# Copyright (c) 2020 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.
import math
import paddle
from paddle import nn
from paddle.fluid.layers import sequence_mask
from paddle.nn import functional as F
from paddle.nn import initializer as I
from tqdm import trange
from paddlespeech.t2s.modules.attention import LocationSensitiveAttention
from paddlespeech.t2s.modules.conv import Conv1dBatchNorm
from paddlespeech.t2s.modules.losses import guided_attention_loss
from paddlespeech.t2s.utils import checkpoint
__all__ = ["Tacotron2", "Tacotron2Loss"]
class DecoderPreNet(nn.Layer):
"""Decoder prenet module for Tacotron2.
Parameters
----------
d_input: int
The input feature size.
d_hidden: int
The hidden size.
d_output: int
The output feature size.
dropout_rate: float
The droput probability.
"""
def __init__(self,
d_input: int,
d_hidden: int,
d_output: int,
dropout_rate: float):
super().__init__()
self.dropout_rate = dropout_rate
self.linear1 = nn.Linear(d_input, d_hidden, bias_attr=False)
self.linear2 = nn.Linear(d_hidden, d_output, bias_attr=False)
def forward(self, x):
"""Calculate forward propagation.
Parameters
----------
x: Tensor [shape=(B, T_mel, C)]
Batch of the sequences of padded mel spectrogram.
Returns
-------
output: Tensor [shape=(B, T_mel, C)]
Batch of the sequences of padded hidden state.
"""
x = F.dropout(F.relu(self.linear1(x)), self.dropout_rate, training=True)
output = F.dropout(
F.relu(self.linear2(x)), self.dropout_rate, training=True)
return output
class DecoderPostNet(nn.Layer):
"""Decoder postnet module for Tacotron2.
Parameters
----------
d_mels: int
The number of mel bands.
d_hidden: int
The hidden size of postnet.
kernel_size: int
The kernel size of the conv layer in postnet.
num_layers: int
The number of conv layers in postnet.
dropout: float
The droput probability.
"""
def __init__(self,
d_mels: int,
d_hidden: int,
kernel_size: int,
num_layers: int,
dropout: float):
super().__init__()
self.dropout = dropout
self.num_layers = num_layers
padding = int((kernel_size - 1) / 2)
self.conv_batchnorms = nn.LayerList()
k = math.sqrt(1.0 / (d_mels * kernel_size))
self.conv_batchnorms.append(
Conv1dBatchNorm(
d_mels,
d_hidden,
kernel_size=kernel_size,
padding=padding,
bias_attr=I.Uniform(-k, k),
data_format='NLC'))
k = math.sqrt(1.0 / (d_hidden * kernel_size))
self.conv_batchnorms.extend([
Conv1dBatchNorm(
d_hidden,
d_hidden,
kernel_size=kernel_size,
padding=padding,
bias_attr=I.Uniform(-k, k),
data_format='NLC') for i in range(1, num_layers - 1)
])
self.conv_batchnorms.append(
Conv1dBatchNorm(
d_hidden,
d_mels,
kernel_size=kernel_size,
padding=padding,
bias_attr=I.Uniform(-k, k),
data_format='NLC'))
def forward(self, x):
"""Calculate forward propagation.
Parameters
----------
x: Tensor [shape=(B, T_mel, C)]
Output sequence of features from decoder.
Returns
-------
output: Tensor [shape=(B, T_mel, C)]
Output sequence of features after postnet.
"""
for i in range(len(self.conv_batchnorms) - 1):
x = F.dropout(
F.tanh(self.conv_batchnorms[i](x)),
self.dropout,
training=self.training)
output = F.dropout(
self.conv_batchnorms[self.num_layers - 1](x),
self.dropout,
training=self.training)
return output
class Tacotron2Encoder(nn.Layer):
"""Tacotron2 encoder module for Tacotron2.
Parameters
----------
d_hidden: int
The hidden size in encoder module.
conv_layers: int
The number of conv layers.
kernel_size: int
The kernel size of conv layers.
p_dropout: float
The droput probability.
