Whether frame-level loss is used？ about pytorch-mbnet

[sky1456723]

Hi, liushenme
In line 112 to 122 in train.py, the model output is of shape (batch, seq_len) (line 116 and 117), and the label to calculate MSE is repeated to shape (batch, seq_len). So when we calculate the MSE, actually the frame-level loss is used.

If you think that there are some mistakes in these lines, please let me know. Thank you.

[liushenme]

Hi, I think your code of the frame-level loss have some mistakes. Because your model only outputs the uttrance-level score without the frame-level scores, but the frame-level scores are needed in the frame-level loss in MOSNet. So I think your code just repeats uttrance-level score many times.

[sky1456723]

The model output "mean score" in line 110 is the output of the LSTM and DNN according to model.py, so I think that the model output is frame-level (at least, I think that it is time-dependent). The "repeat" operation in line 116 is for the label. According to MOSNet (https://arxiv.org/pdf/1904.08352.pdf) eq. 1, I think the operation is correct. Do I misunderstand about the frame-level loss?

(I clean the code few hours ago, so the line number may change a little bit.)

[liushenme]

Thanks for your reply. I I re-read your code, i think the frame-level loss is correct. But in the MOSNet (https://arxiv.org/pdf/1904.08352.pdf) eq. 1, the first part uttrance-level loss was not used in your code. Have you tried to use the two loss together according to MOSNet？

[sky1456723]

I have not tried to use the utterance-level loss since MBNet uses frame-level loss in the paper. But I think that the usage of judge bias is the reason why MBNet outperforms MOSNet. So I guess that the utterance-level loss may not improve MBNet greatly.

[liushenme]

I see, thank you.