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import torch
from torch import nn
from torch.nn import Module
from models.tts.delightful_tts.constants import LEAKY_RELU_SLOPE
from models.tts.delightful_tts.conv_blocks import ConvTransposed
class VariancePredictor(Module):
r"""Duration and Pitch predictor neural network module in PyTorch.
It consists of multiple layers, including `ConvTransposed` layers (custom convolution transpose layers from
the `model.conv_blocks` module), LeakyReLU activation functions, Layer Normalization and Dropout layers.
Constructor for `VariancePredictor` class.
Args:
channels_in (int): Number of input channels.
channels (int): Number of output channels for ConvTransposed layers and input channels for linear layer.
channels_out (int): Number of output channels for linear layer.
kernel_size (int): Size of the kernel for ConvTransposed layers.
p_dropout (float): Probability of dropout.
Returns:
torch.Tensor: Output tensor.
"""
def __init__(
self,
channels_in: int,
channels: int,
channels_out: int,
kernel_size: int,
p_dropout: float,
leaky_relu_slope: float = LEAKY_RELU_SLOPE,
):
super().__init__()
self.layers = nn.ModuleList(
[
# Convolution transpose layer followed by LeakyReLU, LayerNorm and Dropout
ConvTransposed(
channels_in,
channels,
kernel_size=kernel_size,
padding=(kernel_size - 1) // 2,
),
nn.LeakyReLU(leaky_relu_slope),
nn.LayerNorm(
channels,
),
nn.Dropout(p_dropout),
# Another "block" of ConvTransposed, LeakyReLU, LayerNorm, and Dropout
ConvTransposed(
channels,
channels,
kernel_size=kernel_size,
padding=(kernel_size - 1) // 2,
),
nn.LeakyReLU(leaky_relu_slope),
nn.LayerNorm(
channels,
),
nn.Dropout(p_dropout),
],
)
# Output linear layer
self.linear_layer = nn.Linear(
channels,
channels_out,
)
def forward(self, x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
r"""Forward pass for `VariancePredictor`.
Args:
x (torch.Tensor): Input tensor.
mask (torch.Tensor): Mask tensor, has the same size as x.
Returns:
torch.Tensor: Output tensor.
"""
# Sequentially pass the input through all defined layers
# (ConvTransposed -> LeakyReLU -> LayerNorm -> Dropout -> ConvTransposed -> LeakyReLU -> LayerNorm -> Dropout)
for layer in self.layers:
x = layer(x)
x = self.linear_layer(x)
x = x.squeeze(-1)
return x.masked_fill(mask, 0.0)
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