean'
zero_mean : int, optional
Zero mean the references and estimates before
computing the loss, by default True
clip_min : int, optional
The minimum possible loss value. Helps network
to not focus on making already good examples better, by default None
weight : float, optional
Weight of this loss, defaults to 1.0.
Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/distance.py
"""
def __init__(
self,
scaling: int = True,
reduction: str = "mean",
zero_mean: int = True,
clip_min: int = None,
weight: float = 1.0,
):
self.scaling = scaling
self.reduction = reduction
self.zero_mean = zero_mean
self.clip_min = clip_min
self.weight = weight
super().__init__()
def forward(self, x: AudioSignal, y: AudioSignal):
eps = 1e-8
# nb, nc, nt
if isinstance(x, AudioSignal):
references = x.audio_data
estimates = y.audio_data
else:
references = x
estimates = y
nb = references.shape[0]
references = references.reshape(nb, 1, -1).permute(0, 2, 1)
estimates = estimates.reshape(nb, 1, -1).permute(0, 2, 1)
# samples now on axis 1
if self.zero_mean:
mean_reference = references.mean(dim=1, keepdim=True)
mean_estimate = estimates.mean(dim=1, keepdim=True)
else:
mean_reference = 0
mean_estimate = 0
_references = references - mean_reference
_estimates = estimates - mean_estimate
references_projection = (_references**2).sum(dim=-2) + eps
references_on_estimates = (_estimates * _references).sum(dim=-2) + eps
scale = (
(references_on_estimates / references_projection).unsqueeze(1)
if self.scaling
else 1
)
e_true = scale * _references
e_res = _estimates - e_true
signal = (e_true**2).sum(dim=1)
noise = (e_res**2).sum(dim=1)
sdr = -10 * torch.log10(signal / noise + eps)
if self.clip_min is not None:
sdr = torch.clamp(sdr, min=self.clip_min)
if self.reduction == "mean":
sdr = sdr.mean()
elif self.reduction == "sum":
sdr = sdr.sum()
return sdr
class MultiScaleSTFTLoss(nn.Module):
"""Computes the multi-scale STFT loss from [1].
Parameters
----------
window_lengths : List[int], optional
Length of each window of each STFT, by default [2048, 512]
loss_fn : typing.Callable, optional
How to compare each loss, by default nn.L1Loss()
clamp_eps : float, optional
Clamp on the log magnitude, below, by default 1e-5
mag_weight : float, optional
Weight of raw magnitude portion of loss, by default 1.0
log_weight : float, optional
Weight of log magnitude portion of loss, by default 1.0
pow : float, optional
Power to raise magnitude to before taking log, by default 2.0
weight : float, optional
Weight of this loss, by default 1.0
match_stride : bool, optional
Whether to match the stride of convolutional layers, by default False
References
----------
1. Engel, Jesse, Chenjie Gu, and Adam Roberts.
"DDSP: Differentiable Digital Signal Processing."
International Conference on Learning Representations. 2019.
Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/spectral.py
"""
def __init__(
self,
window_lengths: List[int] = [2048, 512],
loss_fn: typing.Callable = nn.L1Loss(),
clamp_eps: float = 1e-5,
mag_weight: float = 1.0,
log_weight: float = 1.0,
pow: float = 2.0,
weight: float = 1.0,
match_stride: bool = False,
window_type: str = None,
):
super().__init__()
self.stft_params = [
STFTParams(
window_length=w,
hop_length=w // 4,
match_stride=match_stride,
window_type=window_type,
)
for w in window_lengths
]
self.loss_fn = loss_fn
self.log_weight = log_weight
self.mag_weight = mag_weight
self.clamp_eps = clamp_eps
self.weight = weight
self.pow = pow
def forward(self, x: AudioSignal, y: AudioSignal):
"""Computes multi-scale STFT between an estimate and a reference
signal.
Parameters
----------
x : AudioSignal
Estimate signal
y : AudioSignal
Reference signal
Returns
-------
torch.Tensor
Multi-scale STFT loss.
"""
loss = 0.0
for s in self.stft_params:
x.stft(s.window_length, s.hop_length, s.window_type)
y.stft(s.window_length, s.hop_length, s.window_type)
loss += self.log_weight * self.loss_fn(
x.magnitude.clamp(self.clamp_eps).pow(self.pow).log10(),
y.magnitude.clamp(self.clamp_eps).pow(self.pow).log10(),
)
loss += self.mag_weight * self.loss_fn(x.magnitude, y.magnitude)
return loss
class MelSpectrogramLoss(nn.Module):
"""Compute distance between mel spectrograms. Can be used
in a multi-scale way.
