Hi all,
I divide the training, validation and testing in my custom dataset as follows:
total_dataset = speechdataset.SpanishSpeechDataSet(csv_files=csv_files, root_dir=root_dir)
train_size = int(0.8 * len(total_dataset))
val_test_size = len(total_dataset) - train_size
train_dataset, val_test_dataset = torch.utils.data.random_split(total_dataset, [train_size, val_test_size])
valid_size = int(0.9 * len(val_test_dataset))
test_size = len(val_test_dataset) - valid_size
val_dataset, test_dataset = torch.utils.data.random_split(val_test_dataset, [valid_size, test_size])

In this case, I would like to inject noise to the training dataset only, how can I do that? Because I can’t pass the type of data=“train” or “valid” like normally done…
Thanks.
BR,
Shweta.