def METHOD_NAME(path='RAVDESS/', sr=22050, n_mfcc=10, preprocess=False,
                                    target_time=3.6, input_fps=30, save_frames=15, target_im_size=224, device='cpu'):
    if preprocess:
        preprocess_ravdess(path, sr, n_mfcc, target_time, input_fps, save_frames, target_im_size, device)
    annot_path = os.path.join(path, 'annotations.txt')
    train_annots, val_annots, test_annots = parse_annotations(path, annot_path)
    video_scale = 255
    video_train_transform = transforms.Compose([
                transforms.RandomHorizontalFlip(),
                transforms.RandomRotate(),
                transforms.ToTensor(video_scale)])
    video_val_transform = transforms.Compose([
                transforms.ToTensor(video_scale)])
    train_set = RavdessDataset(train_annots, video_train_transform, sr=sr, n_mfcc=n_mfcc)
    val_set = RavdessDataset(val_annots, video_val_transform, sr=sr, n_mfcc=n_mfcc)
    test_set = RavdessDataset(test_annots, video_val_transform, sr=sr, n_mfcc=n_mfcc)
    return train_set, val_set, test_set