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refactoring code

This commit is contained in:
iclementine 2021-03-22 21:23:46 +08:00
parent 0aa7088d36
commit 086fbf8e35
5 changed files with 291 additions and 398 deletions
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7
examples/tacotron2/config.py
View File

@ -37,9 +37,9 @@ _C.model = CN(
encoder_kernel_size=5, # kernel size of conv layers in tacotron2 encoder
d_prenet=256, # hidden size of decoder prenet
d_attention_rnn=1024, # hidden size of the first rnn layer in tacotron2 decoder
d_decoder_rnn=1024, #hidden size of the second rnn layer in tacotron2 decoder
d_decoder_rnn=1024, # hidden size of the second rnn layer in tacotron2 decoder
d_attention=128, # hidden size of decoder location linear layer
attention_filters=32, # number of filter in decoder location conv layer
attention_filters=32, # number of filter in decoder location conv layer
attention_kernel_size=31, # kernel size of decoder location conv layer
d_postnet=512, # hidden size of decoder postnet
postnet_kernel_size=5, # kernel size of conv layers in postnet
@ -48,7 +48,8 @@ _C.model = CN(
p_prenet_dropout=0.5, # droput probability in decoder prenet
p_attention_dropout=0.1, # droput probability of first rnn layer in decoder
p_decoder_dropout=0.1, # droput probability of second rnn layer in decoder
p_postnet_dropout=0.5, #droput probability in decoder postnet
p_postnet_dropout=0.5, # droput probability in decoder postnet
guided_attn_loss_sigma=0.2 # sigma in guided attention loss
))
_C.training = CN(

10
examples/tacotron2/train.py
View File

@ -34,14 +34,14 @@ from ljspeech import LJSpeech, LJSpeechCollector
class Experiment(ExperimentBase):
def compute_losses(self, inputs, outputs):
_, mel_targets, _, _, stop_tokens = inputs
_, mel_targets, plens, slens, stop_tokens = inputs
mel_outputs = outputs["mel_output"]
mel_outputs_postnet = outputs["mel_outputs_postnet"]
stop_logits = outputs["stop_logits"]
attention_weight = outputs["alignments"]
losses = self.criterion(mel_outputs, mel_outputs_postnet, stop_logits,
mel_targets, stop_tokens)
losses = self.criterion(mel_outputs, mel_outputs_postnet, mel_targets,
attention_weight, slens, plens)
return losses
def train_batch(self):
@ -145,7 +145,7 @@ class Experiment(ExperimentBase):
weight_decay=paddle.regularizer.L2Decay(
config.training.weight_decay),
grad_clip=grad_clip)
criterion = Tacotron2Loss()
criterion = Tacotron2Loss(config.mode.guided_attn_loss_sigma)
self.model = model
self.optimizer = optimizer
self.criterion = criterion

525
parakeet/models/tacotron2.py
View File

@ -13,14 +13,16 @@
# limitations under the License.
import math
import numpy as np
import paddle
from paddle import nn
from paddle.nn import functional as F
import parakeet
from paddle.nn import initializer as I
from paddle.fluid.layers import sequence_mask
from parakeet.modules.conv import Conv1dBatchNorm
from parakeet.modules.attention import LocationSensitiveAttention
from parakeet.modules import masking
from parakeet.modules.losses import guided_attention_loss
from parakeet.utils import checkpoint
__all__ = ["Tacotron2", "Tacotron2Loss"]
@ -44,11 +46,7 @@ class DecoderPreNet(nn.Layer):
The droput probability.
"""
def __init__(self,
d_input: int,
d_hidden: int,
d_output: int,
def __init__(self, d_input: int, d_hidden: int, d_output: int,
dropout_rate: float):
super().__init__()
@ -63,7 +61,7 @@ class DecoderPreNet(nn.Layer):
----------
x: Tensor [shape=(B, T_mel, C)]
Batch of the sequences of padded mel spectrogram.
