def METHOD_NAME(data_folder, save_folder, skip_prep=False):
    """
    Prepares the csv files for the CommonLanguage dataset for LID.
    Download: https://www.dropbox.com/s/qqpmqay3q9xb1vf/common_voice_kpd.tar.gz?dl=0
    Arguments
    ---------
    data_folder : str
        Path to the folder where the CommonLanguage dataset for LID is stored.
        This path should include the multi: /datasets/CommonLanguage
    save_folder : str
        The directory where to store the csv files.
    max_duration : int, optional
        Max duration (in seconds) of training uterances.
    skip_prep: bool
        If True, skip data preparation.
    Example
    -------
    >>> from recipes.CommonLanguage.common_language_prepare import prepare_common_language
    >>> data_folder = '/datasets/CommonLanguage'
    >>> save_folder = 'exp/CommonLanguage_exp'
    >>> prepare_common_language(\
            data_folder,\
            save_folder,\
            skip_prep=False\
        )
    """
    if skip_prep:
        return
    # Setting the save folder
    os.makedirs(save_folder, exist_ok=True)
    # Setting ouput files
    save_csv_train = os.path.join(save_folder, "train.csv")
    save_csv_dev = os.path.join(save_folder, "dev.csv")
    save_csv_test = os.path.join(save_folder, "test.csv")
    # If csv already exists, we skip the data preparation
    if skip(save_csv_train, save_csv_dev, save_csv_test):
        csv_exists = " already exists, skipping data preparation!"
        msg = save_csv_train + csv_exists
        logger.info(msg)
        msg = save_csv_dev + csv_exists
        logger.info(msg)
        msg = save_csv_test + csv_exists
        logger.info(msg)
        return
    # Additional checks to make sure the data folder contains Common Language
    check_common_language_folder(data_folder)
    # Audio files extensions
    extension = [".wav"]
    # Create the signal list of train, dev, and test sets.
    data_split = create_sets(data_folder, extension)
    # Creating csv files for training, dev and test data
    create_csv(wav_list=data_split["train"], csv_file=save_csv_train)
    create_csv(wav_list=data_split["dev"], csv_file=save_csv_dev)
    create_csv(wav_list=data_split["test"], csv_file=save_csv_test)