File size: 9,680 Bytes
9b2107c |
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 |
import math
import torch
from torch import nn
from TTS.tts.layers.glow_tts.glow import WN
from TTS.tts.layers.glow_tts.transformer import RelativePositionTransformer
from TTS.tts.utils.helpers import sequence_mask
LRELU_SLOPE = 0.1
def convert_pad_shape(pad_shape):
l = pad_shape[::-1]
pad_shape = [item for sublist in l for item in sublist]
return pad_shape
def init_weights(m, mean=0.0, std=0.01):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
m.weight.data.normal_(mean, std)
def get_padding(kernel_size, dilation=1):
return int((kernel_size * dilation - dilation) / 2)
class TextEncoder(nn.Module):
def __init__(
self,
n_vocab: int,
out_channels: int,
hidden_channels: int,
hidden_channels_ffn: int,
num_heads: int,
num_layers: int,
kernel_size: int,
dropout_p: float,
language_emb_dim: int = None,
):
"""Text Encoder for VITS model.
Args:
n_vocab (int): Number of characters for the embedding layer.
out_channels (int): Number of channels for the output.
hidden_channels (int): Number of channels for the hidden layers.
hidden_channels_ffn (int): Number of channels for the convolutional layers.
num_heads (int): Number of attention heads for the Transformer layers.
num_layers (int): Number of Transformer layers.
kernel_size (int): Kernel size for the FFN layers in Transformer network.
dropout_p (float): Dropout rate for the Transformer layers.
"""
super().__init__()
self.out_channels = out_channels
self.hidden_channels = hidden_channels
self.emb = nn.Embedding(n_vocab, hidden_channels)
nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
if language_emb_dim:
hidden_channels += language_emb_dim
self.encoder = RelativePositionTransformer(
in_channels=hidden_channels,
out_channels=hidden_channels,
hidden_channels=hidden_channels,
hidden_channels_ffn=hidden_channels_ffn,
num_heads=num_heads,
num_layers=num_layers,
kernel_size=kernel_size,
dropout_p=dropout_p,
layer_norm_type="2",
rel_attn_window_size=4,
)
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
def forward(self, x, x_lengths, lang_emb=None):
"""
Shapes:
- x: :math:`[B, T]`
- x_length: :math:`[B]`
"""
assert x.shape[0] == x_lengths.shape[0]
x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
# concat the lang emb in embedding chars
if lang_emb is not None:
x = torch.cat((x, lang_emb.transpose(2, 1).expand(x.size(0), x.size(1), -1)), dim=-1)
x = torch.transpose(x, 1, -1) # [b, h, t]
x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) # [b, 1, t]
x = self.encoder(x * x_mask, x_mask)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
return x, m, logs, x_mask
class ResidualCouplingBlock(nn.Module):
def __init__(
self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
num_layers,
dropout_p=0,
cond_channels=0,
mean_only=False,
):
assert channels % 2 == 0, "channels should be divisible by 2"
super().__init__()
self.half_channels = channels // 2
self.mean_only = mean_only
# input layer
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
# coupling layers
self.enc = WN(
hidden_channels,
hidden_channels,
kernel_size,
dilation_rate,
num_layers,
dropout_p=dropout_p,
c_in_channels=cond_channels,
)
# output layer
# Initializing last layer to 0 makes the affine coupling layers
# do nothing at first. This helps with training stability
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
self.post.weight.data.zero_()
self.post.bias.data.zero_()
def forward(self, x, x_mask, g=None, reverse=False):
"""
Note:
Set `reverse` to True for inference.
Shapes:
- x: :math:`[B, C, T]`
- x_mask: :math:`[B, 1, T]`
- g: :math:`[B, C, 1]`
"""
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
h = self.pre(x0) * x_mask
h = self.enc(h, x_mask, g=g)
stats = self.post(h) * x_mask
if not self.mean_only:
m, log_scale = torch.split(stats, [self.half_channels] * 2, 1)
else:
m = stats
log_scale = torch.zeros_like(m)
if not reverse:
x1 = m + x1 * torch.exp(log_scale) * x_mask
x = torch.cat([x0, x1], 1)
logdet = torch.sum(log_scale, [1, 2])
return x, logdet
else:
x1 = (x1 - m) * torch.exp(-log_scale) * x_mask
x = torch.cat([x0, x1], 1)
return x
class ResidualCouplingBlocks(nn.Module):
def __init__(
self,
channels: int,
hidden_channels: int,
kernel_size: int,
dilation_rate: int,
num_layers: int,
num_flows=4,
cond_channels=0,
):
"""Redisual Coupling blocks for VITS flow layers.
Args:
channels (int): Number of input and output tensor channels.
hidden_channels (int): Number of hidden network channels.
kernel_size (int): Kernel size of the WaveNet layers.
dilation_rate (int): Dilation rate of the WaveNet layers.
num_layers (int): Number of the WaveNet layers.
num_flows (int, optional): Number of Residual Coupling blocks. Defaults to 4.
cond_channels (int, optional): Number of channels of the conditioning tensor. Defaults to 0.
"""
super().__init__()
self.channels = channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.num_layers = num_layers
self.num_flows = num_flows
self.cond_channels = cond_channels
self.flows = nn.ModuleList()
for _ in range(num_flows):
self.flows.append(
ResidualCouplingBlock(
channels,
hidden_channels,
kernel_size,
dilation_rate,
num_layers,
cond_channels=cond_channels,
mean_only=True,
)
)
def forward(self, x, x_mask, g=None, reverse=False):
"""
Note:
Set `reverse` to True for inference.
Shapes:
- x: :math:`[B, C, T]`
- x_mask: :math:`[B, 1, T]`
- g: :math:`[B, C, 1]`
"""
if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
x = torch.flip(x, [1])
else:
for flow in reversed(self.flows):
x = torch.flip(x, [1])
x = flow(x, x_mask, g=g, reverse=reverse)
return x
class PosteriorEncoder(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
hidden_channels: int,
kernel_size: int,
dilation_rate: int,
num_layers: int,
cond_channels=0,
):
"""Posterior Encoder of VITS model.
::
x -> conv1x1() -> WaveNet() (non-causal) -> conv1x1() -> split() -> [m, s] -> sample(m, s) -> z
Args:
in_channels (int): Number of input tensor channels.
out_channels (int): Number of output tensor channels.
hidden_channels (int): Number of hidden channels.
kernel_size (int): Kernel size of the WaveNet convolution layers.
dilation_rate (int): Dilation rate of the WaveNet layers.
num_layers (int): Number of the WaveNet layers.
cond_channels (int, optional): Number of conditioning tensor channels. Defaults to 0.
"""
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.num_layers = num_layers
self.cond_channels = cond_channels
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
self.enc = WN(
hidden_channels, hidden_channels, kernel_size, dilation_rate, num_layers, c_in_channels=cond_channels
)
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
def forward(self, x, x_lengths, g=None):
"""
Shapes:
- x: :math:`[B, C, T]`
- x_lengths: :math:`[B, 1]`
- g: :math:`[B, C, 1]`
"""
x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
x = self.pre(x) * x_mask
x = self.enc(x, x_mask, g=g)
stats = self.proj(x) * x_mask
mean, log_scale = torch.split(stats, self.out_channels, dim=1)
z = (mean + torch.randn_like(mean) * torch.exp(log_scale)) * x_mask
return z, mean, log_scale, x_mask
|