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we are trying to train wav2vec-u on Korean data but our GAN model does not converge well. Are the parameters used to train the model presented in the paper the same as those included in the repo? Also, are the parameters for the multilingual GAN the same as the English model?
Code
What have you tried?
We successfully replicated the Librispeech experiment with English data. We prepared a Korean wav2vec 2.0 model with the public AI hub Korean dataset, but the GAN model does not converge when trained on this same data.
( it is not decreasing until 150K)
We also tried training the GAN using the XLRS model as an encoder using English Librispeech 100 dataset, and it also does not converge well (with hyper parameter range in paper and recipe). The valididation weighted PPL does not fall below ~60.
( it is not decreasing until 150K)
What's your environment?
fairseq Version (e.g., 1.0 or main): 1.0.0a0+741fd13
@KimJeongSun Hello, I have the same problem with wav2vec-u experiment on the Mandarin data set. I guess it may be related to GAN's training hyperparameter settings, but I have adjusted the hyperparameters many times and have not achieved reasonable results. If you have new progress, please share it.
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❓ Questions and Help
Before asking:
What is your question?
we are trying to train wav2vec-u on Korean data but our GAN model does not converge well. Are the parameters used to train the model presented in the paper the same as those included in the repo? Also, are the parameters for the multilingual GAN the same as the English model?
Code
What have you tried?
We successfully replicated the Librispeech experiment with English data. We prepared a Korean wav2vec 2.0 model with the public AI hub Korean dataset, but the GAN model does not converge when trained on this same data.

( it is not decreasing until 150K)
We also tried training the GAN using the XLRS model as an encoder using English Librispeech 100 dataset, and it also does not converge well (with hyper parameter range in paper and recipe). The valididation weighted PPL does not fall below ~60.



( it is not decreasing until 150K)
What's your environment?
pip
, source): git clone https://github.com/pytorch/fairseq.git && cd fairseq && git reset --hard 741fd13The text was updated successfully, but these errors were encountered: