Medix-AI/coqui-tts
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coqui-tts
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replace zeros() with a better alternative

This commit is contained in:
Eren Golge 2019-10-12 18:34:12 +02:00
parent fbfa20e3b3
commit 53ec066733
2 changed files with 15 additions and 20 deletions
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8
layers/tacotron.py
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@ -340,13 +340,13 @@ class Decoder(nn.Module):
T = inputs.size(1) T = inputs.size(1)
# go frame as zeros matrix # go frame as zeros matrix
if self.use_memory_queue: if self.use_memory_queue:
self.memory_input = torch.zeros(B, self.memory_dim * self.memory_size, device=inputs.device) self.memory_input = torch.zeros(1, device=inputs.device).repeat(B, self.memory_dim * self.memory_size)
else: else:
self.memory_input = torch.zeros(B, self.memory_dim, device=inputs.device) self.memory_input = torch.zeros(1, device=inputs.device).repeat(B, self.memory_dim)
# decoder states # decoder states
self.attention_rnn_hidden = torch.zeros(B, 256, device=inputs.device) self.attention_rnn_hidden = torch.zeros(1, device=inputs.device).repeat(B, 256)
self.decoder_rnn_hiddens = [ self.decoder_rnn_hiddens = [
torch.zeros(B, 256, device=inputs.device) torch.zeros(1, device=inputs.device).repeat(B, 256)
for idx in range(len(self.decoder_rnns)) for idx in range(len(self.decoder_rnns))
] ]
self.context_vec = inputs.data.new(B, self.in_features).zero_() self.context_vec = inputs.data.new(B, self.in_features).zero_()

27
layers/tacotron2.py
View File

@ -154,28 +154,23 @@ class Decoder(nn.Module):
def get_go_frame(self, inputs): def get_go_frame(self, inputs):
B = inputs.size(0) B = inputs.size(0)
memory = torch.zeros(B, memory = torch.zeros(1, device=inputs.device).repeat(B,
self.mel_channels * self.r, self.mel_channels * self.r)
device=inputs.device)
return memory return memory
def _init_states(self, inputs, mask, keep_states=False): def _init_states(self, inputs, mask, keep_states=False):
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(B, self.query_dim, device=inputs.device) self.query = torch.zeros(1, device=inputs.device).repeat(B, self.query_dim)
self.attention_rnn_cell_state = torch.zeros(B, self.attention_rnn_cell_state = torch.zeros(1, device=inputs.device).repeat(B,
self.query_dim, self.query_dim)
device=inputs.device) self.decoder_hidden = torch.zeros(1, device=inputs.device).repeat(B,
self.decoder_hidden = torch.zeros(B, self.decoder_rnn_dim)
self.decoder_rnn_dim, self.decoder_cell = torch.zeros(1, device=inputs.device).repeat(B,
device=inputs.device) self.decoder_rnn_dim)
self.decoder_cell = torch.zeros(B, self.context = torch.zeros(1, device=inputs.device).repeat(B,
self.decoder_rnn_dim, self.encoder_embedding_dim)
device=inputs.device)
self.context = torch.zeros(B,
self.encoder_embedding_dim,
device=inputs.device)
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