Medix-AI/coqui-tts
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coqui-tts
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linter fix

This commit is contained in:
Eren Golge 2019-10-24 14:11:07 +02:00
parent 77f5fd0584
commit ea32f2368d
2 changed files with 13 additions and 12 deletions
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23
layers/tacotron2.py
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@ -162,15 +162,16 @@ class Decoder(nn.Module):
B = inputs.size(0) B = inputs.size(0)
# T = inputs.size(1) # T = inputs.size(1)
if not keep_states: if not keep_states:
self.query = torch.zeros(1, device=inputs.device).repeat(B, self.query_dim) self.query = torch.zeros(1, device=inputs.device).repeat(
self.attention_rnn_cell_state = torch.zeros(1, device=inputs.device).repeat(B, B, self.query_dim)
self.query_dim) self.attention_rnn_cell_state = torch.zeros(
self.decoder_hidden = torch.zeros(1, device=inputs.device).repeat(B, 1, device=inputs.device).repeat(B, self.query_dim)
self.decoder_rnn_dim) self.decoder_hidden = torch.zeros(1, device=inputs.device).repeat(
self.decoder_cell = torch.zeros(1, device=inputs.device).repeat(B, B, self.decoder_rnn_dim)
self.decoder_rnn_dim) self.decoder_cell = torch.zeros(1, device=inputs.device).repeat(
self.context = torch.zeros(1, device=inputs.device).repeat(B, B, self.decoder_rnn_dim)
self.encoder_embedding_dim) self.context = torch.zeros(1, device=inputs.device).repeat(
B, self.encoder_embedding_dim)
self.inputs = inputs self.inputs = inputs
self.processed_inputs = self.attention.inputs_layer(inputs) self.processed_inputs = self.attention.inputs_layer(inputs)
self.mask = mask self.mask = mask
@ -277,7 +278,7 @@ class Decoder(nn.Module):
stop_flags[2] = t > inputs.shape[1] * 2 stop_flags[2] = t > inputs.shape[1] * 2
if all(stop_flags): if all(stop_flags):
break break
elif len(outputs) == self.max_decoder_steps: if len(outputs) == self.max_decoder_steps:
print(" | > Decoder stopped with 'max_decoder_steps") print(" | > Decoder stopped with 'max_decoder_steps")
break break
@ -317,7 +318,7 @@ class Decoder(nn.Module):
stop_flags[2] = t > inputs.shape[1] * 2 stop_flags[2] = t > inputs.shape[1] * 2
if all(stop_flags): if all(stop_flags):
break break
elif len(outputs) == self.max_decoder_steps: if len(outputs) == self.max_decoder_steps:
print(" | > Decoder stopped with 'max_decoder_steps") print(" | > Decoder stopped with 'max_decoder_steps")
break break