Does this program require a large amount of gpu memory？ about cmgan HOT 7 CLOSED

Yes it requires large memories, the computational complexity of attention increase quadratically with the increase of the input length, you can reduce the length of track segmentation to reduce the memory cost.

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Thank you for your answer，I wonder if reducing the track length will affect the performance of the model？

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Yes, of course, it has some impact because attention relies on long-distance dependency if you reduce the track length to 40ms or 80ms it will have an extensive drop.

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Sorry to bother you again, I try to use the code training model to use batch_ size=4，cut_ len=16000 * 2, and the model trained for 80 and 120 epochs was tested, but the two results were found to be lower than the result in the paper.

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How much lower it is? If not much you can try to test with less epochs, as we train with only batch size of 3.

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the ssnr is only 10.4 in epoch=80, I want ask what method do you use to reduce the data to 16 khz.

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You can use the librosa downsampling which is similar to torch:
audio_down, sr = librosa.load(audio_path, sr=16000)

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