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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) | |