maybe there is a bug in fastspeech.py

[forwiat]

hello rishikksh20, thanks for your contribution! I found a problem when training with these code. in line 415, fastspeech.py

if avg_mel is not None:
    avg_mel = avg_mel.unsqueeze(0)
    # inference
    before_outs, outs, d_outs, _ = self._forward(xs, ilens=ilens, ys=ref_mel, avg_mel=avg_mel,
                                                 is_inference=True,
                                                 phn_level_predictor=phn_level_predictor)  # (L, odim)
else:
    before_outs, outs, d_outs, _ = self._forward(xs, ilens=ilens, ys=ref_mel, is_inference=True,
                                                 phn_level_predictor=phn_level_predictor)  # (L, odim)

# inference
_, outs, _, _, _ = self._forward(xs, ilens, is_inference=True)  # (L, odim)

return outs[0]

I think the last inference don't need to forward?, like below.

if avg_mel is not None:
    avg_mel = avg_mel.unsqueeze(0)
    # inference
    before_outs, outs, d_outs, _ = self._forward(xs, ilens=ilens, ys=ref_mel, avg_mel=avg_mel,
                                                 is_inference=True,
                                                 phn_level_predictor=phn_level_predictor)  # (L, odim)
else:
    before_outs, outs, d_outs, _ = self._forward(xs, ilens=ilens, ys=ref_mel, is_inference=True,
                                                 phn_level_predictor=phn_level_predictor)  # (L, odim)

# inference
#_, outs, _, _, _ = self._forward(xs, ilens, is_inference=True)  # (L, odim)

return outs[0]

[forwiat]

also, in line 182, fastspeech.py.

def _forward(
        self,
        xs: torch.Tensor,
        ilens: torch.Tensor,
        ys: torch.Tensor = None,
        olens: torch.Tensor = None,
        ds: torch.Tensor = None,
        es: torch.Tensor = None,
        ps: torch.Tensor = None,
        is_inference: bool = False,
        phn_level_predictor: bool = False,
        avg_mel: torch.Tensor = None,
    ) -> Sequence[torch.Tensor]:
        # forward encoder
        x_masks = self._source_mask(
            ilens
        )  # (B, Tmax, Tmax) -> torch.Size([32, 121, 121])

        hs, _ = self.encoder(
            xs, x_masks
        )  # (B, Tmax, adim) -> torch.Size([32, 121, 256])

        ## AdaSpeech Specific ##

        uttr = self.utterance_encoder(ys.transpose(1, 2)).transpose(1, 2)
        hs = hs + uttr.repeat(1, hs.size(1), 1)

means ys shouldn't be None, but in line 24, evaluation.py _, after_outs, d_outs, e_outs, p_outs = model._forward(x_.cuda(), ilens.cuda(), out_length_.cuda(), dur_.cuda(), es=e_.cuda(), ps=p_.cuda(), is_inference=False) # [T, num_mel] will throw error.