Upload LengthRegulator.py

LengthRegulator.py

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+# Copyright 2019 Tomoki Hayashi
+# MIT License (https://opensource.org/licenses/MIT)
+# Adapted by Florian Lux 2021
+
+from abc import ABC
+
+import torch
+
+from Utility.utils import pad_list
+
+
+class LengthRegulator(torch.nn.Module, ABC):
+    """
+    Length regulator module for feed-forward Transformer.
+
+    This is a module of length regulator described in
+    `FastSpeech: Fast, Robust and Controllable Text to Speech`_.
+    The length regulator expands char or
+    phoneme-level embedding features to frame-level by repeating each
+    feature based on the corresponding predicted durations.
+
+    .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
+        https://arxiv.org/pdf/1905.09263.pdf
+
+    """
+
+    def __init__(self, pad_value=0.0):
+        """
+        Initialize length regulator module.
+
+        Args:
+            pad_value (float, optional): Value used for padding.
+        """
+        super(LengthRegulator, self).__init__()
+        self.pad_value = pad_value
+
+    def forward(self, xs, ds, alpha=1.0):
+        """
+        Calculate forward propagation.
+
+        Args:
+            xs (Tensor): Batch of sequences of char or phoneme embeddings (B, Tmax, D).
+            ds (LongTensor): Batch of durations of each frame (B, T).
+            alpha (float, optional): Alpha value to control speed of speech.
+
+        Returns:
+            Tensor: replicated input tensor based on durations (B, T*, D).
+        """
+        if alpha != 1.0:
+            assert alpha > 0
+            ds = torch.round(ds.float() * alpha).long()
+
+        if ds.sum() == 0:
+            ds[ds.sum(dim=1).eq(0)] = 1
+
+        return pad_list([self._repeat_one_sequence(x, d) for x, d in zip(xs, ds)], self.pad_value)
+
+    def _repeat_one_sequence(self, x, d):
+        """
+        Repeat each frame according to duration
+        """
+        return torch.repeat_interleave(x, d, dim=0)