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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)
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