"""
def __init__(self,
d_hidden: int,
conv_layers: int,
kernel_size: int,
p_dropout: float):
super().__init__()
k = math.sqrt(1.0 / (d_hidden * kernel_size))
self.conv_batchnorms = paddle.nn.LayerList([
Conv1dBatchNorm(
d_hidden,
d_hidden,
kernel_size,
stride=1,
padding=int((kernel_size - 1) / 2),
bias_attr=I.Uniform(-k, k),
data_format='NLC') for i in range(conv_layers)
])
self.p_dropout = p_dropout
self.hidden_size = int(d_hidden / 2)
self.lstm = nn.LSTM(
d_hidden, self.hidden_size, direction="bidirectional")
def forward(self, x, input_lens=None):
"""Calculate forward propagation of tacotron2 encoder.
Parameters
----------
x: Tensor [shape=(B, T, C)]
Input embeddings.
text_lens: Tensor [shape=(B,)], optional
Batch of lengths of each text input batch. Defaults to None.
Returns
-------
output : Tensor [shape=(B, T, C)]
Batch of the sequences of padded hidden states.
"""
for conv_batchnorm in self.conv_batchnorms:
x = F.dropout(
F.relu(conv_batchnorm(x)),
self.p_dropout,
training=self.training)
output, _ = self.lstm(inputs=x, sequence_length=input_lens)
return output
class Tacotron2Decoder(nn.Layer):
"""Tacotron2 decoder module for Tacotron2.
Parameters
----------
d_mels: int
The number of mel bands.
reduction_factor: int
The reduction factor of tacotron.
d_encoder: int
The hidden size of encoder.
d_prenet: int
The hidden size in decoder prenet.
d_attention_rnn: int
The attention rnn layer hidden size.
d_decoder_rnn: int
The decoder rnn layer hidden size.
d_attention: int
The hidden size of the linear layer in location sensitive attention.
attention_filters: int
The filter size of the conv layer in location sensitive attention.
attention_kernel_size: int
The kernel size of the conv layer in location sensitive attention.
p_prenet_dropout: float
The droput probability in decoder prenet.
p_attention_dropout: float
The droput probability in location sensitive attention.
p_decoder_dropout: float
The droput probability in decoder.
use_stop_token: bool
Whether to use a binary classifier for stop token prediction.
Defaults to False
"""
def __init__(self,
d_mels: int,
reduction_factor: int,
d_encoder: int,
d_prenet: int,
d_attention_rnn: int,
d_decoder_rnn: int,
d_attention: int,
attention_filters: int,
attention_kernel_size: int,
p_prenet_dropout: float,
p_attention_dropout: float,
p_decoder_dropout: float,
use_stop_token: bool=False):
super().__init__()
self.d_mels = d_mels
self.reduction_factor = reduction_factor
self.d_encoder = d_encoder
self.d_attention_rnn = d_attention_rnn
self.d_decoder_rnn = d_decoder_rnn
self.p_attention_dropout = p_attention_dropout
self.p_decoder_dropout = p_decoder_dropout
self.prenet = DecoderPreNet(
d_mels * reduction_factor,
d_prenet,
d_prenet,
dropout_rate=p_prenet_dropout)
# attention_rnn takes attention's context vector has an
# auxiliary input
self.attention_rnn = nn.LSTMCell(d_prenet + d_encoder, d_attention_rnn)
self.attention_layer = LocationSensitiveAttention(
d_attention_rnn, d_encoder, d_attention, attention_filters,
attention_kernel_size)
# decoder_rnn takes prenet's output and attention_rnn's input
# as input
self.decoder_rnn = nn.LSTMCell(d_attention_rnn + d_encoder,
d_decoder_rnn)
self.linear_projection = nn.Linear(d_decoder_rnn + d_encoder,
d_mels * reduction_factor)
self.use_stop_token = use_stop_token
if use_stop_token:
self.stop_layer = nn.Linear(d_decoder_rnn + d_encoder, 1)
# states - temporary attributes
self.attention_hidden = None
self.attention_cell = None
self.decoder_hidden = None
self.decoder_cell = None
self.attention_weights = None
self.attention_weights_cum = None
self.attention_context = None
self.key = None
self.mask = None
self.processed_key = None
def _initialize_decoder_states(self, key):
"""init states be used in decoder
"""
batch_size, encoder_steps, _ = key.shape
self.attention_hidden = paddle.zeros(
shape=[batch_size, self.d_attention_rnn], dtype=key.dtype)
