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e8a8918 | 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 | import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
import typing as tp
class DiscriminatorBlock1d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int,
padding: int,
groups: int = 1,
norm: bool = True,
activation: bool = True
):
super().__init__()
self.activation = activation
conv_layer = nn.Conv1d(in_channels, out_channels, kernel_size, stride, padding, groups=groups)
if norm:
self.conv = nn.utils.spectral_norm(conv_layer)
else:
self.conv = conv_layer
if self.activation:
self.act_fn = nn.LeakyReLU(0.2, inplace=True)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.conv(x)
if self.activation:
x = self.act_fn(x)
return x
class DiscriminatorBlock2d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: tp.Tuple[int, int],
stride: tp.Tuple[int, int],
padding: tp.Tuple[int, int],
norm: bool = True,
activation: bool = True
):
super().__init__()
self.activation = activation
conv_layer = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
if norm:
self.conv = nn.utils.spectral_norm(conv_layer)
else:
self.conv = conv_layer
if self.activation:
self.act_fn = nn.LeakyReLU(0.2, inplace=True)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.conv(x)
if self.activation:
x = self.act_fn(x)
return x
class ResolutionDiscriminatorBlock(nn.Module):
def __init__(
self,
window_length: int,
nch: int = 1,
sample_rate: int = 48000,
hop_factor: float = 0.25,
bands: tp.List[tp.Tuple[float, float]] = [(0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)],
norm: bool = True,
hidden_channels: int = 32
):
super().__init__()
self.window_length = window_length
self.hop_length = int(window_length * hop_factor)
self.sample_rate = sample_rate
self.nch = nch
n_fft_bins = window_length // 2 + 1
self.bands = [(int(b[0] * n_fft_bins), int(b[1] * n_fft_bins)) for b in bands]
self.band_discriminators = nn.ModuleList()
for _ in self.bands:
layers = nn.ModuleList([
DiscriminatorBlock2d(2 * nch, hidden_channels, (3, 9), (1, 1), padding=(1, 4), norm=norm),
DiscriminatorBlock2d(hidden_channels, hidden_channels, (3, 9), (1, 2), padding=(1, 4), norm=norm),
DiscriminatorBlock2d(hidden_channels, hidden_channels, (3, 9), (1, 2), padding=(1, 4), norm=norm),
DiscriminatorBlock2d(hidden_channels, hidden_channels, (3, 9), (1, 2), padding=(1, 4), norm=norm),
DiscriminatorBlock2d(hidden_channels, hidden_channels, (3, 3), (1, 1), padding=(1, 1), norm=norm),
])
self.band_discriminators.append(layers)
self.output_conv = DiscriminatorBlock2d(hidden_channels, 1, (3, 3), (1, 1), padding=(1, 1), norm=norm, activation=False)
def forward(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.List[torch.Tensor]]:
fmaps = []
band_outputs = []
x_spec = torch.stft(
x.reshape(-1, x.shape[-1]),
n_fft=self.window_length,
hop_length=self.hop_length,
win_length=self.window_length,
window=torch.hann_window(self.window_length, device=x.device),
return_complex=True
)
x_ri = torch.stack([x_spec.real, x_spec.imag], dim=1)
B, C, _ = x.shape
x_ri = x_ri.view(B, C * 2, x_ri.shape[-2], x_ri.shape[-1])
x_ri = rearrange(x_ri, 'b c f t -> b c t f')
for i, (band_start, band_end) in enumerate(self.bands):
x_band = x_ri[..., band_start:band_end]
disc_stack = self.band_discriminators[i]
for layer in disc_stack:
x_band = layer(x_band)
fmaps.append(x_band)
band_outputs.append(x_band)
x_combined = torch.cat(band_outputs, dim=-1)
score = self.output_conv(x_combined)
return score, fmaps
class MultiResolutionDiscriminator(nn.Module):
def __init__(self, nch: int = 1, sample_rate: int = 48000, window_lengths: tp.List[int] = [2048, 1024, 512], hop_factor: float = 0.25, bands: tp.List[tp.Tuple[float, float]] = [(0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)], norm: bool = True, hidden_channels: int = 32):
super().__init__()
self.nch = nch
self.sample_rate = sample_rate
self.window_lengths = window_lengths
self.hop_factor = hop_factor
self.bands = bands
self.norm = norm
self.hidden_channels = hidden_channels
self.discriminators = nn.ModuleList([ResolutionDiscriminatorBlock(window_length, nch, sample_rate, hop_factor, bands, norm, hidden_channels) for window_length in window_lengths])
def forward(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.List[torch.Tensor]]:
scores = []
fmaps = []
for discriminator in self.discriminators:
score, fmap = discriminator(x)
scores.append(score)
fmaps.append(fmap)
return scores, fmaps
if __name__ == '__main__':
N_CHANNELS = 1
SAMPLE_RATE = 48000
model = MultiResolutionDiscriminator(
nch=N_CHANNELS,
sample_rate=SAMPLE_RATE,
window_lengths=[2048, 1024, 512],
hop_factor=0.25,
bands=[(0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)],
norm=True,
hidden_channels=32
)
dummy_audio = torch.randn(2, N_CHANNELS, SAMPLE_RATE)
scores_list, fmaps_list = model(dummy_audio)
for score, fmap in zip(scores_list, fmaps_list):
for fm in fmap:
print(fm.shape) |