Upload VariancePredictor.py

VariancePredictor.py

@@ -0,0 +1,65 @@
+# Copyright 2019 Tomoki Hayashi
+# MIT License (https://opensource.org/licenses/MIT)
+# Adapted by Florian Lux 2021
+
+from abc import ABC
+
+import torch
+
+from Layers.LayerNorm import LayerNorm
+
+
+class VariancePredictor(torch.nn.Module, ABC):
+    """
+    Variance predictor module.
+
+    This is a module of variance predictor described in `FastSpeech 2:
+    Fast and High-Quality End-to-End Text to Speech`_.
+
+    .. _`FastSpeech 2: Fast and High-Quality End-to-End Text to Speech`:
+        https://arxiv.org/abs/2006.04558
+
+    """
+
+    def __init__(self, idim, n_layers=2, n_chans=384, kernel_size=3, bias=True, dropout_rate=0.5, ):
+        """
+        Initilize duration predictor module.
+
+        Args:
+            idim (int): Input dimension.
+            n_layers (int, optional): Number of convolutional layers.
+            n_chans (int, optional): Number of channels of convolutional layers.
+            kernel_size (int, optional): Kernel size of convolutional layers.
+            dropout_rate (float, optional): Dropout rate.
+        """
+        super().__init__()
+        self.conv = torch.nn.ModuleList()
+        for idx in range(n_layers):
+            in_chans = idim if idx == 0 else n_chans
+            self.conv += [
+                torch.nn.Sequential(torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=(kernel_size - 1) // 2, bias=bias, ), torch.nn.ReLU(),
+                                    LayerNorm(n_chans, dim=1), torch.nn.Dropout(dropout_rate), )]
+        self.linear = torch.nn.Linear(n_chans, 1)
+
+    def forward(self, xs, x_masks=None):
+        """
+        Calculate forward propagation.
+
+        Args:
+            xs (Tensor): Batch of input sequences (B, Tmax, idim).
+            x_masks (ByteTensor, optional):
+                Batch of masks indicating padded part (B, Tmax).
+
+        Returns:
+            Tensor: Batch of predicted sequences (B, Tmax, 1).
+        """
+        xs = xs.transpose(1, -1)  # (B, idim, Tmax)
+        for f in self.conv:
+            xs = f(xs)  # (B, C, Tmax)
+
+        xs = self.linear(xs.transpose(1, 2))  # (B, Tmax, 1)
+
+        if x_masks is not None:
+            xs = xs.masked_fill(x_masks, 0.0)
+
+        return xs