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