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"""Length regulator related modules.""" |
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import logging |
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import torch |
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import sys |
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import os |
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sys.path.append(os.path.dirname(__file__)) |
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from utils.nets_utils import pad_list |
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class LengthRegulator(torch.nn.Module): |
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"""Length regulator module for feed-forward Transformer. |
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This is a module of length regulator described in |
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`FastSpeech: Fast, Robust and Controllable Text to Speech`_. |
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The length regulator expands char or |
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phoneme-level embedding features to frame-level by repeating each |
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feature based on the corresponding predicted durations. |
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.. _`FastSpeech: Fast, Robust and Controllable Text to Speech`: |
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https://arxiv.org/pdf/1905.09263.pdf |
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""" |
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def __init__(self, pad_value=0.0): |
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"""Initilize length regulator module. |
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Args: |
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pad_value (float, optional): Value used for padding. |
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""" |
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super().__init__() |
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self.pad_value = pad_value |
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def forward(self, xs, ds, alpha=1.0): |
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"""Calculate forward propagation. |
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Args: |
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xs (Tensor): Batch of sequences of char or phoneme embeddings (B, Tmax, D). |
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ds (LongTensor): Batch of durations of each frame (B, T). |
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alpha (float, optional): Alpha value to control speed of speech. |
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Returns: |
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Tensor: replicated input tensor based on durations (B, T*, D). |
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""" |
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if alpha != 1.0: |
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assert alpha > 0 |
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ds = torch.round(ds.float() * alpha).long() |
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if ds.sum() == 0: |
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logging.warning( |
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"predicted durations includes all 0 sequences. " |
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"fill the first element with 1." |
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) |
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ds[ds.sum(dim=1).eq(0)] = 1 |
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repeat = [torch.repeat_interleave(x, d, dim=0) for x, d in zip(xs, ds)] |
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return pad_list(repeat, self.pad_value) |
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if __name__ == 'main': |
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