Returns
-------
output: Tensor [shape=(B, T_mel, C)]
@ -71,10 +69,12 @@ class DecoderPreNet(nn.Layer):
"""
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)
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
@ -99,13 +99,8 @@ class DecoderPostNet(nn.Layer):
The droput probability.
"""
def __init__(self,
d_mels: int,
d_hidden: int,
kernel_size: int,
num_layers: int,
dropout: float):
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
@ -115,45 +110,40 @@ class DecoderPostNet(nn.Layer):
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=paddle.ParamAttr(initializer=nn.initializer.Uniform(
low=-k, high=k)),
data_format='NLC'))
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=paddle.ParamAttr(initializer=nn.initializer.Uniform(
low=-k, high=k)),
data_format='NLC') for i in range(1, num_layers - 1)
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=paddle.ParamAttr(initializer=nn.initializer.Uniform(
low=-k, high=k)),
data_format='NLC'))
Conv1dBatchNorm(d_hidden,
d_mels,
kernel_size=kernel_size,
padding=padding,
bias_attr=I.Uniform(-k, k),
data_format='NLC'))
def forward(self, input):
def forward(self, x):
"""Calculate forward propagation.
Parameters
----------
input: Tensor [shape=(B, T_mel, C)]
x: Tensor [shape=(B, T_mel, C)]
Output sequence of features from decoder.
Returns
-------
output: Tensor [shape=(B, T_mel, C)]
@ -162,14 +152,12 @@ class DecoderPostNet(nn.Layer):
"""
for i in range(len(self.conv_batchnorms) - 1):
input = F.dropout(
F.tanh(self.conv_batchnorms[i](input)),
self.dropout,
training=self.training)
output = F.dropout(
self.conv_batchnorms[self.num_layers - 1](input),
self.dropout,
training=self.training)
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
@ -180,41 +168,36 @@ class Tacotron2Encoder(nn.Layer):
----------
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,
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=paddle.ParamAttr(initializer=nn.initializer.Uniform(
low=-k, high=k)),
data_format='NLC') for i in range(conv_layers)
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")
self.lstm = nn.LSTM(d_hidden,
self.hidden_size,
direction="bidirectional")
def forward(self, x, input_lens=None):
"""Calculate forward propagation of tacotron2 encoder.
@ -223,10 +206,10 @@ class Tacotron2Encoder(nn.Layer):
----------
x: Tensor [shape=(B, T)]
Batch of the sequencees of padded character ids.
text_lens: Tensor [shape=(B,)], optional
Batch of lengths of each text input batch. Defaults to None.
Returns
-------
output : Tensor [shape=(B, T, C)]
@ -234,10 +217,9 @@ class Tacotron2Encoder(nn.Layer):
"""
for conv_batchnorm in self.conv_batchnorms:
x = F.dropout(
F.relu(conv_batchnorm(x)),
self.p_dropout,
training=self.training)
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
@ -253,7 +235,7 @@ class Tacotron2Decoder(nn.Layer):
reduction_factor: int
The reduction factor of tacotron.
d_encoder: int
The hidden size of encoder.
@ -265,13 +247,13 @@ class Tacotron2Decoder(nn.Layer):
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.
@ -284,20 +266,11 @@ class Tacotron2Decoder(nn.Layer):
p_decoder_dropout: float
The droput probability in decoder.
