fix transformer_tts' stop condition
This commit is contained in:
chenfeiyu
parent
e87bfb7d05
commit
c57e8e7350
|
|
@ -12,10 +12,10 @@
|
||||||
# See the License for the specific language governing permissions and
|
# See the License for the specific language governing permissions and
|
||||||
# limitations under the License.
|
# limitations under the License.
|
||||||
|
|
||||||
from parakeet.models.clarinet import *
|
#from parakeet.models.clarinet import *
|
||||||
from parakeet.models.waveflow import *
|
from parakeet.models.waveflow import *
|
||||||
from parakeet.models.wavenet import *
|
#from parakeet.models.wavenet import *
|
||||||
|
|
||||||
from parakeet.models.transformer_tts import *
|
from parakeet.models.transformer_tts import *
|
||||||
from parakeet.models.deepvoice3 import *
|
#from parakeet.models.deepvoice3 import *
|
||||||
# from parakeet.models.fastspeech import *
|
# from parakeet.models.fastspeech import *
|
||||||
|
|
|
||||||
|
|
@ -491,7 +491,7 @@ class TransformerTTS(nn.Layer):
|
||||||
decoder_output = paddle.concat([decoder_output, mel_output[:, -self.r:, :]], 1)
|
decoder_output = paddle.concat([decoder_output, mel_output[:, -self.r:, :]], 1)
|
||||||
|
|
||||||
# stop condition: (if any ouput frame of the output multiframes hits the stop condition)
|
# stop condition: (if any ouput frame of the output multiframes hits the stop condition)
|
||||||
if paddle.any(paddle.argmax(stop_logits[0, :, :], axis=-1) == self.stop_prob_index):
|
if paddle.any(paddle.argmax(stop_logits[0, -self.r:, :], axis=-1) == self.stop_prob_index):
|
||||||
if verbose:
|
if verbose:
|
||||||
print("Hits stop condition.")
|
print("Hits stop condition.")
|
||||||
break
|
break
|
||||||
|
|
|
||||||
|
|
@ -1,3 +1,5 @@
|
||||||
|
import numba
|
||||||
|
import numpy as np
|
||||||
import paddle
|
import paddle
|
||||||
from paddle import nn
|
from paddle import nn
|
||||||
from paddle.nn import functional as F
|
from paddle.nn import functional as F
|
||||||
|
|
@ -22,3 +24,32 @@ def masked_l1_loss(prediction, target, mask):
|
||||||
def masked_softmax_with_cross_entropy(logits, label, mask, axis=-1):
|
def masked_softmax_with_cross_entropy(logits, label, mask, axis=-1):
|
||||||
ce = F.softmax_with_cross_entropy(logits, label, axis=axis)
|
ce = F.softmax_with_cross_entropy(logits, label, axis=axis)
|
||||||
return weighted_mean(ce, mask)
|
return weighted_mean(ce, mask)
|
||||||
|
|
||||||
|
def diagonal_loss(attentions, input_lengths, target_lengths, g=0.2, multihead=False):
|
||||||
|
"""A metric to evaluate how diagonal a attention distribution is."""
|
||||||
|
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
|
||||||
|
|
@ -1,202 +0,0 @@
|
||||||
# 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 numpy as np
|
|
||||||
import paddle.fluid as fluid
|
|
||||||
import paddle.fluid.dygraph as dg
|
|
||||||
import paddle.fluid.layers as layers
|
|
||||||
|
|
||||||
|
|
||||||
class Linear(dg.Layer):
|
|
||||||
def __init__(self,
|
|
||||||
in_features,
|
|
||||||
out_features,
|
|
||||||
is_bias=True,
|
|
||||||
dtype="float32"):
|
|
||||||
super(Linear, self).__init__()
|
|
||||||
self.in_features = in_features
|
|
||||||
self.out_features = out_features
|
|
||||||
self.dtype = dtype
|
|
||||||
self.weight = fluid.ParamAttr(
|
|
||||||
initializer=fluid.initializer.XavierInitializer())
|
|
||||||
self.bias = is_bias
|
|
||||||
|
|
||||||
if is_bias is not False:
|
|
||||||
k = math.sqrt(1.0 / in_features)
|
|
||||||
self.bias = fluid.ParamAttr(initializer=fluid.initializer.Uniform(
|
|
||||||
low=-k, high=k))
|
|
||||||
|
|
||||||
self.linear = dg.Linear(
|
|
||||||
in_features,
|
|
||||||
out_features,
|
|
||||||
param_attr=self.weight,
|
|
||||||
bias_attr=self.bias, )
|
|
||||||
|
|
||||||
def forward(self, x):
|
|
||||||
x = self.linear(x)
|
|
||||||
return x
|
|
||||||
|
|
||||||
|
|
||||||
class ScaledDotProductAttention(dg.Layer):
|
|
||||||
def __init__(self, d_key):
|
|
||||||
"""Scaled dot product attention module.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
d_key (int): the dim of key in multihead attention.