self.attention_cell = paddle.zeros(
shape=[batch_size, self.d_attention_rnn], dtype=key.dtype)
self.decoder_hidden = paddle.zeros(
shape=[batch_size, self.d_decoder_rnn], dtype=key.dtype)
self.decoder_cell = paddle.zeros(
shape=[batch_size, self.d_decoder_rnn], dtype=key.dtype)
self.attention_weights = paddle.zeros(
shape=[batch_size, encoder_steps], dtype=key.dtype)
self.attention_weights_cum = paddle.zeros(
shape=[batch_size, encoder_steps], dtype=key.dtype)
self.attention_context = paddle.zeros(
shape=[batch_size, self.d_encoder], dtype=key.dtype)
self.key = key # [B, T, C]
# pre-compute projected keys to improve efficiency
self.processed_key = self.attention_layer.key_layer(key) # [B, T, C]
def _decode(self, query):
"""decode one time step
"""
cell_input = paddle.concat([query, self.attention_context], axis=-1)
# The first lstm layer (or spec encoder lstm)
_, (self.attention_hidden, self.attention_cell) = self.attention_rnn(
cell_input, (self.attention_hidden, self.attention_cell))
self.attention_hidden = F.dropout(
self.attention_hidden,
self.p_attention_dropout,
training=self.training)
# Loaction sensitive attention
attention_weights_cat = paddle.stack(
[self.attention_weights, self.attention_weights_cum], axis=-1)
self.attention_context, self.attention_weights = self.attention_layer(
self.attention_hidden, self.processed_key, self.key,
attention_weights_cat, self.mask)
self.attention_weights_cum += self.attention_weights
# The second lstm layer (or spec decoder lstm)
decoder_input = paddle.concat(
[self.attention_hidden, self.attention_context], axis=-1)
_, (self.decoder_hidden, self.decoder_cell) = self.decoder_rnn(
decoder_input, (self.decoder_hidden, self.decoder_cell))
self.decoder_hidden = F.dropout(
self.decoder_hidden,
p=self.p_decoder_dropout,
training=self.training)
# decode output one step
decoder_hidden_attention_context = paddle.concat(
[self.decoder_hidden, self.attention_context], axis=-1)
decoder_output = self.linear_projection(
decoder_hidden_attention_context)
if self.use_stop_token:
stop_logit = self.stop_layer(decoder_hidden_attention_context)
return decoder_output, self.attention_weights, stop_logit
return decoder_output, self.attention_weights
def forward(self, keys, querys, mask):
"""Calculate forward propagation of tacotron2 decoder.
Parameters
----------
keys: Tensor[shape=(B, T_key, C)]
Batch of the sequences of padded output from encoder.
querys: Tensor[shape(B, T_query, C)]
Batch of the sequences of padded mel spectrogram.
mask: Tensor
Mask generated with text length. Shape should be (B, T_key, 1).
Returns
-------
mel_output: Tensor [shape=(B, T_query, C)]
Output sequence of features.
alignments: Tensor [shape=(B, T_query, T_key)]
Attention weights.
"""
self._initialize_decoder_states(keys)
self.mask = mask
querys = paddle.reshape(
querys,
[querys.shape[0], querys.shape[1] // self.reduction_factor, -1])
start_step = paddle.zeros(
shape=[querys.shape[0], 1, querys.shape[-1]], dtype=querys.dtype)
querys = paddle.concat([start_step, querys], axis=1)
querys = self.prenet(querys)
mel_outputs, alignments = [], []
stop_logits = []
# Ignore the last time step
while len(mel_outputs) < querys.shape[1] - 1:
query = querys[:, len(mel_outputs), :]
if self.use_stop_token:
mel_output, attention_weights, stop_logit = self._decode(query)
else:
mel_output, attention_weights = self._decode(query)
mel_outputs.append(mel_output)
alignments.append(attention_weights)
if self.use_stop_token:
stop_logits.append(stop_logit)
alignments = paddle.stack(alignments, axis=1)
mel_outputs = paddle.stack(mel_outputs, axis=1)
if self.use_stop_token:
stop_logits = paddle.concat(stop_logits, axis=1)
return mel_outputs, alignments, stop_logits
return mel_outputs, alignments
def infer(self, key, max_decoder_steps=1000):
"""Calculate forward propagation of tacotron2 decoder.
