Why does the mel.npy file I trained with the tacotron2 model not match the dimensions in hifi-gan?

I trained the tacotron2 model to produce mel_**.npy ,but in this model, the error of dimension mismatch is reported python3 inference_e2e.py --checkpoint_file cp_hifigan-1208-test/g_00036000 Initializing Inference Process.. cp_hifigan-1208-test/g_00036000 Loading 'cp_hifigan-1208-test/g_00036000' Complete. Removing weight norm... Traceback (most recent call last): File "inference_e2e.py", line 90, in <module> main() File "inference_e2e.py", line 86, in main inference(a) File "inference_e2e.py", line 51, in inference y_g_hat = generator(x) File "/home/zhchen/python3/lib/python3.5/site-packages/torch/nn/modules/module.py", line 547, in __call__ result = self.forward(*input, **kwargs) File "/home/zhchen/hifi-gan-master/models.py", line 101, in forward x = self.conv_pre(x) File "/home/zhchen/python3/lib/python3.5/site-packages/torch/nn/modules/module.py", line 547, in __call__ result = self.forward(*input, **kwargs) File "/home/zhchen/python3/lib/python3.5/site-packages/torch/nn/modules/conv.py", line 200, in forward self.padding, self.dilation, self.groups) RuntimeError: Expected 3-dimensional input for 3-dimensional weight 128 80, but got 2-dimensional input of size [288, 80] instead

[Miralan]

It is because of the shape of the input data. Insert the following code at line 50 of inference_e2e.py file and try again.

        if len(x.shape) < 3:
            x = x.unsqueeze(0)

We will update it soon.

Maybe below better

          if len(x.shape) < 3:
              x = x.unsqueeze(0)
          if not x.shape[1] == h.num_mels:
              x = x.tranpose(1, 2) 

[McTi]

Just a heads up that there’s a typo in that code snippet… tranpose -> transpose

Should be:

          if len(x.shape) < 3:
              x = x.unsqueeze(0)
          if not x.shape[1] == h.num_mels:
              x = x.transpose(1, 2)