"""
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):
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):
super().__init__()
self.d_mels = d_mels
self.reduction_factor = reduction_factor
@ -307,28 +280,45 @@ class Tacotron2Decoder(nn.Layer):
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)
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.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 = key.shape[0]
MAX_TIME = key.shape[1]
batch_size, encoder_steps, _ = key.shape
self.attention_hidden = paddle.zeros(
shape=[batch_size, self.d_attention_rnn], dtype=key.dtype)
@ -341,27 +331,27 @@ class Tacotron2Decoder(nn.Layer):
shape=[batch_size, self.d_decoder_rnn], dtype=key.dtype)
self.attention_weights = paddle.zeros(
shape=[batch_size, MAX_TIME], dtype=key.dtype)
shape=[batch_size, encoder_steps], dtype=key.dtype)
self.attention_weights_cum = paddle.zeros(
shape=[batch_size, MAX_TIME], dtype=key.dtype)
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]
self.processed_key = self.attention_layer.key_layer(key) #[B, T, C]
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
# 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)
self.attention_hidden = F.dropout(self.attention_hidden,
self.p_attention_dropout,
training=self.training)
# Loaction sensitive attention
attention_weights_cat = paddle.stack(
@ -371,23 +361,21 @@ class Tacotron2Decoder(nn.Layer):
attention_weights_cat, self.mask)
self.attention_weights_cum += self.attention_weights
# The second lstm layer
# 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)
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)
stop_logit = self.stop_layer(decoder_hidden_attention_context)
return decoder_output, stop_logit, self.attention_weights
return decoder_output, self.attention_weights
def forward(self, keys, querys, mask):
"""Calculate forward propagation of tacotron2 decoder.
@ -396,117 +384,105 @@ class Tacotron2Decoder(nn.Layer):
----------
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, T_query) or broadcastable shape.
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.
stop_logits: Tensor [shape=(B, T_query)]
Output sequence of stop logits.
alignments: Tensor [shape=(B, T_query, T_key)]
Attention weights.
"""
querys = paddle.reshape(
querys,
[querys.shape[0], querys.shape[1] // self.reduction_factor, -1])
querys = paddle.concat(
[
paddle.zeros(
shape=[querys.shape[0], 1, querys.shape[-1]],
dtype=querys.dtype), querys
],
axis=1)
querys = self.prenet(querys)
self._initialize_decoder_states(keys)
self.mask = mask
mel_outputs, stop_logits, alignments = [], [], []
while len(mel_outputs) < querys.shape[
1] - 1: # Ignore the last time step
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 = [], []
# Ignore the last time step
while len(mel_outputs) < querys.shape[1] - 1:
query = querys[:, len(mel_outputs), :]
mel_output, stop_logit, attention_weights = self._decode(query)
mel_outputs += [mel_output]
stop_logits += [stop_logit]
alignments += [attention_weights]
mel_output, attention_weights = self._decode(query)
mel_outputs.append(mel_output)
alignments.append(attention_weights)
alignments = paddle.stack(alignments, axis=1)
stop_logits = paddle.concat(stop_logits, axis=1)
mel_outputs = paddle.stack(mel_outputs, axis=1)
return mel_outputs, stop_logits, alignments
return mel_outputs, alignments
def infer(self, key, stop_threshold=0.5, max_decoder_steps=1000):
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.
stop_threshold: float, optional
Stop synthesize when stop logit is greater than this stop threshold. Defaults to 0.5.
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.
stop_logits: Tensor [shape=(B, T_mel)]
Output sequence of stop logits.
alignments: Tensor [shape=(B, T_mel, T_key)]
Attention weights.
"""
query = paddle.zeros(
shape=[key.shape[0], self.d_mels * self.reduction_factor],
dtype=key.dtype) #[B, C]
encoder_steps = key.shape[1]
self._initialize_decoder_states(key)
self.mask = None
self.mask = None # mask is not needed for single instance inference
mel_outputs, stop_logits, alignments = [], [], []
# [B, C]
start_step = paddle.zeros(
shape=[key.shape[0], self.d_mels * self.reduction_factor],
dtype=key.dtype)
query = start_step # [B, C]
mel_outputs, alignments = [], []
while True:
query = self.prenet(query)
mel_output, stop_logit, alignment = self._decode(query)
mel_output, alignment = self._decode(query)
mel_outputs += [mel_output]
stop_logits += [stop_logit]
alignments += [alignment]
mel_outputs.append(mel_output)
alignments.append(alignment) # (B=1, T)
if F.sigmoid(stop_logit) > stop_threshold:
if int(paddle.argmax(alignment[0])) == encoder_steps - 1:
print("Text content exhausted, synthesize stops.")
break
elif len(mel_outputs) == max_decoder_steps:
if len(mel_outputs) == max_decoder_steps:
print("Warning! Reached max decoder steps!!!")
break
query = mel_output
alignments = paddle.stack(alignments, axis=1)
stop_logits = paddle.concat(stop_logits, axis=1)
mel_outputs = paddle.stack(mel_outputs, axis=1)
return mel_outputs, stop_logits, alignments
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>`_,
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.