Parameters
----------
keys: Tensor [shape=(B, T_key, C)]
Batch of the sequences of padded output from encoder.
max_decoder_steps: int, optional
Number of max step when synthesize. Defaults to 1000.
Returns
-------
mel_output: Tensor [shape=(B, T_mel, C)]
Output sequence of features.
alignments: Tensor [shape=(B, T_mel, T_key)]
Attention weights.
"""
self._initialize_decoder_states(key)
self.mask = None # mask is not needed for single instance inference
encoder_steps = key.shape[1]
# [B, C]
start_step = paddle.zeros(
shape=[key.shape[0], self.d_mels * self.reduction_factor],
dtype=key.dtype)
query = start_step # [B, C]
first_hit_end = None
mel_outputs, alignments = [], []
stop_logits = []
for i in trange(max_decoder_steps):
query = self.prenet(query)
if self.use_stop_token:
mel_output, alignment, stop_logit = self._decode(query)
else:
mel_output, alignment = self._decode(query)
mel_outputs.append(mel_output)
alignments.append(alignment) # (B=1, T)
if self.use_stop_token:
stop_logits.append(stop_logit)
if self.use_stop_token:
if F.sigmoid(stop_logit) > 0.5:
print("hit stop condition!")
break
else:
if int(paddle.argmax(alignment[0])) == encoder_steps - 1:
if first_hit_end is None:
first_hit_end = i
elif i > (first_hit_end + 20):
print("content exhausted!")
break
if len(mel_outputs) == max_decoder_steps:
print("Warning! Reached max decoder steps!!!")
break
query = mel_output
alignments = paddle.stack(alignments, axis=1)
mel_outputs = paddle.stack(mel_outputs, axis=1)
if self.use_stop_token:
stop_logits = paddle.concat(stop_logits, axis=1)
return mel_outputs, alignments, stop_logits
return mel_outputs, alignments
class Tacotron2(nn.Layer):
"""Tacotron2 model for end-to-end text-to-speech (E2E-TTS).
This is a model of Spectrogram prediction network in Tacotron2 described
in `Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram
Predictions <https://arxiv.org/abs/1712.05884>`_,
which converts the sequence of characters
into the sequence of mel spectrogram.
Parameters
----------
vocab_size : int
Vocabulary size of phons of the model.
n_tones: int
Vocabulary size of tones of the model. Defaults to None. If provided,
the model has an extra tone embedding.
d_mels: int
Number of mel bands.
d_encoder: int
Hidden size in encoder module.
encoder_conv_layers: int
Number of conv layers in encoder.
encoder_kernel_size: int
Kernel size of conv layers in encoder.
d_prenet: int
Hidden size in decoder prenet.
d_attention_rnn: int
Attention rnn layer hidden size in decoder.
d_decoder_rnn: int
Decoder rnn layer hidden size in decoder.
attention_filters: int
Filter size of the conv layer in location sensitive attention.
attention_kernel_size: int
Kernel size of the conv layer in location sensitive attention.
d_attention: int
Hidden size of the linear layer in location sensitive attention.
d_postnet: int
Hidden size of postnet.
postnet_kernel_size: int
Kernel size of the conv layer in postnet.
postnet_conv_layers: int
Number of conv layers in postnet.
reduction_factor: int
Reduction factor of tacotron2.
p_encoder_dropout: float
Droput probability in encoder.
p_prenet_dropout: float
Droput probability in decoder prenet.
p_attention_dropout: float
Droput probability in location sensitive attention.
p_decoder_dropout: float
Droput probability in decoder.
p_postnet_dropout: float
Droput probability in postnet.
d_global_condition: int
Feature size of global condition. Defaults to None. If provided, The
model assumes a global condition that is concatenated to the encoder
outputs.
