Exemple #1
0
def skip(save_folder, conf, meta_files, opt_file):
    """
    Detects if the AMI data_preparation has been already done.
    If the preparation has been done, we can skip it.

    Returns
    -------
    bool
        if True, the preparation phase can be skipped.
        if False, it must be done.
    """
    # Checking if meta (json) files are available
    skip = True
    for file_path in meta_files:
        if not os.path.isfile(file_path):
            skip = False

    # Checking saved options
    save_opt_file = os.path.join(save_folder, opt_file)
    if skip is True:
        if os.path.isfile(save_opt_file):
            opts_old = load_pkl(save_opt_file)
            if opts_old == conf:
                skip = True
            else:
                skip = False
        else:
            skip = False

    return skip
def skip(splits, save_folder, conf):
    """
    Detect when the librispeech data prep can be skipped.

    Arguments
    ---------
    splits : list
        A list of the splits expected in the preparation.
    save_folder : str
        The location of the seave directory
    conf : dict
        The configuration options to ensure they haven't changed.

    Returns
    -------
    bool
        if True, the preparation phase can be skipped.
        if False, it must be done.
    """

    # Checking csv files
    skip = True

    for split in splits:
        if not os.path.isfile(os.path.join(save_folder, split + ".csv")):
            skip = False

    #  Checking saved options
    save_opt = os.path.join(save_folder, OPT_FILE)
    if skip is True:
        if os.path.isfile(save_opt):
            opts_old = load_pkl(save_opt)
            if opts_old == conf:
                skip = True
            else:
                skip = False
        else:
            skip = False

    return skip
def skip(splits, save_folder, conf):
    """
    Detects if the AMI data_preparation has been already done.
    If the preparation has been done, we can skip it.

    Returns
    -------
    bool
        if True, the preparation phase can be skipped.
        if False, it must be done.
    """
    # Checking csv files
    skip = True

    split_files = {
        "train": TRAIN_CSV,
        "dev": DEV_CSV,
        "eval": EVAL_CSV,
    }
    for split in splits:
        if not os.path.isfile(
                os.path.join(save_folder, "csv", split_files[split])):
            skip = False

    #  Checking saved options
    save_opt = os.path.join(save_folder, OPT_FILE)
    if skip is True:
        if os.path.isfile(save_opt):
            opts_old = load_pkl(save_opt)
            if opts_old == conf:
                skip = True
            else:
                skip = False
        else:
            skip = False

    return skip