Spaces:
Running
on
Zero
Running
on
Zero
File size: 8,180 Bytes
2bd0633 |
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 |
from typing import List, Optional, Tuple
import torch
from einops import rearrange
from torch import nn
from torch.nn import Conv2d
from torch.nn.utils import weight_norm
from torchaudio.transforms import Spectrogram
class MultiPeriodDiscriminator(nn.Module):
"""
Multi-Period Discriminator module adapted from https://github.com/jik876/hifi-gan.
Additionally, it allows incorporating conditional information with a learned embeddings table.
Args:
periods (tuple[int]): Tuple of periods for each discriminator.
num_embeddings (int, optional): Number of embeddings. None means non-conditional discriminator.
Defaults to None.
"""
def __init__(self, periods: Tuple[int, ...] = (2, 3, 5, 7, 11), num_embeddings: Optional[int] = None):
super().__init__()
self.discriminators = nn.ModuleList([DiscriminatorP(period=p, num_embeddings=num_embeddings) for p in periods])
def forward(
self, y: torch.Tensor, y_hat: torch.Tensor, bandwidth_id: Optional[torch.Tensor] = None
) -> Tuple[List[torch.Tensor], List[torch.Tensor], List[List[torch.Tensor]], List[List[torch.Tensor]]]:
y_d_rs = []
y_d_gs = []
fmap_rs = []
fmap_gs = []
for d in self.discriminators:
y_d_r, fmap_r = d(x=y, cond_embedding_id=bandwidth_id)
y_d_g, fmap_g = d(x=y_hat, cond_embedding_id=bandwidth_id)
y_d_rs.append(y_d_r)
fmap_rs.append(fmap_r)
y_d_gs.append(y_d_g)
fmap_gs.append(fmap_g)
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
class DiscriminatorP(nn.Module):
def __init__(
self,
period: int,
in_channels: int = 1,
kernel_size: int = 5,
stride: int = 3,
lrelu_slope: float = 0.1,
num_embeddings: Optional[int] = None,
):
super().__init__()
self.period = period
self.convs = nn.ModuleList(
[
weight_norm(Conv2d(in_channels, 32, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))),
weight_norm(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))),
weight_norm(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))),
weight_norm(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))),
weight_norm(Conv2d(1024, 1024, (kernel_size, 1), (1, 1), padding=(kernel_size // 2, 0))),
]
)
if num_embeddings is not None:
self.emb = torch.nn.Embedding(num_embeddings=num_embeddings, embedding_dim=1024)
torch.nn.init.zeros_(self.emb.weight)
self.conv_post = weight_norm(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
self.lrelu_slope = lrelu_slope
def forward(
self, x: torch.Tensor, cond_embedding_id: Optional[torch.Tensor] = None
) -> Tuple[torch.Tensor, List[torch.Tensor]]:
x = x.unsqueeze(1)
fmap = []
# 1d to 2d
b, c, t = x.shape
if t % self.period != 0: # pad first
n_pad = self.period - (t % self.period)
x = torch.nn.functional.pad(x, (0, n_pad), "reflect")
t = t + n_pad
x = x.view(b, c, t // self.period, self.period)
for i, l in enumerate(self.convs):
x = l(x)
x = torch.nn.functional.leaky_relu(x, self.lrelu_slope)
if i > 0:
fmap.append(x)
if cond_embedding_id is not None:
emb = self.emb(cond_embedding_id)
h = (emb.view(1, -1, 1, 1) * x).sum(dim=1, keepdims=True)
else:
h = 0
x = self.conv_post(x)
fmap.append(x)
x += h
x = torch.flatten(x, 1, -1)
return x, fmap
class MultiResolutionDiscriminator(nn.Module):
def __init__(
self,
fft_sizes: Tuple[int, ...] = (2048, 1024, 512),
num_embeddings: Optional[int] = None,
):
"""
Multi-Resolution Discriminator module adapted from https://github.com/descriptinc/descript-audio-codec.
Additionally, it allows incorporating conditional information with a learned embeddings table.
Args:
fft_sizes (tuple[int]): Tuple of window lengths for FFT. Defaults to (2048, 1024, 512).
num_embeddings (int, optional): Number of embeddings. None means non-conditional discriminator.
Defaults to None.
"""
super().__init__()
self.discriminators = nn.ModuleList(
[DiscriminatorR(window_length=w, num_embeddings=num_embeddings) for w in fft_sizes]
)
def forward(
self, y: torch.Tensor, y_hat: torch.Tensor, bandwidth_id: torch.Tensor = None
) -> Tuple[List[torch.Tensor], List[torch.Tensor], List[List[torch.Tensor]], List[List[torch.Tensor]]]:
y_d_rs = []
y_d_gs = []
fmap_rs = []
fmap_gs = []
for d in self.discriminators:
y_d_r, fmap_r = d(x=y, cond_embedding_id=bandwidth_id)
y_d_g, fmap_g = d(x=y_hat, cond_embedding_id=bandwidth_id)
y_d_rs.append(y_d_r)
fmap_rs.append(fmap_r)
y_d_gs.append(y_d_g)
fmap_gs.append(fmap_g)
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
class DiscriminatorR(nn.Module):
def __init__(
self,
window_length: int,
num_embeddings: Optional[int] = None,
channels: int = 32,
hop_factor: float = 0.25,
bands: Tuple[Tuple[float, float], ...] = ((0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)),
):
super().__init__()
self.window_length = window_length
self.hop_factor = hop_factor
self.spec_fn = Spectrogram(
n_fft=window_length, hop_length=int(window_length * hop_factor), win_length=window_length, power=None
)
n_fft = window_length // 2 + 1
bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands]
self.bands = bands
convs = lambda: nn.ModuleList(
[
weight_norm(nn.Conv2d(2, channels, (3, 9), (1, 1), padding=(1, 4))),
weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
weight_norm(nn.Conv2d(channels, channels, (3, 3), (1, 1), padding=(1, 1))),
]
)
self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))])
if num_embeddings is not None:
self.emb = torch.nn.Embedding(num_embeddings=num_embeddings, embedding_dim=channels)
torch.nn.init.zeros_(self.emb.weight)
self.conv_post = weight_norm(nn.Conv2d(channels, 1, (3, 3), (1, 1), padding=(1, 1)))
def spectrogram(self, x):
# Remove DC offset
x = x - x.mean(dim=-1, keepdims=True)
# Peak normalize the volume of input audio
x = 0.8 * x / (x.abs().max(dim=-1, keepdim=True)[0] + 1e-9)
x = self.spec_fn(x)
x = torch.view_as_real(x)
x = rearrange(x, "b f t c -> b c t f")
# Split into bands
x_bands = [x[..., b[0] : b[1]] for b in self.bands]
return x_bands
def forward(self, x: torch.Tensor, cond_embedding_id: torch.Tensor = None):
x_bands = self.spectrogram(x)
fmap = []
x = []
for band, stack in zip(x_bands, self.band_convs):
for i, layer in enumerate(stack):
band = layer(band)
band = torch.nn.functional.leaky_relu(band, 0.1)
if i > 0:
fmap.append(band)
x.append(band)
x = torch.cat(x, dim=-1)
if cond_embedding_id is not None:
emb = self.emb(cond_embedding_id)
h = (emb.view(1, -1, 1, 1) * x).sum(dim=1, keepdims=True)
else:
h = 0
x = self.conv_post(x)
fmap.append(x)
x += h
return x, fmap
|