Can't train with output of Tacotron 2

[tuong-olli]

The shape of mel output of Tacotron2 is bigger than mel extracted from audio and the model has issue

 File "train.py", line 113, in train
    for i, batch in enumerate(train_loader):
  File "/data/tuong/Yen/hifi-gan/env/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 345, in __next__
    data = self._next_data()
  File "/data/tuong/Yen/hifi-gan/env/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 856, in _next_data
    return self._process_data(data)
  File "/data/tuong/Yen/hifi-gan/env/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 881, in _process_data
    data.reraise()
  File "/data/tuong/Yen/hifi-gan/env/lib/python3.6/site-packages/torch/_utils.py", line 394, in reraise
    raise self.exc_type(msg)
RuntimeError: Caught RuntimeError in DataLoader worker process 2.
Original Traceback (most recent call last):
  File "/data/tuong/Yen/hifi-gan/env/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
    data = fetcher.fetch(index)
  File "/data/tuong/Yen/hifi-gan/env/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch
    return self.collate_fn(data)
  File "/data/tuong/Yen/hifi-gan/env/lib/python3.6/site-packages/torch/utils/data/_utils/collate.py", line 79, in default_collate
    return [default_collate(samples) for samples in transposed]
  File "/data/tuong/Yen/hifi-gan/env/lib/python3.6/site-packages/torch/utils/data/_utils/collate.py", line 79, in <listcomp>
    return [default_collate(samples) for samples in transposed]
  File "/data/tuong/Yen/hifi-gan/env/lib/python3.6/site-packages/torch/utils/data/_utils/collate.py", line 55, in default_collate
    return torch.stack(batch, 0, out=out)
RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 8192 and 8119 in dimension 1 at /pytorch/aten/src/TH/generic/THTensor.cpp:612

[CookiePPP]

Got 8192 and 8119 in dimension 1 at /pytorch/aten/src/TH/generic/THTensor.cpp:612

I think this is more likely to be an incorrectly padded audio tensor. There shouldn't ever be a spectrogram that long being collated.

Saying that, I'm looking at the code and I can't figure out how this issue can occur. If you're able to provide more details then that'd help a lot.

Which tacotron2 repo are you using and what is the hop_length you're using for both tacotron2 and hifigan?

[tuong-olli]

I used the below parameters to train successfully with my audio data.

"segment_size": 8192,
    "num_mels": 80,
    "num_freq": 513,
    "n_fft": 1024,
    "hop_size": 256,
    "win_size": 1024,

After that I have synthesized mel spectrogram from Tacotron 2 model with my dataset to train with --fine_tuning True and the same parameters but it has been errored:

Using a target size (torch.Size([1, 80, 759])) that is different to the input size (torch.Size([1, 80, 815])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
  val_err_tot += F.l1_loss(y_mel, y_g_hat_mel).item()

To fix this error I add padding to train.py file:

if y_mel.size(2) > y_g_hat_mel.size(2):
     y_g_hat_mel = torch.nn.functional.pad(y_g_hat_mel, (0, y_mel.size(2) - y_g_hat_mel.size(2)), 'constant')
elif y_mel.size(2) < y_g_hat_mel.size(2):
     y_mel = torch.nn.functional.pad(y_mel, (0, y_g_hat_mel.size(2) - y_mel.size(2)), 'constant')

And it is errored as above.

[ghost]

I have a very similar issue. I generated the mell files using tacotron2.

train.py:199: UserWarning: Using a target size (torch.Size([1, 80, 240])) that is different to the input size (torch.Size([1, 80, 235])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
  val_err_tot += F.l1_loss(y_mel, y_g_hat_mel).item()
Traceback (most recent call last):
  File "train.py", line 271, in <module>
    main()
  File "train.py", line 267, in main
    train(0, a, h)
  File "train.py", line 199, in train
    val_err_tot += F.l1_loss(y_mel, y_g_hat_mel).item()
  File "C:\Anaconda3\lib\site-packages\torch\nn\functional.py", line 2633, in l1_loss
    expanded_input, expanded_target = torch.broadcast_tensors(input, target)
  File "C:\Anaconda3\lib\site-packages\torch\functional.py", line 71, in broadcast_tensors
    return _VF.broadcast_tensors(tensors)  # type: ignore
RuntimeError: The size of tensor a (235) must match the size of tensor b (240) at non-singleton dimension 2

[longjoke]

You can try the fix from this fork (line 245).

Replace in line 199: val_err_tot += F.l1_loss(y_mel, y_g_hat_mel).item()

with: val_err_tot += F.l1_loss(y_mel, y_g_hat_mel[:,:,:y_mel.size(2)]).item()

[v-nhandt21]

val_err_tot

This seem to solve that problem miss match between two mel spec, but when I compare difference y_mel, y_g_hat_mel, It is larger than when using mel from audio (not using mel from Tacotron 2 force alignment)

Can you share your result is okie or not?

[v-nhandt21]

e synthesized mel spectrogram from Tacotron 2 model with my dataset to train with --fine_tuning True and the same parameters but it has been errored:

The mel you get from Tacotron2 is at training step or inference step, As my knowledge, It is from force teacher at training.

[Body123]

val_err_tot

This seem to solve that problem miss match between two mel spec, but when I compare difference y_mel, y_g_hat_mel, It is larger than when using mel from audio (not using mel from Tacotron 2 force alignment)

Can you share your result is okie or not?

do you resolve this issue