mirror of https://github.com/coqui-ai/TTS.git
58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
import torch
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from torch import nn
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from ..generic.normalization import LayerNorm
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class DurationPredictor(nn.Module):
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"""Glow-TTS duration prediction model.
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[2 x (conv1d_kxk -> relu -> layer_norm -> dropout)] -> conv1d_1x1 -> durs
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Args:
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in_channels ([type]): [description]
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hidden_channels ([type]): [description]
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kernel_size ([type]): [description]
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dropout_p ([type]): [description]
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"""
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def __init__(self, in_channels, hidden_channels, kernel_size, dropout_p):
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super().__init__()
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# class arguments
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self.in_channels = in_channels
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self.filter_channels = hidden_channels
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self.kernel_size = kernel_size
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self.dropout_p = dropout_p
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# layers
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self.drop = nn.Dropout(dropout_p)
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self.conv_1 = nn.Conv1d(in_channels,
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hidden_channels,
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kernel_size,
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padding=kernel_size // 2)
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self.norm_1 = LayerNorm(hidden_channels)
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self.conv_2 = nn.Conv1d(hidden_channels,
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hidden_channels,
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kernel_size,
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padding=kernel_size // 2)
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self.norm_2 = LayerNorm(hidden_channels)
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# output layer
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self.proj = nn.Conv1d(hidden_channels, 1, 1)
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def forward(self, x, x_mask):
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"""
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Shapes:
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x: [B, C, T]
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x_mask: [B, 1, T]
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Returns:
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[type]: [description]
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"""
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x = self.conv_1(x * x_mask)
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x = torch.relu(x)
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x = self.norm_1(x)
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x = self.drop(x)
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x = self.conv_2(x * x_mask)
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x = torch.relu(x)
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x = self.norm_2(x)
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x = self.drop(x)
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x = self.proj(x * x_mask)
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return x * x_mask
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