Comments (2)
Hi,
How many GPUs did you use for decoding? Current script doesn't support multiple GPU for decoding and if you use >1 GPUs only one part of the dataset will be decoded. If you are under multi-gpu environment, you can do
CUDA_VISIBLE_DEVICES=0 python infer_s2s.py ...
to only use one GPU (index 0).Besides, if your test set contains long utterances (depending on
max_sample_size
in fine-tuning config), there longer utterances will also be ignored. You can check how many of them are ignored by seeing the line like[INFO] - max_keep=500, min_keep=0, loaded 1200, skipped 0 short / 0 long
from the output decoding log. If there are utterances ignored, you can add one line liketask.cfg.max_sample_size=1000000
here ininfer_s2s.py
to decode all utterances.
Yes I just found out decoding cannot be run on multiple GPUs(even CPUs, as long as multiprocessing is involved), but it still took me quite an amount of time to find that out when I went deeper into the code.
Therefore, I would suggest adding some warnings to README.md for users like me who may not know infer_s2s.py can only be run under single GPU setting. Would you consider briefly mentioning this little tip in README on your possible recent updates?
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Hi,
How many GPUs did you use for decoding? Current script doesn't support multiple GPU for decoding and if you use >1 GPUs only one part of the dataset will be decoded. If you are under multi-gpu environment, you can do CUDA_VISIBLE_DEVICES=0 python infer_s2s.py ...
to only use one GPU (index 0).
Besides, if your test set contains long utterances (depending on max_sample_size
in fine-tuning config), there longer utterances will also be ignored. You can check how many of them are ignored by seeing the line like [INFO] - max_keep=500, min_keep=0, loaded 1200, skipped 0 short / 0 long
from the output decoding log. If there are utterances ignored, you can add one line like task.cfg.max_sample_size=1000000
here in infer_s2s.py
to decode all utterances.
from av_hubert.
Related Issues (20)
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