"""
def __init__(self,
vocab_size,
n_tones=None,
d_mels: int=80,
d_encoder: int=512,
encoder_conv_layers: int=3,
encoder_kernel_size: int=5,
d_prenet: int=256,
d_attention_rnn: int=1024,
d_decoder_rnn: int=1024,
attention_filters: int=32,
attention_kernel_size: int=31,
d_attention: int=128,
d_postnet: int=512,
postnet_kernel_size: int=5,
postnet_conv_layers: int=5,
reduction_factor: int=1,
p_encoder_dropout: float=0.5,
p_prenet_dropout: float=0.5,
p_attention_dropout: float=0.1,
p_decoder_dropout: float=0.1,
p_postnet_dropout: float=0.5,
d_global_condition=None,
use_stop_token=False):
super().__init__()
std = math.sqrt(2.0 / (vocab_size + d_encoder))
val = math.sqrt(3.0) * std # uniform bounds for std
self.embedding = nn.Embedding(
vocab_size, d_encoder, weight_attr=I.Uniform(-val, val))
if n_tones:
self.embedding_tones = nn.Embedding(
n_tones,
d_encoder,
padding_idx=0,
weight_attr=I.Uniform(-0.1 * val, 0.1 * val))
self.toned = n_tones is not None
self.encoder = Tacotron2Encoder(d_encoder, encoder_conv_layers,
encoder_kernel_size, p_encoder_dropout)
# input augmentation scheme: concat global condition to the encoder output
if d_global_condition is not None:
d_encoder += d_global_condition
self.decoder = Tacotron2Decoder(
d_mels,
reduction_factor,
d_encoder,
d_prenet,
d_attention_rnn,
d_decoder_rnn,
d_attention,
attention_filters,
attention_kernel_size,
p_prenet_dropout,
p_attention_dropout,
p_decoder_dropout,
use_stop_token=use_stop_token)
self.postnet = DecoderPostNet(
d_mels=d_mels * reduction_factor,
d_hidden=d_postnet,
kernel_size=postnet_kernel_size,
num_layers=postnet_conv_layers,
dropout=p_postnet_dropout)
def forward(self,
text_inputs,
text_lens,
mels,
output_lens=None,
tones=None,
global_condition=None):
"""Calculate forward propagation of tacotron2.
Parameters
----------
text_inputs: Tensor [shape=(B, T_text)]
Batch of the sequencees of padded character ids.
text_lens: Tensor [shape=(B,)]
Batch of lengths of each text input batch.
mels: Tensor [shape(B, T_mel, C)]
Batch of the sequences of padded mel spectrogram.
output_lens: Tensor [shape=(B,)], optional
Batch of lengths of each mels batch. Defaults to None.
tones: Tensor [shape=(B, T_text)]
Batch of sequences of padded tone ids.
global_condition: Tensor [shape(B, C)]
Batch of global conditions. Defaults to None. If the
`d_global_condition` of the model is not None, this input should be
provided.
use_stop_token: bool
Whether to include a binary classifier to predict the stop token.
Defaults to False.
