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103 lines
3.4 KiB
103 lines
3.4 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Modified from espnet(https://github.com/espnet/espnet)
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"""Variance predictor related modules."""
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import paddle
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from paddle import nn
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from typeguard import check_argument_types
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from paddlespeech.t2s.modules.layer_norm import LayerNorm
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from paddlespeech.t2s.modules.masked_fill import masked_fill
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class VariancePredictor(nn.Layer):
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"""Variance predictor module.
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This is a module of variacne predictor described in `FastSpeech 2:
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Fast and High-Quality End-to-End Text to Speech`_.
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.. _`FastSpeech 2: Fast and High-Quality End-to-End Text to Speech`:
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https://arxiv.org/abs/2006.04558
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"""
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def __init__(
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self,
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idim: int,
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n_layers: int=2,
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n_chans: int=384,
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kernel_size: int=3,
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bias: bool=True,
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dropout_rate: float=0.5, ):
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"""Initilize duration predictor module.
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Args:
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idim (int):
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Input dimension.
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n_layers (int, optional):
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Number of convolutional layers.
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n_chans (int, optional):
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Number of channels of convolutional layers.
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kernel_size (int, optional):
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Kernel size of convolutional layers.
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dropout_rate (float, optional):
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Dropout rate.
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"""
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assert check_argument_types()
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super().__init__()
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self.conv = nn.LayerList()
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for idx in range(n_layers):
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in_chans = idim if idx == 0 else n_chans
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self.conv.append(
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nn.Sequential(
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nn.Conv1D(
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in_chans,
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n_chans,
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kernel_size,
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stride=1,
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padding=(kernel_size - 1) // 2,
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bias_attr=True, ),
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nn.ReLU(),
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LayerNorm(n_chans, dim=1),
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nn.Dropout(dropout_rate), ))
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self.linear = nn.Linear(n_chans, 1, bias_attr=True)
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def forward(self, xs: paddle.Tensor,
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x_masks: paddle.Tensor=None) -> paddle.Tensor:
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"""Calculate forward propagation.
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Args:
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xs (Tensor):
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Batch of input sequences (B, Tmax, idim).
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x_masks (Tensor(bool), optional):
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Batch of masks indicating padded part (B, Tmax, 1).
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Returns:
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Tensor:
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Batch of predicted sequences (B, Tmax, 1).
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"""
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# (B, idim, Tmax)
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xs = xs.transpose([0, 2, 1])
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# (B, C, Tmax)
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for f in self.conv:
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# (B, C, Tmax)
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xs = f(xs)
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# (B, Tmax, 1)
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xs = self.linear(xs.transpose([0, 2, 1]))
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if x_masks is not None:
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xs = masked_fill(xs, x_masks, 0.0)
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return xs
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