@ -517,10 +493,10 @@ class Tacotron2(nn.Layer):
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.
@ -538,7 +514,7 @@ class Tacotron2(nn.Layer):
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.
@ -573,38 +549,34 @@ class Tacotron2(nn.Layer):
Droput probability in postnet.
"""
def __init__(self,
frontend: parakeet.frontend.Phonetics,
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):
vocab_size,
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):
super().__init__()
self.frontend = frontend
std = math.sqrt(2.0 / (self.frontend.vocab_size + d_encoder))
std = math.sqrt(2.0 / (vocab_size + d_encoder))
val = math.sqrt(3.0) * std # uniform bounds for std
self.embedding = nn.Embedding(
self.frontend.vocab_size,
d_encoder,
weight_attr=paddle.ParamAttr(initializer=nn.initializer.Uniform(
low=-val, high=val)))
self.embedding = nn.Embedding(vocab_size,
d_encoder,
weight_attr=I.Uniform(-val, val))
self.encoder = Tacotron2Encoder(d_encoder, encoder_conv_layers,
encoder_kernel_size, p_encoder_dropout)
self.decoder = Tacotron2Decoder(
@ -612,12 +584,11 @@ class Tacotron2(nn.Layer):
d_decoder_rnn, d_attention, attention_filters,
attention_kernel_size, p_prenet_dropout, p_attention_dropout,
p_decoder_dropout)
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)
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, mels, text_lens, output_lens=None):
"""Calculate forward propagation of tacotron2.
@ -626,20 +597,20 @@ class Tacotron2(nn.Layer):
----------
text_inputs: Tensor [shape=(B, T_text)]
Batch of the sequencees of padded character ids.
mels: Tensor [shape(B, T_mel, C)]
Batch of the sequences of padded mel spectrogram.
text_lens: Tensor [shape=(B,)]
Batch of lengths of each text input batch.
output_lens: Tensor [shape=(B,)], optional
Batch of lengths of each mels batch. Defaults to None.
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);
@ -651,47 +622,41 @@ class Tacotron2(nn.Layer):
embedded_inputs = self.embedding(text_inputs)
encoder_outputs = self.encoder(embedded_inputs, text_lens)
mask = paddle.tensor.unsqueeze(
paddle.fluid.layers.sequence_mask(
x=text_lens, dtype=encoder_outputs.dtype), [-1])
mel_outputs, stop_logits, alignments = self.decoder(
encoder_outputs, mels, mask=mask)
# [B, T_enc, 1]
mask = paddle.unsqueeze(
sequence_mask(x=text_lens, dtype=encoder_outputs.dtype), [-1])
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:
mask = paddle.tensor.unsqueeze(
paddle.fluid.layers.sequence_mask(x=output_lens),
[-1]) #[B, T, 1]
mel_outputs = mel_outputs * mask #[B, T, C]
mel_outputs_postnet = mel_outputs_postnet * mask #[B, T, C]
stop_logits = stop_logits * mask[:, :, 0] + (1 - mask[:, :, 0]
) * 1e3 #[B, T]
# [B, T_dec, 1]
mask = paddle.unsqueeze(sequence_mask(x=output_lens), [-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,
"stop_logits": stop_logits,
"alignments": alignments
}
return outputs
@paddle.no_grad()
def infer(self, text_inputs, stop_threshold=0.5, max_decoder_steps=1000):
def infer(self, text_inputs, max_decoder_steps=1000):
"""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.
stop_threshold: float, optional
Stop synthesize when stop logit is greater than this stop threshold. Defaults to 0.5.
max_decoder_steps: int, optional
Number of max step when synthesize. Defaults to 1000.
