How can I adapt the pretrained AST model to fit my own dataset about ast

[YuanGongND]

hi there,

Is this an audio spectrogram?

I assume 32 is the batch size, 513 is the time, and 313 is the #number of frequency bins of the filter, right?

[zky-66]

hi there,

Is this an audio spectrogram?

I assume 32 is the batch size, 513 is the time, and 313 is the #number of frequency bins of the filter, right?

Yes, the following problem occurred: RuntimeError: Error(s) in loading state_dict for DataParallel:
size mismatch for module.v.pos_embed: copying a param with shape torch.Size([1, 1214, 768]) from checkpoint, the shape in current model is torch.Size([1, 602, 768]).

[YuanGongND]

this error is expected.

The problem is your #number of frequency bins, what is the sampling rate of your audio? If it is 16kHz, please use our code to covert to 128 frequency bins; if it is not, the best way is to convert them to 16kHz and then use our code to convert to 128 frequency bins.

-Yuan

[zky-66]

the sampling rate of your audio is 16kHz，but how to use your code to convert to 128 frequency bins，I don't know which part of the code to modify.Does the input_tdim=1024 need to be modified?

[YuanGongND]

If you do nothing, the code will convert it to 128, i.e., the shape should be [32, 513, 128].

I suggest to first run the ESC-50 recipe, it is simple, and should not have any problem.

[zky-66]

If you do nothing, the code will convert it to 128, i.e., the shape should be [32, 513, 128].

I suggest to first run the ESC-50 recipe, it is simple, and should not have any problem.

I simply inserted the following code into my network, where test_input = x_spec.to(device), and the dimensions of x_spec are [32, 513, 313]. Is it correct to use the pretrained model like this？

    pretrained_mdl_path = '/home/zky/ST/pretrained_models/pretrained_models/audioset_10_10_0.4495.pth'
    # get the frequency and time stride of the pretrained model from its name
    fstride, tstride = int(pretrained_mdl_path.split('/')[-1].split('_')[1]), int(
        pretrained_mdl_path.split('/')[-1].split('_')[2].split('.')[0])

    input_tdim = 513
    input_fdim = 313
  
    if torch.cuda.device_count() > 1:
        device = torch.device("cuda:1")  
    else:
        raise Exception("只找到一块GPU，但是代码尝试使用第二块GPU，请检查系统配置")

    audio_model = ASTModel(input_tdim=input_tdim,input_fdim=input_fdim).to(device)
    audio_model = torch.nn.DataParallel(audio_model, device_ids=[1])  

    sd = torch.load(pretrained_mdl_path, map_location=device)
    audio_model.load_state_dict(sd, strict=False)

    test_input = x_spec.to(device)  
    test_output = audio_model(test_input) 

    print("test_output:",test_output.shape)