How to use the spleeter.commands.separate.entrypoint function in spleeter

:param argv: Provided command line arguments.
    """
    try:
        parser = create_argument_parser()
        arguments = parser.parse_args(argv[1:])
        enable_logging()
        if arguments.verbose:
            enable_tensorflow_logging()
        if arguments.command == 'separate':
            from .commands.separate import entrypoint
        elif arguments.command == 'train':
            from .commands.train import entrypoint
        elif arguments.command == 'evaluate':
            from .commands.evaluate import entrypoint
        params = load_configuration(arguments.configuration)
        entrypoint(arguments, params)
    except SpleeterError as e:
        get_logger().error(e)

def _separate_evaluation_dataset(arguments, musdb_root_directory, params):
    """ Performs audio separation on the musdb dataset from
    the given directory and params.

    :param arguments: Entrypoint arguments.
    :param musdb_root_directory: Directory to retrieve dataset from.
    :param params: Spleeter configuration to apply to separation.
    :returns: Separation output directory path.
    """
    songs = glob(join(musdb_root_directory, _SPLIT, '*/'))
    mixtures = [join(song, _MIXTURE) for song in songs]
    audio_output_directory = join(
        arguments.output_path,
        _AUDIO_DIRECTORY)
    separate_entrypoint(
        Namespace(
            audio_adapter=arguments.audio_adapter,
            configuration=arguments.configuration,
            inputs=mixtures,
            output_path=join(audio_output_directory, _SPLIT),
            filename_format='{filename}/{instrument}.{codec}',
            codec='wav',
            duration=600.,
            offset=0.,
            bitrate='128k',
            MWF=arguments.MWF,
            verbose=arguments.verbose),
        params)
    return audio_output_directory