def METHOD_NAME(
    data_folder,
    save_json_train,
    save_json_valid,
    save_json_test,
    sample_rate,
    split_ratio=[80, 10, 10],
    libritts_subsets=["train-clean-100"],
):
    """
    Prepares the json files for the LibriTTS dataset.
    Downloads the dataset if it is not found in the `data_folder` as expected.
    Arguments
    ---------
    data_folder : str
        Path to the folder where the LibriTTS dataset is stored.
    save_json_train : str
        Path where the train data specification file will be saved.
    save_json_valid : str
        Path where the validation data specification file will be saved.
    save_json_test : str
        Path where the test data specification file will be saved.
    split_ratio : list
        List composed of three integers that sets split ratios for train, valid,
        and test sets, respectively. For instance split_ratio=[80, 10, 10] will
        assign 80% of the sentences to training, 10% for validation, and 10%
        for test.
    sample_rate : int
        The sample rate to be used for the dataset
    libritts_subsets: list
        List of librispeech subsets to use (e.g., dev-clean, train-clean-100, ...).
    Example
    -------
    >>> data_folder = '/path/to/LibriTTS'
    >>> prepare_libritts(data_folder, 'train.json', 'valid.json', 'test.json', 2050)
    """
    # Checks if this phase is already done (if so, skips it)
    if skip(save_json_train, save_json_valid, save_json_test):
        logger.info("Preparation completed in previous run, skipping.")
        return
    extension = [".wav"]  # The expected extension for audio files
    wav_list = list()  # Stores all audio file paths for the dataset
    # For every subset of the dataset, if it doesn't exist, downloads it and sets flag to resample the subset
    for subset_name in libritts_subsets:
        subset_folder = os.path.join(data_folder, subset_name)
        subset_archive = os.path.join(subset_folder, subset_name + ".tar.gz")
        subset_data = os.path.join(subset_folder, "LibriTTS")
        if not check_folders(subset_data):
            logger.info(
                f"No data found for {subset_name}. Checking for an archive file."
            )
            if not os.path.isfile(subset_archive):
                logger.info(
                    f"No archive file found for {subset_name}. Downloading and unpacking."
                )
                subset_url = LIBRITTS_URL_PREFIX + subset_name + ".tar.gz"
                download_file(subset_url, subset_archive)
                logger.info(f"Downloaded data for subset {subset_name}.")
            else:
                logger.info(
                    f"Found an archive file for {subset_name}. Unpacking."
                )
            shutil.unpack_archive(subset_archive, subset_folder)
        # Collects all files matching the provided extension
        wav_list.extend(get_all_files(subset_folder, match_and=extension))
    logger.info(
        f"Creating {save_json_train}, {save_json_valid}, and {save_json_test}"
    )
    # Random split the signal list into train, valid, and test sets.
    data_split = split_sets(wav_list, split_ratio)
    # Creating json files
    create_json(data_split["train"], save_json_train, sample_rate)
    create_json(data_split["valid"], save_json_valid, sample_rate)
    create_json(data_split["test"], save_json_test, sample_rate)