Returns
-------
outputs : Dict[str, Tensor]
mel_output: output sequence of features (B, T_mel, C);
mel_outputs_postnet: output sequence of features after postnet (B, T_mel, C);
alignments: attention weights (B, T_mel, T_text);
stop_logits: output sequence of stop logits (B, T_mel)
"""
# input of embedding must be int64
text_inputs = paddle.cast(text_inputs, 'int64')
embedded_inputs = self.embedding(text_inputs)
if self.toned:
embedded_inputs += self.embedding_tones(tones)
encoder_outputs = self.encoder(embedded_inputs, text_lens)
if global_condition is not None:
global_condition = global_condition.unsqueeze(1)
global_condition = paddle.expand(global_condition,
[-1, encoder_outputs.shape[1], -1])
encoder_outputs = paddle.concat([encoder_outputs, global_condition],
-1)
# [B, T_enc, 1]
mask = sequence_mask(
text_lens, dtype=encoder_outputs.dtype).unsqueeze(-1)
if self.decoder.use_stop_token:
mel_outputs, alignments, stop_logits = self.decoder(
encoder_outputs, mels, mask=mask)
else:
mel_outputs, alignments = self.decoder(
encoder_outputs, mels, mask=mask)
mel_outputs_postnet = self.postnet(mel_outputs)
mel_outputs_postnet = mel_outputs + mel_outputs_postnet
if output_lens is not None:
# [B, T_dec, 1]
mask = sequence_mask(output_lens).unsqueeze(-1)
mel_outputs = mel_outputs * mask # [B, T, C]
mel_outputs_postnet = mel_outputs_postnet * mask # [B, T, C]
outputs = {
"mel_output": mel_outputs,
"mel_outputs_postnet": mel_outputs_postnet,
"alignments": alignments
}
if self.decoder.use_stop_token:
outputs["stop_logits"] = stop_logits
return outputs
@paddle.no_grad()
def infer(self,
text_inputs,
max_decoder_steps=1000,
tones=None,
global_condition=None):
"""Generate the mel sepctrogram of features given the sequences of character ids.
Parameters
----------
text_inputs: Tensor [shape=(B, T_text)]
Batch of the sequencees of padded character ids.
max_decoder_steps: int, optional
Number of max step when synthesize. Defaults to 1000.
Returns
-------
outputs : Dict[str, Tensor]
mel_output: output sequence of sepctrogram (B, T_mel, C);
mel_outputs_postnet: output sequence of sepctrogram after postnet (B, T_mel, C);
stop_logits: output sequence of stop logits (B, T_mel);
alignments: attention weights (B, T_mel, T_text). This key is only
present when `use_stop_token` is True.
"""
# input of embedding must be int64
text_inputs = paddle.cast(text_inputs, 'int64')
embedded_inputs = self.embedding(text_inputs)
if self.toned:
embedded_inputs += self.embedding_tones(tones)
encoder_outputs = self.encoder(embedded_inputs)
if global_condition is not None:
global_condition = global_condition.unsqueeze(1)
global_condition = paddle.expand(global_condition,
[-1, encoder_outputs.shape[1], -1])
encoder_outputs = paddle.concat([encoder_outputs, global_condition],
-1)
if self.decoder.use_stop_token:
mel_outputs, alignments, stop_logits = self.decoder.infer(
encoder_outputs, max_decoder_steps=max_decoder_steps)
else:
mel_outputs, alignments = self.decoder.infer(
encoder_outputs, max_decoder_steps=max_decoder_steps)
mel_outputs_postnet = self.postnet(mel_outputs)
mel_outputs_postnet = mel_outputs + mel_outputs_postnet
outputs = {
"mel_output": mel_outputs,
"mel_outputs_postnet": mel_outputs_postnet,
"alignments": alignments
}
if self.decoder.use_stop_token:
outputs["stop_logits"] = stop_logits
return outputs
@classmethod
def from_pretrained(cls, config, checkpoint_path):
"""Build a Tacotron2 model from a pretrained model.
Parameters
----------
config: yacs.config.CfgNode
model configs
checkpoint_path: Path or str
the path of pretrained model checkpoint, without extension name
Returns
-------
ConditionalWaveFlow
The model built from pretrained result.
