aa40fe1aa0382bf5148d1475dd647def1d2ed332,TTS/tts/models/speedy_speech.py,SpeedySpeech,forward,#SpeedySpeech#,59

Before Change

        x_emb = torch.transpose(x_emb, 1, -1)

        // compute sequence masks
        x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.shape[1]),
                                 1).to(x.dtype)

        y_mask = torch.unsqueeze(sequence_mask(y_lengths, None),
                                 1).to(x_mask.dtype)

        // encoder pass
        o_en = self.encoder(x_emb, x_mask)

        // duration predictor pass
        o_dr_log = self.duration_predictor(o_en.detach(), x_mask)

        // expand o_en with durations
        o_en_ex, attn = self.expand_encoder_outputs(o_en, dr, x_mask, y_mask)

        // positional encoding
        if hasattr(self, "pos_encoder"):
            o_en_ex = self.pos_encoder(o_en_ex, y_mask)

        // decoder pass
        o_de = self.decoder(o_en_ex, y_mask)

        return o_de, o_dr_log.squeeze(1), attn.transpose(1, 2)

    def inference(self, x, x_lengths, g=None):  // pylint: disable=unused-argument

After Change

        return o_de, attn.transpose(1, 2)

    def forward(self, x, x_lengths, y_lengths, dr, g=None):  // pylint: disable=unused-argument
        o_en, o_en_dp, x_mask, g = self._forward_encoder(x, x_lengths, g)
        o_dr_log = self.duration_predictor(o_en_dp.detach(), x_mask)
        o_de, attn= self._forward_decoder(o_en, o_en_dp, dr, x_mask, y_lengths, g=g)
        return o_de, o_dr_log.squeeze(1), attn

Instances