Returns
-------
outputs : Dict[str, Tensor]
@ -706,10 +671,8 @@ class Tacotron2(nn.Layer):
"""
embedded_inputs = self.embedding(text_inputs)
encoder_outputs = self.encoder(embedded_inputs)
mel_outputs, stop_logits, alignments = self.decoder.infer(
encoder_outputs,
stop_threshold=stop_threshold,
max_decoder_steps=max_decoder_steps)
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
@ -717,62 +680,32 @@ class Tacotron2(nn.Layer):
outputs = {
"mel_output": mel_outputs,
"mel_outputs_postnet": mel_outputs_postnet,
"stop_logits": stop_logits,
"alignments": alignments
}
return outputs
@paddle.no_grad()
def predict(self, text, stop_threshold=0.5, max_decoder_steps=1000):
"""Generate the mel sepctrogram of features given the sequenc of characters.
Parameters
----------
text: str
Sequence of characters.
stop_threshold: float, optional
Stop synthesize when stop logit is greater than this stop threshold. Defaults to 0.5.
max_decoder_steps: int, optional
Number of max step when synthesize. Defaults to 1000.
Returns
-------
outputs : Dict[str, Tensor]
mel_outputs_postnet: output sequence of sepctrogram after postnet (T_mel, C);
alignments: attention weights (T_mel, T_text).
"""
ids = np.asarray(self.frontend(text))
ids = paddle.unsqueeze(paddle.to_tensor(ids, dtype='int64'), [0])
outputs = self.infer(ids, stop_threshold, max_decoder_steps)
return outputs['mel_outputs_postnet'][0].numpy(), outputs[
'alignments'][0].numpy()
@classmethod
def from_pretrained(cls, frontend, config, checkpoint_path):
def from_pretrained(cls, config, checkpoint_path):
"""Build a tacotron2 model from a pretrained model.
Parameters
----------
frontend: parakeet.frontend.Phonetics
Frontend used to preprocess text.
config: yacs.config.CfgNode
Model configs.
checkpoint_path: Path or str
The path of pretrained model checkpoint, without extension name.
Returns
-------
Tacotron2
The model build from pretrined result.
"""
model = cls(frontend,
model = cls(vocab_size=config.model.vocab_size,
d_mels=config.data.d_mels,
d_encoder=config.model.d_encoder,
encoder_conv_layers=config.model.encoder_conv_layers,
@ -800,50 +733,46 @@ class Tacotron2(nn.Layer):
class Tacotron2Loss(nn.Layer):
""" Tacotron2 Loss module
"""
def __init__(self):
def __init__(self, sigma=0.2):
super().__init__()
self.spec_criterion = nn.MSELoss()
self.attn_criterion = guided_attention_loss
self.sigma = sigma
def forward(self, mel_outputs, mel_outputs_postnet, stop_logits,
mel_targets, stop_tokens):
def forward(self, mel_outputs, mel_outputs_postnet, mel_targets,
attention_weights, slens, plens):
"""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.
stop_logits: Tensor [shape=(B, T_mel)]
Output sequence of stop logits befor sigmoid.
mel_targets: Tensor [shape=(B, T_mel, C)]
Target mel spectrogram sequence.
stop_tokens: Tensor [shape=(B,)]
Target stop token.
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;
stop_loss: stop loss computed by stop_logits and stop token.
