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import torch
import torch.nn as nn # pylint: disable=consider-using-from-import
from TTS.tts.layers.delightful_tts.conv_layers import ConvTransposed
class VariancePredictor(nn.Module):
"""
Network is 2-layer 1D convolutions with leaky relu activation and then
followed by layer normalization then a dropout layer and finally an
extra linear layer to project the hidden states into the output sequence.
Args:
channels_in (int): Number of in channels for conv layers.
channels_out (int): Number of out channels for the last linear layer.
kernel_size (int): Size the kernel for the conv layers.
p_dropout (float): Probability of dropout.
lrelu_slope (float): Slope for the leaky relu.
Inputs: inputs, mask
- **inputs** (batch, time, dim): Tensor containing input vector
- **mask** (batch, time): Tensor containing indices to be masked
Returns:
- **outputs** (batch, time): Tensor produced by last linear layer.
"""
def __init__(
self, channels_in: int, channels: int, channels_out: int, kernel_size: int, p_dropout: float, lrelu_slope: float
):
super().__init__()
self.layers = nn.ModuleList(
[
ConvTransposed(
channels_in,
channels,
kernel_size=kernel_size,
padding=(kernel_size - 1) // 2,
),
nn.LeakyReLU(lrelu_slope),
nn.LayerNorm(channels),
nn.Dropout(p_dropout),
ConvTransposed(
channels,
channels,
kernel_size=kernel_size,
padding=(kernel_size - 1) // 2,
),
nn.LeakyReLU(lrelu_slope),
nn.LayerNorm(channels),
nn.Dropout(p_dropout),
]
)
self.linear_layer = nn.Linear(channels, channels_out)
def forward(self, x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
"""
Shapes:
x: :math: `[B, T_src, C]`
mask: :math: `[B, T_src]`
"""
for layer in self.layers:
x = layer(x)
x = self.linear_layer(x)
x = x.squeeze(-1)
x = x.masked_fill(mask, 0.0)
return x
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