Spaces:
Sleeping
Sleeping
File size: 7,647 Bytes
fa25a07 |
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 |
import numpy as np
import torch
import torch.nn as nn
class Discriminator2DFactory(nn.Module):
def __init__(self, time_length, freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128,
norm_type='bn', reduction='sum'):
super(Discriminator2DFactory, self).__init__()
padding = (kernel[0] // 2, kernel[1] // 2)
def discriminator_block(in_filters, out_filters, first=False):
"""
Input: (B, in, 2H, 2W)
Output:(B, out, H, W)
"""
conv = nn.Conv2d(in_filters, out_filters, kernel, (2, 2), padding)
if norm_type == 'sn':
conv = nn.utils.spectral_norm(conv)
block = [
conv, # padding = kernel//2
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25)
]
if norm_type == 'bn' and not first:
block.append(nn.BatchNorm2d(out_filters, 0.8))
if norm_type == 'in' and not first:
block.append(nn.InstanceNorm2d(out_filters, affine=True))
block = nn.Sequential(*block)
return block
self.model = nn.ModuleList([
discriminator_block(c_in, hidden_size, first=True),
discriminator_block(hidden_size, hidden_size),
discriminator_block(hidden_size, hidden_size),
])
self.reduction = reduction
ds_size = (time_length // 2 ** 3, (freq_length + 7) // 2 ** 3)
if reduction != 'none':
# The height and width of downsampled image
self.adv_layer = nn.Linear(hidden_size * ds_size[0] * ds_size[1], 1)
else:
self.adv_layer = nn.Linear(hidden_size * ds_size[1], 1)
def forward(self, x):
"""
:param x: [B, C, T, n_bins]
:return: validity: [B, 1], h: List of hiddens
"""
h = []
for l in self.model:
x = l(x)
h.append(x)
if self.reduction != 'none':
x = x.view(x.shape[0], -1)
validity = self.adv_layer(x) # [B, 1]
else:
B, _, T_, _ = x.shape
x = x.transpose(1, 2).reshape(B, T_, -1)
validity = self.adv_layer(x)[:, :, 0] # [B, T]
return validity, h
class MultiWindowDiscriminator(nn.Module):
def __init__(self, time_lengths, cond_size=0, freq_length=80, kernel=(3, 3),
c_in=1, hidden_size=128, norm_type='bn', reduction='sum'):
super(MultiWindowDiscriminator, self).__init__()
self.win_lengths = time_lengths
self.reduction = reduction
self.conv_layers = nn.ModuleList()
if cond_size > 0:
self.cond_proj_layers = nn.ModuleList()
self.mel_proj_layers = nn.ModuleList()
for time_length in time_lengths:
conv_layer = [
Discriminator2DFactory(
time_length, freq_length, kernel, c_in=c_in, hidden_size=hidden_size,
norm_type=norm_type, reduction=reduction)
]
self.conv_layers += conv_layer
if cond_size > 0:
self.cond_proj_layers.append(nn.Linear(cond_size, freq_length))
self.mel_proj_layers.append(nn.Linear(freq_length, freq_length))
def forward(self, x, x_len, cond=None, start_frames_wins=None):
'''
Args:
x (tensor): input mel, (B, c_in, T, n_bins).
x_length (tensor): len of per mel. (B,).
Returns:
tensor : (B).
'''
validity = []
if start_frames_wins is None:
start_frames_wins = [None] * len(self.conv_layers)
h = []
for i, start_frames in zip(range(len(self.conv_layers)), start_frames_wins):
x_clip, c_clip, start_frames = self.clip(
x, cond, x_len, self.win_lengths[i], start_frames) # (B, win_length, C)
start_frames_wins[i] = start_frames
if x_clip is None:
continue
if cond is not None:
x_clip = self.mel_proj_layers[i](x_clip) # (B, 1, win_length, C)
c_clip = self.cond_proj_layers[i](c_clip)[:, None] # (B, 1, win_length, C)
x_clip = x_clip + c_clip
x_clip, h_ = self.conv_layers[i](x_clip)
h += h_
validity.append(x_clip)
if len(validity) != len(self.conv_layers):
return None, start_frames_wins, h
if self.reduction == 'sum':
validity = sum(validity) # [B]
elif self.reduction == 'stack':
validity = torch.stack(validity, -1) # [B, W_L]
elif self.reduction == 'none':
validity = torch.cat(validity, -1) # [B, W_sum]
return validity, start_frames_wins, h
def clip(self, x, cond, x_len, win_length, start_frames=None):
'''Ramdom clip x to win_length.
Args:
x (tensor) : (B, c_in, T, n_bins).
cond (tensor) : (B, T, H).
x_len (tensor) : (B,).
win_length (int): target clip length
Returns:
(tensor) : (B, c_in, win_length, n_bins).
'''
T_start = 0
T_end = x_len.max() - win_length
if T_end < 0:
return None, None, start_frames
T_end = T_end.item()
if start_frames is None:
start_frame = np.random.randint(low=T_start, high=T_end + 1)
start_frames = [start_frame] * x.size(0)
else:
start_frame = start_frames[0]
x_batch = x[:, :, start_frame: start_frame + win_length]
c_batch = cond[:, start_frame: start_frame + win_length] if cond is not None else None
return x_batch, c_batch, start_frames
class Discriminator(nn.Module):
def __init__(self, time_lengths=[32, 64, 128], freq_length=80, cond_size=0, kernel=(3, 3), c_in=1,
hidden_size=128, norm_type='bn', reduction='sum', uncond_disc=True):
super(Discriminator, self).__init__()
self.time_lengths = time_lengths
self.cond_size = cond_size
self.reduction = reduction
self.uncond_disc = uncond_disc
if uncond_disc:
self.discriminator = MultiWindowDiscriminator(
freq_length=freq_length,
time_lengths=time_lengths,
kernel=kernel,
c_in=c_in, hidden_size=hidden_size, norm_type=norm_type,
reduction=reduction
)
if cond_size > 0:
self.cond_disc = MultiWindowDiscriminator(
freq_length=freq_length,
time_lengths=time_lengths,
cond_size=cond_size,
kernel=kernel,
c_in=c_in, hidden_size=hidden_size, norm_type=norm_type,
reduction=reduction
)
def forward(self, x, cond=None, start_frames_wins=None):
"""
:param x: [B, T, 80]
:param cond: [B, T, cond_size]
:param return_y_only:
:return:
"""
if len(x.shape) == 3:
x = x[:, None, :, :]
x_len = x.sum([1, -1]).ne(0).int().sum([-1])
ret = {'y_c': None, 'y': None}
if self.uncond_disc:
ret['y'], start_frames_wins, ret['h'] = self.discriminator(
x, x_len, start_frames_wins=start_frames_wins)
if self.cond_size > 0 and cond is not None:
ret['y_c'], start_frames_wins, ret['h_c'] = self.cond_disc(
x, x_len, cond, start_frames_wins=start_frames_wins)
ret['start_frames_wins'] = start_frames_wins
return ret |