def parse_output(self, outputs, output_lengths=None): if self.mask_padding and output_lengths is not None: mask = ~get_mask_from_lengths(output_lengths) mask = mask.expand(self.n_mel_channels, mask.size(0), mask.size(1)) mask = mask.permute(1, 0, 2) outputs[0].data.masked_fill_(mask, 0.0) outputs[1].data.masked_fill_(mask, 0.0) outputs[2].data.masked_fill_(mask[:, 0, :], 1e3) # gate energies return outputs
def forward(self, memory, decoder_inputs, memory_lengths): """ Decoder forward pass for training PARAMS ------ memory: Encoder outputs decoder_inputs: Decoder inputs for teacher forcing. i.e. mel-specs memory_lengths: Encoder output lengths for attention masking. RETURNS ------- mel_outputs: mel outputs from the decoder gate_outputs: gate outputs from the decoder alignments: sequence of attention weights from the decoder """ decoder_input = self.get_go_frame(memory).unsqueeze(0) decoder_inputs = self.parse_decoder_inputs(decoder_inputs) decoder_inputs = torch.cat((decoder_input, decoder_inputs), dim=0) decoder_inputs = self.prenet(decoder_inputs) self.initialize_decoder_states( memory, mask=~get_mask_from_lengths(memory_lengths)) mel_outputs, gate_outputs, alignments = [], [], [] while len(mel_outputs) < decoder_inputs.size(0) - 1: decoder_input = decoder_inputs[len(mel_outputs)] mel_output, gate_output, attention_weights = self.decode( decoder_input) mel_outputs += [mel_output.squeeze(1)] gate_outputs += [gate_output.squeeze()] alignments += [attention_weights] mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs( mel_outputs, gate_outputs, alignments) return mel_outputs, gate_outputs, alignments