I am working on an audio classification project where I’m converting audio into spectrograms (1-channel images) and want the model to classify whether the spectrogram belongs to one of two classes. I perform this conversion on the fly, i.e. not saving any images locally, and have my input data now in form of a tensor with the shape:
torch.Size([398709, 1, 128]) (398,709 training examples, 1 channel, 128 image size.)
I have my targets in form of a tensor, too, with shape:
torch.Size([398709, 1]) (398,709 outputs of 0 or 1) I can also have this data in form of a list with a length of 398709 elements.
My question is: How to pass my data in form of tensors into
I have tried creating
get_y functions like this (where x is the tensor with spectrograms and y the tensor with targets):
def get_x(x) : return x def get_y(x) : return y
But I’m getting
Creating a dataset using
list(zip(x,y)), then creating a training
DataLoader and a valid
DataLoader but then I’m also getting errors, and nothing seems to work. Any help would be highly appreciated!