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1. fix imports for renamed functions in position encoding; 

2. fix dimension in MLPPrenet in transformer_tts;
3. use dropout also in inference in MLPPrenet in transformer_tts.
This commit is contained in:
chenfeiyu 2021-01-29 18:56:49 +08:00
parent a0ce65211c
commit 30e3b9172f
1 changed files with 4 additions and 4 deletions
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8
parakeet/models/transformer_tts.py
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@ -317,14 +317,14 @@ class MLPPreNet(nn.Layer):
super(MLPPreNet, self).__init__()
self.lin1 = nn.Linear(d_input, d_hidden)
self.lin2 = nn.Linear(d_hidden, d_hidden)
self.lin3 = nn.Linear(d_hidden, d_hidden)
self.lin3 = nn.Linear(d_hidden, d_output)
self.dropout = dropout
def forward(self, x, dropout):
l1 = F.dropout(
F.relu(self.lin1(x)), self.dropout, training=self.training)
F.relu(self.lin1(x)), self.dropout, training=True)
l2 = F.dropout(
F.relu(self.lin2(l1)), self.dropout, training=self.training)
F.relu(self.lin2(l1)), self.dropout, training=True)
l3 = self.lin3(l2)
return l3
@ -473,7 +473,7 @@ class TransformerTTS(nn.Layer):
# twice its length if needed
if x.shape[1] * self.r > self.decoder_pe.shape[0]:
new_T = max(x.shape[1] * self.r, self.decoder_pe.shape[0] * 2)
self.decoder_pe = pe.positional_encoding(0, new_T, self.d_decoder)
self.decoder_pe = pe.sinusoid_positional_encoding(0, new_T, self.d_decoder)
pos_enc = self.decoder_pe[:T_dec * self.r:self.r, :]
x = x.scale(math.sqrt(
self.d_decoder)) + pos_enc * self.decoder_pe_scalar