requirements for training the model about dcase2020_task2_baseline HOT 10 CLOSED

Thank you very much!

1. Minimum requirements for a PC
   I am sorry. I have not checked it exactly.
   We will confirm it at the beginning of the week.

2. About "requirements.txt"
   We will also check it at the beginning of the week.
   As soon as we get information, we will report you.

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Thank you very much.
I want to know your environment.

1. OS version
2. Python version

For answering the second question, I changed versions to

• Keras-Preprocessing == 1.1.0
• gast == 0.3.3
  and ran the baseline.
  The same results were obtained.

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@nakamura419
Thanks for your reply.
I run the code in
Windows10 Home (version 1903),
Pycharm 2019.2.4(Community),
Python 3.6.10.

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Answer questions 1.
As a result of training only with the largest amount of data, Toy Car, the maximum memory usage was 7578MB.
Even if batch_size was changed, this usage did not change much, so I think it is better not to use all dev_data with 8GB memory.

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We have found that the memory usage can be reduced to 4136MB if the parameter of "n_mels" is changed from 128 to 64.
Please try it.

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@Yuki-Nikaido
Thank you. I changed the 'n_mels' from 128 to 64, and 'batch_size' from 512 to 1, it still does not work. However, I reduced half of the training data, then it works. For example, there are 3291 files in valve, and the training process works when only 1496 files are used.

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Because train_data = list_to_vector_array() read all data in to train_data, it is very memory-consuming. It could save memory consumption by reading data in batch_size.

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If only one model (ToyCar, valve ... etc) is trained, it can be executed with 8GB memory, so if you use all data, how about training only one model in one execution?
When using n_mels = 64 and using all dev_data, it was confirmed that a maximum of 8527 MB of memory was used, so in an 8 GB memory environment, training using all dev_data would be difficult.
(Maximum memory usage depends on garbage collection)

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@20050710212 Thank you very much!
We understand that loading data little by little will save memory consumption.
However, processing speed will be slower. So, we will not revise this repository in such a direction.
At this time, it is recommended to train one machine-type model at a time as @Yuki-Nikaido said.
We hope you understand.

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@y-kawagu @Yuki-Nikaido @nakamura419
Thank you for the reply. I have already add a memory chip in my pc.

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