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pretrained show worse cer than decode? #142
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What if you change it to There is a convolutional layer in the conformer model and the padding in a batch may affect the result. |
Both feature extractors use the same parameters and should produce the same features. |
I have use --max-duration=1 for decode, but meet MemoryError below. Also print the batch data for error. |
Can you try doing |
Hm, I think we're not quite drilling down into the error yet. Looks like the error may have occurred in _k2.index, which goes to C++ code. See if you can find it by running with gdb; you may need to do 'catch throw'. |
I have trained my own model and test one my datasets.
The first step is decoded with many params, just like finetune (as decode.py), and save the best params. I use --max-duration=20. And I will save the decode results using the best params on all dataset (not just one).
Then I use this best params to decode wave (as pretrained.py), just one by one on these datasets.
All my dataset show a little worse cer using pretrained.py. Cer comparision below.
decode.py: 3.190 12.802 17.995 9.569 14.478 10.299 16.242 7.329 20.695
pretrained.py: 3.203 13.029 18.177 9.662 14.610 10.447 16.463 7.333 20.911
Is this normal? I see the feature extraction is not the same, will this be the reason?
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