Parameters
----------
n_mels : List[int]
Number of mels per STFT, by default [150, 80],
window_lengths : List[int], optional
Length of each window of each STFT, by default [2048, 512]
loss_fn : typing.Callable, optional
How to compare each loss, by default nn.L1Loss()
clamp_eps : float, optional
Clamp on the log magnitude, below, by default 1e-5
mag_weight : float, optional
Weight of raw magnitude portion of loss, by default 1.0
log_weight : float, optional
Weight of log magnitude portion of loss, by default 1.0
pow : float, optional
Power to raise magnitude to before taking log, by default 2.0
weight : float, optional
Weight of this loss, by default 1.0
match_stride : bool, optional
Whether to match the stride of convolutional layers, by default False
Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/spectral.py
"""
def __init__(
self,
n_mels: List[int] = [150, 80],
window_lengths: List[int] = [2048, 512],
loss_fn: typing.Callable = nn.L1Loss(),
clamp_eps: float = 1e-5,
mag_weight: float = 1.0,
log_weight: float = 1.0,
pow: float = 2.0,
weight: float = 1.0,
match_stride: bool = False,
mel_fmin: List[float] = [0.0, 0.0],
mel_fmax: List[float] = [None, None],
window_type: str = None,
):
super().__init__()
self.stft_params = [
STFTParams(
window_length=w,
hop_length=w // 4,
match_stride=match_stride,
window_type=window_type,
)
for w in window_lengths
]
self.n_mels = n_mels
self.loss_fn = loss_fn
self.clamp_eps = clamp_eps
self.log_weight = log_weight
self.mag_weight = mag_weight
self.weight = weight
self.mel_fmin = mel_fmin
self.mel_fmax = mel_fmax
self.pow = pow
def forward(self, x: AudioSignal, y: AudioSignal):
"""Computes mel loss between an estimate and a reference
signal.
Parameters
----------
x : AudioSignal
Estimate signal
y : AudioSignal
Reference signal
Returns
-------
torch.Tensor
Mel loss.
"""
loss = 0.0
for n_mels, fmin, fmax, s in zip(
self.n_mels, self.mel_fmin, self.mel_fmax, self.stft_params
):
kwargs = {
"window_length": s.window_length,
"hop_length": s.hop_length,
"window_type": s.window_type,
}
x_mels = x.mel_spectrogram(n_mels, mel_fmin=fmin, mel_fmax=fmax, **kwargs)
y_mels = y.mel_spectrogram(n_mels, mel_fmin=fmin, mel_fmax=fmax, **kwargs)
loss += self.log_weight * self.loss_fn(
x_mels.clamp(self.clamp_eps).pow(self.pow).log10(),
y_mels.clamp(self.clamp_eps).pow(self.pow).log10(),
)
loss += self.mag_weight * self.loss_fn(x_mels, y_mels)
return loss
class GANLoss(nn.Module):
"""
Computes a discriminator loss, given a discriminator on
generated waveforms/spectrograms compared to ground truth
waveforms/spectrograms. Computes the loss for both the
discriminator and the generator in separate functions.
"""
def __init__(self, discriminator):
super().__init__()
self.discriminator = discriminator
def forward(self, fake, real):
d_fake = self.discriminator(fake.audio_data)
d_real = self.discriminator(real.audio_data)
return d_fake, d_real
def discriminator_loss(self, fake, real):
d_fake, d_real = self.forward(fake.clone().detach(), real)
loss_d = 0
for x_fake, x_real in zip(d_fake, d_real):
loss_d += torch.mean(x_fake[-1] ** 2)
loss_d += torch.mean((1 - x_real[-1]) ** 2)
return loss_d
def generator_loss(self, fake, real):
d_fake, d_real = self.forward(fake, real)
loss_g = 0
for x_fake in d_fake:
loss_g += torch.mean((1 - x_fake[-1]) ** 2)
loss_feature = 0
for i in range(len(d_fake)):
for j in range(len(d_fake[i]) - 1):
loss_feature += F.l1_loss(d_fake[i][j], d_real[i][j].detach())
return loss_g, loss_feature
|