|
|
||||||
"""
|
|
||||||
super(ScaledDotProductAttention, self).__init__()
|
|
||||||
|
|
||||||
self.d_key = d_key
|
|
||||||
|
|
||||||
# please attention this mask is diff from pytorch
|
|
||||||
def forward(self,
|
|
||||||
key,
|
|
||||||
value,
|
|
||||||
query,
|
|
||||||
mask=None,
|
|
||||||
query_mask=None,
|
|
||||||
dropout=0.1):
|
|
||||||
"""
|
|
||||||
Compute scaled dot product attention.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
key (Variable): shape(B, T, C), dtype float32, the input key of scaled dot product attention.
|
|
||||||
value (Variable): shape(B, T, C), dtype float32, the input value of scaled dot product attention.
|
|
||||||
query (Variable): shape(B, T, C), dtype float32, the input query of scaled dot product attention.
|
|
||||||
mask (Variable, optional): shape(B, T_q, T_k), dtype float32, the mask of key. Defaults to None.
|
|
||||||
query_mask (Variable, optional): shape(B, T_q, T_q), dtype float32, the mask of query. Defaults to None.
|
|
||||||
dropout (float32, optional): the probability of dropout. Defaults to 0.1.
|
|
||||||
Returns:
|
|
||||||
result (Variable): shape(B, T, C), the result of mutihead attention.
|
|
||||||
attention (Variable): shape(n_head * B, T, C), the attention of key.
|
|
||||||
"""
|
|
||||||
# Compute attention score
|
|
||||||
attention = layers.matmul(
|
|
||||||
query, key, transpose_y=True, alpha=self.d_key
|
|
||||||
**-0.5) #transpose the last dim in y
|
|
||||||
|
|
||||||
# Mask key to ignore padding
|
|
||||||
if mask is not None:
|
|
||||||
attention = attention + mask
|
|
||||||
attention = layers.softmax(attention, use_cudnn=True)
|
|
||||||
attention = layers.dropout(
|
|
||||||
attention, dropout, dropout_implementation='upscale_in_train')
|
|
||||||
|
|
||||||
# Mask query to ignore padding
|
|
||||||
if query_mask is not None:
|
|
||||||
attention = attention * query_mask
|
|
||||||
|
|
||||||
result = layers.matmul(attention, value)
|
|
||||||
return result, attention
|
|
||||||
|
|
||||||
|
|
||||||
class MultiheadAttention(dg.Layer):
|
|
||||||
def __init__(self,
|
|
||||||
num_hidden,
|
|
||||||
d_k,
|
|
||||||
d_q,
|
|
||||||
num_head=4,
|
|
||||||
is_bias=False,
|
|
||||||
dropout=0.1,
|
|
||||||
is_concat=True):
|
|
||||||
"""Multihead Attention.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
num_hidden (int): the number of hidden layer in network.
|
|
||||||
d_k (int): the dim of key in multihead attention.
|
|
||||||
d_q (int): the dim of query in multihead attention.
|
|
||||||
num_head (int, optional): the head number of multihead attention. Defaults to 4.
|
|
||||||
is_bias (bool, optional): whether have bias in linear layers. Default to False.
|
|
||||||
dropout (float, optional): dropout probability of FFTBlock. Defaults to 0.1.
|
|
||||||
is_concat (bool, optional): whether concat query and result. Default to True.
