Thanks a lot for your suggestion. I actually managed to solve the issue by creating a list of tuples of (data_file_path, desired_index, class_label) and then just returned tuple((torch.load(data_file)[desired_index: desired_index + window_size], label)) and used this to generate batches.
For anyone interested this is the class:
class TimeSeriesDataSet(data.Dataset):

    def __init__(self, tensor_dir, transform, window_size, stride):

        self.tensor_directory = tensor_dir
        self.transform = transform
        self.files = os.listdir(tensor_dir)
        self.window_size = window_size
        self.stride = stride
        self.data_tuples = []

        for f in self.files:
            file = os.path.join(tensor_dir, f)
            data, label = torch.load(file)

            # pad with zeros with tensor is not of right length
            if data.size(0) % self.window_size != 0:
                zeros = torch.zeros(abs(self.window_size - (data.size(0) % self.window_size)), data.size(1)).double()
                data = torch.cat((data, zeros), axis=0)

            idxs = [i for i in range(0, data.size(0) - self.window_size, self.stride)]

            if len(idxs) == 0:
                continue

            for j in idxs:
                data_tuple = (file, j, label)
                self.data_tuples.append(data_tuple)

        shuffle(self.data_tuples)

    def __len__(self):
        return len(self.data_tuples)

    def __getitem__(self, idx):

        if torch.is_tensor(idx):
            idx = idx.tolist()

        sample_tuple = self.data_tuples[idx]
        sample, _ = torch.load(sample_tuple[0])
        label = sample_tuple[2]
        sample = sample[sample_tuple[1]: sample_tuple[1] + self.window_size]

        if self.transform:
            sample = self.transform(sample)

        return {'sample': sample, 'label': label}