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base: malarinv:fp16_path
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malarinv:master malarinv:experiments malarinv:clean_slate malarinv:latest_model malarinv:attention_full_mel malarinv:fp16_path malarinv:load_mel_from_disk malarinv:padding-patch-0.4 malarinv:mask-utils-0.4 malarinv:single-gpu-and-0.4
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compare: malarinv:attention_full_mel
Branches Tags
malarinv:master malarinv:experiments malarinv:clean_slate malarinv:latest_model malarinv:attention_full_mel malarinv:fp16_path malarinv:load_mel_from_disk malarinv:padding-patch-0.4 malarinv:mask-utils-0.4 malarinv:single-gpu-and-0.4

3 Commits
fp16_path ... attention_

Author SHA1 Message Date
Rafael Valle
d5b64729d1 model.py: moving for better readibility 2018-05-20 12:22:06 -07:00
Rafael Valle
977cb37cea model.py: attending to full mel instead of prenet and dropout mel 2018-05-18 06:59:09 -07:00
Rafael Valle
da30fd8709 Merge pull request #20 from NVIDIA/fp16_path 
Fp16 patch, not path!
 2018-05-15 09:55:19 -07:00
1 changed files with 5 additions and 6 deletions
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11
model.py
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@@ -221,7 +221,7 @@ class Decoder(nn.Module):
[hparams.prenet_dim, hparams.prenet_dim])
self.attention_rnn = nn.LSTMCell(
hparams.prenet_dim + hparams.encoder_embedding_dim,
hparams.decoder_rnn_dim + hparams.encoder_embedding_dim,
hparams.attention_rnn_dim)
self.attention_layer = Attention(
@@ -230,7 +230,7 @@ class Decoder(nn.Module):
hparams.attention_location_kernel_size)
self.decoder_rnn = nn.LSTMCell(
hparams.attention_rnn_dim + hparams.encoder_embedding_dim,
hparams.prenet_dim + hparams.encoder_embedding_dim,
hparams.decoder_rnn_dim, 1)
self.linear_projection = LinearNorm(
@@ -351,8 +351,7 @@ class Decoder(nn.Module):
attention_weights:
"""
decoder_input = self.prenet(decoder_input)
cell_input = torch.cat((decoder_input, self.attention_context), -1)
cell_input = torch.cat((self.decoder_hidden, self.attention_context), -1)
self.attention_hidden, self.attention_cell = self.attention_rnn(
cell_input, (self.attention_hidden, self.attention_cell))
@@ -364,8 +363,8 @@ class Decoder(nn.Module):
attention_weights_cat, self.mask)
self.attention_weights_cum += self.attention_weights
decoder_input = torch.cat(
(self.attention_hidden, self.attention_context), -1)
prenet_output = self.prenet(decoder_input)
decoder_input = torch.cat((prenet_output, self.attention_context), -1)
self.decoder_hidden, self.decoder_cell = self.decoder_rnn(
decoder_input, (self.decoder_hidden, self.decoder_cell))