"""
model = cls(vocab_size=config.model.vocab_size,
n_tones=config.model.n_tones,
d_mels=config.data.n_mels,
d_encoder=config.model.d_encoder,
encoder_conv_layers=config.model.encoder_conv_layers,
encoder_kernel_size=config.model.encoder_kernel_size,
d_prenet=config.model.d_prenet,
d_attention_rnn=config.model.d_attention_rnn,
d_decoder_rnn=config.model.d_decoder_rnn,
attention_filters=config.model.attention_filters,
attention_kernel_size=config.model.attention_kernel_size,
d_attention=config.model.d_attention,
d_postnet=config.model.d_postnet,
postnet_kernel_size=config.model.postnet_kernel_size,
postnet_conv_layers=config.model.postnet_conv_layers,
reduction_factor=config.model.reduction_factor,
p_encoder_dropout=config.model.p_encoder_dropout,
p_prenet_dropout=config.model.p_prenet_dropout,
p_attention_dropout=config.model.p_attention_dropout,
p_decoder_dropout=config.model.p_decoder_dropout,
p_postnet_dropout=config.model.p_postnet_dropout,
d_global_condition=config.model.d_global_condition,
use_stop_token=config.model.use_stop_token)
checkpoint.load_parameters(model, checkpoint_path=checkpoint_path)
return model
class Tacotron2Loss(nn.Layer):
""" Tacotron2 Loss module
"""
def __init__(self,
use_stop_token_loss=True,
use_guided_attention_loss=False,
sigma=0.2):
"""Tacotron 2 Criterion.
Args:
use_stop_token_loss (bool, optional): Whether to use a loss for stop token prediction. Defaults to True.
use_guided_attention_loss (bool, optional): Whether to use a loss for attention weights. Defaults to False.
sigma (float, optional): Hyper-parameter sigma for guided attention loss. Defaults to 0.2.
"""
super().__init__()
self.spec_criterion = nn.MSELoss()
self.use_stop_token_loss = use_stop_token_loss
self.use_guided_attention_loss = use_guided_attention_loss
self.attn_criterion = guided_attention_loss
self.stop_criterion = paddle.nn.BCEWithLogitsLoss()
self.sigma = sigma
def forward(self,
mel_outputs,
mel_outputs_postnet,
mel_targets,
attention_weights=None,
slens=None,
plens=None,
stop_logits=None):
"""Calculate tacotron2 loss.
Parameters
----------
mel_outputs: Tensor [shape=(B, T_mel, C)]
Output mel spectrogram sequence.
mel_outputs_postnet: Tensor [shape(B, T_mel, C)]
Output mel spectrogram sequence after postnet.
mel_targets: Tensor [shape=(B, T_mel, C)]
Target mel spectrogram sequence.
attention_weights: Tensor [shape=(B, T_mel, T_enc)]
Attention weights. This should be provided when
`use_guided_attention_loss` is True.
slens: Tensor [shape=(B,)]
Number of frames of mel spectrograms. This should be provided when
`use_guided_attention_loss` is True.
plens: Tensor [shape=(B, )]
Number of text or phone ids of each utterance. This should be
provided when `use_guided_attention_loss` is True.
stop_logits: Tensor [shape=(B, T_mel)]
Stop logits of each mel spectrogram frame. This should be provided
when `use_stop_token_loss` is True.
Returns
-------
losses : Dict[str, Tensor]
loss: the sum of the other three losses;
mel_loss: MSE loss compute by mel_targets and mel_outputs;
post_mel_loss: MSE loss compute by mel_targets and mel_outputs_postnet;
guided_attn_loss: Guided attention loss for attention weights;
stop_loss: Binary cross entropy loss for stop token prediction.
"""
mel_loss = self.spec_criterion(mel_outputs, mel_targets)
post_mel_loss = self.spec_criterion(mel_outputs_postnet, mel_targets)
total_loss = mel_loss + post_mel_loss
if self.use_guided_attention_loss:
gal_loss = self.attn_criterion(attention_weights, slens, plens,
self.sigma)
total_loss += gal_loss
if self.use_stop_token_loss:
T_dec = mel_targets.shape[1]
stop_labels = F.one_hot(slens - 1, num_classes=T_dec)
stop_token_loss = self.stop_criterion(stop_logits, stop_labels)
total_loss += stop_token_loss
losses = {
"loss": total_loss,
"mel_loss": mel_loss,
"post_mel_loss": post_mel_loss
}
if self.use_guided_attention_loss:
losses["guided_attn_loss"] = gal_loss
if self.use_stop_token_loss:
losses["stop_loss"] = stop_token_loss
return losses