5750090fcd4a6718608c7357e3890f5b2a545ae0,layers/tacotron.py,Decoder,forward,#Decoder#Any#Any#,235

Before Change

                    memory_input = torch.div(outputs[-1] + memory[t-1], 2.0)
                    // add a random noise
                    noise = torch.autograd.Variable(
                        memory_input.data.new(memory_input.size()).normal_(0.0, 0.5))
                    memory_input = memory_input + noise

            // Prenet
            processed_memory = self.prenet(memory_input)

After Change

            stop_token_input = decoder_input
            
            // stop token prediction
            stop_token_input = torch.cat((output, current_context_vec), -1)
            stop_output = self.stop_token(stop_token_input)

            // predict mel vectors from decoder vectors
            output = self.proj_to_mel(output)

            outputs += [output]
            alignments += [alignment]
            stop_outputs += [stop_output]

            t += 1

            if (not greedy and self.training) or (greedy and memory is not None):
                if t >= T_decoder:
                    break
            else:
                if t > 1 and is_end_of_frames(output, self.eps):
                    break
                elif t > self.max_decoder_steps:
                    print(" !! Decoder stopped with "max_decoder_steps". \
                          Something is probably wrong.")
                    break
                           
        assert greedy or len(outputs) == T_decoder

        // Back to batch first
        alignments = torch.stack(alignments).transpose(0, 1)
        outputs = torch.stack(outputs).transpose(0, 1).contiguous()
        stop_outputs = torch.stack(stop_outputs).transpose(0, 1).contiguous()

        return outputs, alignments, stop_outputs

Instances