mirror of https://github.com/coqui-ai/TTS.git
Remove variables
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081e3e8d10
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@ -1,6 +1,5 @@
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# coding: utf-8
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# coding: utf-8
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import torch
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import torch
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from torch.autograd import Variable
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from torch import nn
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from torch import nn
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from .attention import AttentionRNN
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from .attention import AttentionRNN
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@ -263,16 +262,16 @@ class Decoder(nn.Module):
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self.memory_dim, self.r)
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self.memory_dim, self.r)
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T_decoder = memory.size(1)
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T_decoder = memory.size(1)
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# go frame - 0 frames tarting the sequence
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# go frame - 0 frames tarting the sequence
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initial_memory = Variable(
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initial_memory = \
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inputs.data.new(B, self.memory_dim * self.r).zero_())
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inputs.data.new(B, self.memory_dim * self.r).zero_().requires_grad_()
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# Init decoder states
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# Init decoder states
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attention_rnn_hidden = Variable(
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attention_rnn_hidden = \
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inputs.data.new(B, 256).zero_())
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inputs.data.new(B, 256).zero_().requires_grad_()
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decoder_rnn_hiddens = [Variable(
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decoder_rnn_hiddens = [
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inputs.data.new(B, 256).zero_())
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inputs.data.new(B, 256).zero_().requires_grad_()
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for _ in range(len(self.decoder_rnns))]
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for _ in range(len(self.decoder_rnns))]
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current_context_vec = Variable(
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current_context_vec = \
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inputs.data.new(B, 256).zero_())
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inputs.data.new(B, 256).zero_().requires_grad_()
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# Time first (T_decoder, B, memory_dim)
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# Time first (T_decoder, B, memory_dim)
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if memory is not None:
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if memory is not None:
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memory = memory.transpose(0, 1)
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memory = memory.transpose(0, 1)
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@ -1,6 +1,5 @@
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# coding: utf-8
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# coding: utf-8
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import torch
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import torch
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from torch.autograd import Variable
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from torch import nn
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from torch import nn
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from TTS.utils.text.symbols import symbols
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from TTS.utils.text.symbols import symbols
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from TTS.layers.tacotron import Prenet, Encoder, Decoder, CBHG
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from TTS.layers.tacotron import Prenet, Encoder, Decoder, CBHG
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