"""
mel_loss = paddle.nn.MSELoss()(mel_outputs, mel_targets)
post_mel_loss = paddle.nn.MSELoss()(mel_outputs_postnet, mel_targets)
stop_loss = paddle.nn.BCEWithLogitsLoss()(stop_logits, stop_tokens)
total_loss = mel_loss + post_mel_loss + stop_loss
losses = dict(
loss=total_loss,
mel_loss=mel_loss,
post_mel_loss=post_mel_loss,
stop_loss=stop_loss)
mel_loss = self.spec_criterion(mel_outputs, mel_targets)
post_mel_loss = self.spec_criterion(mel_outputs_postnet, mel_targets)
gal_loss = self.attn_criterion(attention_weights, slens, plens,
self.sigma)
total_loss = mel_loss + post_mel_loss + gal_loss
losses = {
"loss": total_loss,
"mel_loss": mel_loss,
"post_mel_loss": post_mel_loss,
"guided_attn_loss": gal_loss
}
return losses

34
parakeet/modules/attention.py
View File

@ -143,9 +143,9 @@ class MonoheadAttention(nn.Layer):
def __init__(self,
model_dim: int,
dropout: float=0.0,
k_dim: int=None,
v_dim: int=None):
dropout: float = 0.0,
k_dim: int = None,
v_dim: int = None):
super(MonoheadAttention, self).__init__()
k_dim = k_dim or model_dim
v_dim = v_dim or model_dim
@ -225,9 +225,9 @@ class MultiheadAttention(nn.Layer):
def __init__(self,
model_dim: int,
num_heads: int,
dropout: float=0.0,
k_dim: int=None,
v_dim: int=None):
dropout: float = 0.0,
k_dim: int = None,
v_dim: int = None):
super(MultiheadAttention, self).__init__()
if model_dim % num_heads != 0:
raise ValueError("model_dim must be divisible by num_heads")
@ -316,14 +316,11 @@ class LocationSensitiveAttention(nn.Layer):
self.key_layer = nn.Linear(d_key, d_attention, bias_attr=False)
self.value = nn.Linear(d_attention, 1, bias_attr=False)
#Location Layer
# Location Layer
self.location_conv = nn.Conv1D(
2,
location_filters,
location_kernel_size,
1,
int((location_kernel_size - 1) / 2),
1,
2, location_filters,
kernel_size=location_kernel_size,
padding=int((location_kernel_size - 1) / 2),
bias_attr=False,
data_format='NLC')
self.location_layer = nn.Linear(
@ -352,21 +349,22 @@ class LocationSensitiveAttention(nn.Layer):
Attention weights concat.
mask : Tensor, optional
The mask. Shape should be (batch_size, times_steps_q, time_steps_k) or broadcastable shape.
The mask. Shape should be (batch_size, times_steps_k, 1).
Defaults to None.
Returns
----------
attention_context : Tensor [shape=(batch_size, time_steps_q, d_attention)]
attention_context : Tensor [shape=(batch_size, d_attention)]
The context vector.
attention_weights : Tensor [shape=(batch_size, times_steps_q, time_steps_k)]
attention_weights : Tensor [shape=(batch_size, time_steps_k)]
The attention weights.
"""
processed_query = self.query_layer(paddle.unsqueeze(query, axis=[1]))
processed_attention_weights = self.location_layer(
self.location_conv(attention_weights_cat))
# (B, T_enc, 1)
alignment = self.value(
paddle.tanh(processed_attention_weights + processed_key +
processed_query))
@ -378,7 +376,7 @@ class LocationSensitiveAttention(nn.Layer):
attention_context = paddle.matmul(
attention_weights, value, transpose_x=True)
attention_weights = paddle.squeeze(attention_weights, axis=[-1])
attention_context = paddle.squeeze(attention_context, axis=[1])
attention_weights = paddle.squeeze(attention_weights, axis=-1)
attention_context = paddle.squeeze(attention_context, axis=1)
return attention_context, attention_weights

113
parakeet/modules/losses.py
View File

@ -17,15 +17,51 @@ import numpy as np
import paddle
from paddle import nn
from paddle.nn import functional as F
from paddle.fluid.layers import sequence_mask
__all__ = [
"guided_attention_loss",
"weighted_mean",
"masked_l1_loss",
"masked_softmax_with_cross_entropy",
"diagonal_loss",
]
def attention_guide(dec_lens, enc_lens, N, T, g, dtype=None):
"""Build that W matrix. shape(B, T_dec, T_enc)
W[i, n, t] = 1 - exp(-(n/dec_lens[i] - t/enc_lens[i])**2 / (2g**2))
See also:
Tachibana, Hideyuki, Katsuya Uenoyama, and Shunsuke Aihara. 2017. “Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention.” ArXiv:1710.08969 [Cs, Eess], October. http://arxiv.org/abs/1710.08969.
