fix transformer_tts' stop condition
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chenfeiyu
parent
e87bfb7d05
commit
c57e8e7350
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@ -12,10 +12,10 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from parakeet.models.clarinet import *
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#from parakeet.models.clarinet import *
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from parakeet.models.waveflow import *
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from parakeet.models.wavenet import *
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#from parakeet.models.wavenet import *
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from parakeet.models.transformer_tts import *
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from parakeet.models.deepvoice3 import *
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#from parakeet.models.deepvoice3 import *
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# from parakeet.models.fastspeech import *
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@ -491,7 +491,7 @@ class TransformerTTS(nn.Layer):
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decoder_output = paddle.concat([decoder_output, mel_output[:, -self.r:, :]], 1)
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# stop condition: (if any ouput frame of the output multiframes hits the stop condition)
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if paddle.any(paddle.argmax(stop_logits[0, :, :], axis=-1) == self.stop_prob_index):
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if paddle.any(paddle.argmax(stop_logits[0, -self.r:, :], axis=-1) == self.stop_prob_index):
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if verbose:
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print("Hits stop condition.")
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break
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@ -1,3 +1,5 @@
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import numba
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import numpy as np
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import paddle
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from paddle import nn
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from paddle.nn import functional as F
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@ -22,3 +24,32 @@ def masked_l1_loss(prediction, target, mask):
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def masked_softmax_with_cross_entropy(logits, label, mask, axis=-1):
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ce = F.softmax_with_cross_entropy(logits, label, axis=axis)
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return weighted_mean(ce, mask)
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def diagonal_loss(attentions, input_lengths, target_lengths, g=0.2, multihead=False):
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"""A metric to evaluate how diagonal a attention distribution is."""
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W = guided_attentions(input_lengths, target_lengths, g)
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W_tensor = paddle.to_tensor(W)
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if not multihead:
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return paddle.mean(attentions * W_tensor)
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else:
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return paddle.mean(attentions * paddle.unsqueeze(W_tensor, 1))
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@numba.jit(nopython=True)
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def guided_attention(N, max_N, T, max_T, g):
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W = np.zeros((max_T, max_N), dtype=np.float32)
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for t in range(T):
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for n in range(N):
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W[t, n] = 1 - np.exp(-(n / N - t / T)**2 / (2 * g * g))
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# (T_dec, T_enc)
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return W
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def guided_attentions(input_lengths, target_lengths, g=0.2):
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B = len(input_lengths)
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max_input_len = input_lengths.max()
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max_target_len = target_lengths.max()
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W = np.zeros((B, max_target_len, max_input_len), dtype=np.float32)
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for b in range(B):
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W[b] = guided_attention(input_lengths[b], max_input_len,
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target_lengths[b], max_target_len, g)
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# (B, T_dec, T_enc)
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return W
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@ -1,202 +0,0 @@
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import numpy as np
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import paddle.fluid as fluid
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import paddle.fluid.dygraph as dg
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import paddle.fluid.layers as layers
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class Linear(dg.Layer):
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def __init__(self,
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in_features,
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out_features,
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is_bias=True,
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dtype="float32"):
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super(Linear, self).__init__()
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self.in_features = in_features
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self.out_features = out_features
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self.dtype = dtype
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self.weight = fluid.ParamAttr(
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initializer=fluid.initializer.XavierInitializer())
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self.bias = is_bias
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if is_bias is not False:
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k = math.sqrt(1.0 / in_features)
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self.bias = fluid.ParamAttr(initializer=fluid.initializer.Uniform(
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low=-k, high=k))
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self.linear = dg.Linear(
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in_features,
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out_features,
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param_attr=self.weight,
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bias_attr=self.bias, )
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def forward(self, x):
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x = self.linear(x)
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return x
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class ScaledDotProductAttention(dg.Layer):
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def __init__(self, d_key):
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"""Scaled dot product attention module.
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Args:
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d_key (int): the dim of key in multihead attention.
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"""
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super(ScaledDotProductAttention, self).__init__()
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self.d_key = d_key
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# please attention this mask is diff from pytorch
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def forward(self,
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key,
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value,
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query,
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mask=None,
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query_mask=None,
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dropout=0.1):
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"""
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Compute scaled dot product attention.
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Args:
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key (Variable): shape(B, T, C), dtype float32, the input key of scaled dot product attention.
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value (Variable): shape(B, T, C), dtype float32, the input value of scaled dot product attention.
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query (Variable): shape(B, T, C), dtype float32, the input query of scaled dot product attention.
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mask (Variable, optional): shape(B, T_q, T_k), dtype float32, the mask of key. Defaults to None.
