You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
PaddleSpeech/paddlespeech/t2s/modules/predictor/variance_predictor.py

103 lines
3.4 KiB

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