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add location attn to decoder

pull/10/head
Eren Golge 2018-05-18 03:34:07 -07:00
parent 243204bc3e
commit 288a6b5b1d
1 changed files with 3 additions and 6 deletions
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9
layers/tacotron.py
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@ -2,8 +2,6 @@
import torch import torch
from torch import nn from torch import nn
from .attention import AttentionRNN from .attention import AttentionRNN
from .attention import get_mask_from_lengths
class Prenet(nn.Module): class Prenet(nn.Module):
r""" Prenet as explained at https://arxiv.org/abs/1703.10135. r""" Prenet as explained at https://arxiv.org/abs/1703.10135.
@ -270,8 +268,8 @@ class Decoder(nn.Module):
for _ in range(len(self.decoder_rnns))] for _ in range(len(self.decoder_rnns))]
current_context_vec = inputs.data.new(B, 256).zero_() current_context_vec = inputs.data.new(B, 256).zero_()
stopnet_rnn_hidden = inputs.data.new(B, self.r * self.memory_dim).zero_() stopnet_rnn_hidden = inputs.data.new(B, self.r * self.memory_dim).zero_()
attention_vec = memory.data.new(B, T).zero_() attention_vec = inputs.data.new(B, T).zero_()
attention_vec_cum = memory.data.new(B, T).zero_() attention_vec_cum = inputs.data.new(B, T).zero_()
# Time first (T_decoder, B, memory_dim) # Time first (T_decoder, B, memory_dim)
if memory is not None: if memory is not None:
memory = memory.transpose(0, 1) memory = memory.transpose(0, 1)
@ -290,12 +288,11 @@ class Decoder(nn.Module):
processed_memory = self.prenet(memory_input) processed_memory = self.prenet(memory_input)
# Attention RNN # Attention RNN
attention_vec_cat = torch.cat((attention_vec.unsqueeze(1), attention_vec_cat = torch.cat((attention_vec.unsqueeze(1),
attention_vec_cum.unsqueeze(1)), attention_vec_cum.unsqueeze(1) / (t + 1)),
dim=1) dim=1)
attention_rnn_hidden, current_context_vec, attention = self.attention_rnn( attention_rnn_hidden, current_context_vec, attention = self.attention_rnn(
processed_memory, current_context_vec, attention_rnn_hidden, inputs, attention_vec_cat) processed_memory, current_context_vec, attention_rnn_hidden, inputs, attention_vec_cat)
attention_vec_cum += attention_vec attention_vec_cum += attention_vec
attention_vec_cum /= (t + 1)
# Concat RNN output and attention context vector # Concat RNN output and attention context vector
decoder_input = self.project_to_decoder_in( decoder_input = self.project_to_decoder_in(
torch.cat((attention_rnn_hidden, current_context_vec), -1)) torch.cat((attention_rnn_hidden, current_context_vec), -1))