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query_mask (Variable, optional): shape(B, T_q, T_q), dtype float32, the mask of query. Defaults to None.
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dropout (float32, optional): the probability of dropout. Defaults to 0.1.
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Returns:
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result (Variable): shape(B, T, C), the result of mutihead attention.
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attention (Variable): shape(n_head * B, T, C), the attention of key.
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"""
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# Compute attention score
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attention = layers.matmul(
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query, key, transpose_y=True, alpha=self.d_key
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**-0.5) #transpose the last dim in y
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# Mask key to ignore padding
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if mask is not None:
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attention = attention + mask
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attention = layers.softmax(attention, use_cudnn=True)
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attention = layers.dropout(
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attention, dropout, dropout_implementation='upscale_in_train')
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# Mask query to ignore padding
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if query_mask is not None:
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attention = attention * query_mask
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result = layers.matmul(attention, value)
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return result, attention
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class MultiheadAttention(dg.Layer):
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def __init__(self,
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num_hidden,
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d_k,
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d_q,
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num_head=4,
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is_bias=False,
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dropout=0.1,
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is_concat=True):
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"""Multihead Attention.
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Args:
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num_hidden (int): the number of hidden layer in network.
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d_k (int): the dim of key in multihead attention.
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d_q (int): the dim of query in multihead attention.
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num_head (int, optional): the head number of multihead attention. Defaults to 4.
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is_bias (bool, optional): whether have bias in linear layers. Default to False.
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dropout (float, optional): dropout probability of FFTBlock. Defaults to 0.1.
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is_concat (bool, optional): whether concat query and result. Default to True.
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"""
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super(MultiheadAttention, self).__init__()
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self.num_hidden = num_hidden
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self.num_head = num_head
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self.d_k = d_k
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self.d_q = d_q
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self.dropout = dropout
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self.is_concat = is_concat
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self.key = Linear(num_hidden, num_head * d_k, is_bias=is_bias)
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self.value = Linear(num_hidden, num_head * d_k, is_bias=is_bias)
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self.query = Linear(num_hidden, num_head * d_q, is_bias=is_bias)
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self.scal_attn = ScaledDotProductAttention(d_k)
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if self.is_concat:
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self.fc = Linear(num_head * d_q * 2, num_hidden)
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else:
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self.fc = Linear(num_head * d_q, num_hidden)
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self.layer_norm = dg.LayerNorm(num_hidden)
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def forward(self, key, value, query_input, mask=None, query_mask=None):
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"""
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Compute attention.
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Args:
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key (Variable): shape(B, T, C), dtype float32, the input key of attention.
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value (Variable): shape(B, T, C), dtype float32, the input value of attention.
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query_input (Variable): shape(B, T, C), dtype float32, the input query of attention.
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mask (Variable, optional): shape(B, T_query, T_key), dtype float32, the mask of key. Defaults to None.
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query_mask (Variable, optional): shape(B, T_query, T_key), dtype float32, the mask of query. Defaults to None.
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Returns:
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result (Variable): shape(B, T, C), the result of mutihead attention.
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attention (Variable): shape(num_head * B, T, C), the attention of key and query.
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"""
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batch_size = key.shape[0]
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seq_len_key = key.shape[1]
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seq_len_query = query_input.shape[1]
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# Make multihead attention
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key = layers.reshape(
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self.key(key), [batch_size, seq_len_key, self.num_head, self.d_k])
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value = layers.reshape(
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self.value(value),
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[batch_size, seq_len_key, self.num_head, self.d_k])
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query = layers.reshape(
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self.query(query_input),
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[batch_size, seq_len_query, self.num_head, self.d_q])
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key = layers.reshape(
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layers.transpose(key, [2, 0, 1, 3]), [-1, seq_len_key, self.d_k])
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value = layers.reshape(
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layers.transpose(value, [2, 0, 1, 3]),
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[-1, seq_len_key, self.d_k])
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query = layers.reshape(
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layers.transpose(query, [2, 0, 1, 3]),
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[-1, seq_len_query, self.d_q])
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result, attention = self.scal_attn(
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key, value, query, mask=mask, query_mask=query_mask)
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# concat all multihead result
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result = layers.reshape(
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result, [self.num_head, batch_size, seq_len_query, self.d_q])
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result = layers.reshape(
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layers.transpose(result, [1, 2, 0, 3]),
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[batch_size, seq_len_query, -1])
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if self.is_concat:
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result = layers.concat([query_input, result], axis=-1)
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result = layers.dropout(
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self.fc(result),
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self.dropout,
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dropout_implementation='upscale_in_train')
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result = result + query_input
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result = self.layer_norm(result)
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return result, attention
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