"""
dtype = dtype or paddle.get_default_dtype()
dec_pos = paddle.arange(0, N).astype(
dtype) / dec_lens.unsqueeze(-1) # n/N # shape(B, T_dec)
enc_pos = paddle.arange(0, T).astype(
dtype) / enc_lens.unsqueeze(-1) # t/T # shape(B, T_enc)
W = 1 - paddle.exp(-(dec_pos.unsqueeze(-1) -
enc_pos.unsqueeze(1))**2 / (2 * g ** 2))
dec_mask = sequence_mask(dec_lens, maxlen=N)
enc_mask = sequence_mask(enc_lens, maxlen=T)
mask = dec_mask.unsqueeze(-1) * enc_mask.unsqueeze(1)
mask = paddle.cast(mask, W.dtype)
W *= mask
return W
def guided_attention_loss(attention_weight, dec_lens, enc_lens, g):
"""Guided attention loss, masked to excluded padding parts."""
_, N, T = attention_weight.shape
W = attention_guide(dec_lens, enc_lens, N, T, g, attention_weight.dtype)
total_tokens = (dec_lens * enc_lens).astype(W.dtype)
loss = paddle.mean(paddle.sum(
W * attention_weight, [1, 2]) / total_tokens)
return loss, W
def weighted_mean(input, weight):
"""Weighted mean. It can also be used as masked mean.
@ -40,14 +76,10 @@ def weighted_mean(input, weight):
----------
Tensor [shape=(1,)]
Weighted mean tensor with the same dtype as input.
Warnings
---------
This is not a mathematical weighted mean. It performs weighted sum and
simple average.
"""
weight = paddle.cast(weight, input.dtype)
return paddle.mean(input * weight)
broadcast_ratio = input.size / weight.size
return paddle.sum(input * weight) / (paddle.sum(weight) * broadcast_ratio)
def masked_l1_loss(prediction, target, mask):
@ -101,70 +133,3 @@ def masked_softmax_with_cross_entropy(logits, label, mask, axis=-1):
ce = F.softmax_with_cross_entropy(logits, label, axis=axis)
loss = weighted_mean(ce, mask)
return loss
def diagonal_loss(attentions,
input_lengths,
target_lengths,
g=0.2,
multihead=False):
"""A metric to evaluate how diagonal a attention distribution is.
It is computed for batch attention distributions. For each attention
distribution, the valid decoder time steps and encoder time steps may
differ.
Parameters
----------
attentions : Tensor [shape=(B, T_dec, T_enc) or (B, H, T_dec, T_dec)]
The attention weights from an encoder-decoder structure.
input_lengths : Tensor [shape=(B,)]
The valid length for each encoder output.
target_lengths : Tensor [shape=(B,)]
The valid length for each decoder output.
g : float, optional
[description], by default 0.2.
multihead : bool, optional
A flag indicating whether ``attentions`` is a multihead attention's
attention distribution.
If ``True``, the shape of attention is ``(B, H, T_dec, T_dec)``, by
default False.
Returns
-------
Tensor [shape=(1,)]
The diagonal loss.
"""
W = guided_attentions(input_lengths, target_lengths, g)
W_tensor = paddle.to_tensor(W)
if not multihead:
return paddle.mean(attentions * W_tensor)
else:
return paddle.mean(attentions * paddle.unsqueeze(W_tensor, 1))
@numba.jit(nopython=True)
def guided_attention(N, max_N, T, max_T, g):
W = np.zeros((max_T, max_N), dtype=np.float32)
for t in range(T):
for n in range(N):
W[t, n] = 1 - np.exp(-(n / N - t / T)**2 / (2 * g * g))
# (T_dec, T_enc)
return W
def guided_attentions(input_lengths, target_lengths, g=0.2):
B = len(input_lengths)
max_input_len = input_lengths.max()
max_target_len = target_lengths.max()
W = np.zeros((B, max_target_len, max_input_len), dtype=np.float32)
for b in range(B):
W[b] = guided_attention(input_lengths[b], max_input_len,
target_lengths[b], max_target_len, g)
# (B, T_dec, T_enc)
return W