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/duration_predictor.py

169 lines
5.6 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)
"""Duration predictor related modules."""
import paddle
from paddle import nn
from paddlespeech.t2s.modules.layer_norm import LayerNorm
from paddlespeech.t2s.modules.masked_fill import masked_fill
class DurationPredictor(nn.Layer):
"""Duration predictor module.
This is a module of duration predictor described
in `FastSpeech: Fast, Robust and Controllable Text to Speech`_.
The duration predictor predicts a duration of each frame in log domain
from the hidden embeddings of encoder.
.. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
https://arxiv.org/pdf/1905.09263.pdf
Note
----------
The calculation domain of outputs is different
between in `forward` and in `inference`. In `forward`,
the outputs are calculated in log domain but in `inference`,
those are calculated in linear domain.
"""
def __init__(self,
idim,
n_layers=2,
n_chans=384,
kernel_size=3,
dropout_rate=0.1,
offset=1.0):
"""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.
offset (float, optional):
Offset value to avoid nan in log domain.
"""
super().__init__()
self.offset = offset
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, ),
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, x_masks=None, is_inference=False):
# (B, idim, Tmax)
xs = xs.transpose([0, 2, 1])
# (B, C, Tmax)
for f in self.conv:
xs = f(xs)
# NOTE: calculate in log domain
# (B, Tmax)
xs = self.linear(xs.transpose([0, 2, 1])).squeeze(-1)
if is_inference:
# NOTE: calculate in linear domain
xs = paddle.clip(paddle.round(xs.exp() - self.offset), min=0)
if x_masks is not None:
xs = masked_fill(xs, x_masks, 0.0)
return xs
def forward(self, xs, x_masks=None):
"""Calculate forward propagation.
Args:
xs(Tensor):
Batch of input sequences (B, Tmax, idim).
x_masks(ByteTensor, optional, optional):
Batch of masks indicating padded part (B, Tmax). (Default value = None)
Returns:
Tensor: Batch of predicted durations in log domain (B, Tmax).
"""
return self._forward(xs, x_masks, False)
def inference(self, xs, x_masks=None):
"""Inference duration.
Args:
xs(Tensor):
Batch of input sequences (B, Tmax, idim).
x_masks(Tensor(bool), optional, optional):
Batch of masks indicating padded part (B, Tmax). (Default value = None)
Returns:
Tensor: Batch of predicted durations in linear domain int64 (B, Tmax).
"""
return self._forward(xs, x_masks, True)
class DurationPredictorLoss(nn.Layer):
"""Loss function module for duration predictor.
The loss value is Calculated in log domain to make it Gaussian.
"""
def __init__(self, offset=1.0, reduction="mean"):
"""Initilize duration predictor loss module.
Args:
offset (float, optional): Offset value to avoid nan in log domain.
reduction (str): Reduction type in loss calculation.
"""
super().__init__()
self.criterion = nn.MSELoss(reduction=reduction)
self.offset = offset
def forward(self, outputs, targets):
"""Calculate forward propagation.
Args:
outputs(Tensor):
Batch of prediction durations in log domain (B, T)
targets(Tensor):
Batch of groundtruth durations in linear domain (B, T)
Returns:
Tensor: Mean squared error loss value.
Note:
`outputs` is in log domain but `targets` is in linear domain.
"""
# NOTE: outputs is in log domain while targets in linear
targets = paddle.log(targets.cast(dtype='float32') + self.offset)
loss = self.criterion(outputs, targets)
return loss