about the training details and asking for model checkpoint

[archiki]

about the training details and asking for model checkpoint

[archiki]

Hey @ChangLee0903 !

Thanks for the feedback! I am not sure what you mean by training cost. Personally, I did not use state-of-the-art GPUs (NVIDIA GeForce GTX 1080 Ti ones), so most experiments took me about 12-15 hours on a single GPU depending on the experiment. Yes, I started with a clean speech checkpoint to train the robust ASR (to save compute effort and to start with a reasonable ASR system). This link will point you to the training command used for the base clean speech model along with the download link for the checkpoint. Hope this clarifies your questions.

[ChangLee0903]

Thx 10 billions for your fast reply, all of my questions have been answered. I'll cite you if my paper publish.

[ChangLee0903]

Hi @archiki,
I test your pre-train model with the LM model, which you point.
Since I cannot find the LM model link, I download one from librispeech's official website.
Here is the link: https://www.openslr.org/resources/11/4-gram.arpa.gz
Then the WER(7.334) and CER(3.028), which I got, are different from ur results.
BTW, your trainEnhance.py's arguments are different from the pre-train checkpoint, and I guess all the arguments I need can follow the pre-train checkpoint's settings, right?

best,
Chi-Chang Lee

[archiki]

The difference between the WER and CER numbers must be due to the difference in beam-decoding parameters like --beam-width, --alpha, --beta. Let me know which arguments you are talking about, if I remember correctly, only the noise-injection-related arguments must be different. In that case, please use the arguments in the trainEnhanced.py. Remaining things like --hidden-size and --hidden-layers are inherited from the pretrained checkpoint.

[ChangLee0903]

Hi @archiki,

Thx for ur reply, the arguments I concern is about "--learning-anneal 1.01 --batch-size 64 --
no-sortaGrad --opt-level O1 --loss-scale 1". Are such arguments the same as the pretrained checkpoints? BTW, did u leave the log files of ur training process? I noticed that the loss in trainTLNNoisy would increase in the beginning. Is it normal?

best,
Chi-Chang Lee

[archiki]

Yes, @ChangLee0903, these arguments are taken from the checkpoint. Note: --learning-anneal and --batch-size depend on your dataset, GPU memory, and type of compute (especially the latter, it can be changed to 32 or 16 if need be). The former depends on the training profile and might be different for a different dataset.