|
|
||||||
"""
|
|
||||||
super(MultiheadAttention, self).__init__()
|
|
||||||
self.num_hidden = num_hidden
|
|
||||||
self.num_head = num_head
|
|
||||||
self.d_k = d_k
|
|
||||||
self.d_q = d_q
|
|
||||||
self.dropout = dropout
|
|
||||||
self.is_concat = is_concat
|
|
||||||
|
|
||||||
self.key = Linear(num_hidden, num_head * d_k, is_bias=is_bias)
|
|
||||||
self.value = Linear(num_hidden, num_head * d_k, is_bias=is_bias)
|
|
||||||
self.query = Linear(num_hidden, num_head * d_q, is_bias=is_bias)
|
|
||||||
|
|
||||||
self.scal_attn = ScaledDotProductAttention(d_k)
|
|
||||||
|
|
||||||
if self.is_concat:
|
|
||||||
self.fc = Linear(num_head * d_q * 2, num_hidden)
|
|
||||||
else:
|
|
||||||
self.fc = Linear(num_head * d_q, num_hidden)
|
|
||||||
|
|
||||||
self.layer_norm = dg.LayerNorm(num_hidden)
|
|
||||||
|
|
||||||
def forward(self, key, value, query_input, mask=None, query_mask=None):
|
|
||||||
"""
|
|
||||||
Compute attention.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
key (Variable): shape(B, T, C), dtype float32, the input key of attention.
|
|
||||||
value (Variable): shape(B, T, C), dtype float32, the input value of attention.
|
|
||||||
query_input (Variable): shape(B, T, C), dtype float32, the input query of attention.
|
|
||||||
mask (Variable, optional): shape(B, T_query, T_key), dtype float32, the mask of key. Defaults to None.
|
|
||||||
query_mask (Variable, optional): shape(B, T_query, T_key), dtype float32, the mask of query. Defaults to None.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
result (Variable): shape(B, T, C), the result of mutihead attention.
|
|
||||||
attention (Variable): shape(num_head * B, T, C), the attention of key and query.
|
|
||||||
"""
|
|
||||||
|
|
||||||
batch_size = key.shape[0]
|
|
||||||
seq_len_key = key.shape[1]
|
|
||||||
seq_len_query = query_input.shape[1]
|
|
||||||
|
|
||||||
# Make multihead attention
|
|
||||||
key = layers.reshape(
|
|
||||||
self.key(key), [batch_size, seq_len_key, self.num_head, self.d_k])
|
|
||||||
value = layers.reshape(
|
|
||||||
self.value(value),
|
|
||||||
[batch_size, seq_len_key, self.num_head, self.d_k])
|
|
||||||
query = layers.reshape(
|
|
||||||
self.query(query_input),
|
|
||||||
[batch_size, seq_len_query, self.num_head, self.d_q])
|
|
||||||
|
|
||||||
key = layers.reshape(
|
|
||||||
layers.transpose(key, [2, 0, 1, 3]), [-1, seq_len_key, self.d_k])
|
|
||||||
value = layers.reshape(
|
|
||||||
layers.transpose(value, [2, 0, 1, 3]),
|
|
||||||
[-1, seq_len_key, self.d_k])
|
|
||||||
query = layers.reshape(
|
|
||||||
layers.transpose(query, [2, 0, 1, 3]),
|
|
||||||
[-1, seq_len_query, self.d_q])
|
|
||||||
|
|
||||||
result, attention = self.scal_attn(
|
|
||||||
key, value, query, mask=mask, query_mask=query_mask)
|
|
||||||
|
|
||||||
# concat all multihead result
|
|
||||||
result = layers.reshape(
|
|
||||||
result, [self.num_head, batch_size, seq_len_query, self.d_q])
|
|
||||||
result = layers.reshape(
|
|
||||||
layers.transpose(result, [1, 2, 0, 3]),
|
|
||||||
[batch_size, seq_len_query, -1])
|
|
||||||
if self.is_concat:
|
|
||||||
result = layers.concat([query_input, result], axis=-1)
|
|
||||||
result = layers.dropout(
|
|
||||||
self.fc(result),
|
|
||||||
self.dropout,
|
|
||||||
dropout_implementation='upscale_in_train')
|
|
||||||
result = result + query_input
|
|
||||||
|
|
||||||
result = self.layer_norm(result)
|
|
||||||
return result, attention
|
|
||||||
Loading…
Reference in New Issue