I am using the Wave-U-Net neural network for audio source separation but there is a bug in the code I have. When performing separation, the outputs are stored in a list and each item in the list is a Tensor. However, the tensors are on a GPU and need to be converted to CPU. My issue is that I cannot convert the list to CPU because numpy lists don’t have a .cpu() attribute. How can I convert all the tensors to CPU?
Here is the code I’m working with. The offending list is called “listofpred0”, and the error occurs when it is passed to mu_law_decode.
def test(epoch):  # testing data
model.eval()
start_time = time.time()
with torch.no_grad():
for iloader, xtrain in loadtest:
iloader=iloader.item()
print(iloader)
listofpred0 = []
for ind in range(0, xtrain.shape[-1] - sampleSize, sampleSize):
output = model(xtrain[:, :,ind:ind + sampleSize].to(device))
listofpred0.append(output.reshape(-1))
ans0 = mu_law_decode(np.concatenate(listofpred0))
if not os.path.exists(‘vsCorpus/’): os.makedirs(‘vsCorpus/’)
sf.write(savemusic.format(iloader), ans0, sample_rate)
print(‘test stored done’, np.round(time.time() - start_time))