def make_parser():
    """Note: As the names indicate use s2x_args(ex:ST, ASR etc) for models with speech input,
    x2s_args for models with speech output(ex:TTS) and mt_args for translation models (ex: mt, T2U etc).
    For direct S2ST models, use x2s_args to provide model details.
    """
    parser = options.get_speech_generation_parser()
    parser.add_argument("--target-is-code", action="store_true", default=False)
    parser.add_argument("--config", type=str)
    parser.add_argument(
        "--model-type",
        default="S2U",
        choices=["S2S", "TTS", "S2UT", "MT", "S2T", "2StageS2ST", "3StageS2ST"],
        help="Choose one of the models. For model inference implementation, refer to core.py",
    )
    parser.add_argument(
        "--dataset-path",
        type=str,
        help="""File to load dataset from. Assumes dataset is a list of samples.
        Each sample is a dict of format {'net_input':{'src_tokens':torch.tenor(),'src_lengths':torch.tensor()}}""",
    )
    parser.add_argument(
        "--dataset-type",
        type=str,
        default="npy",
        choices=["npy", "raw"],
        help="""Type of input dataset file""",
    )
    parser.add_argument(
        "--read-using-sf",
        type=str,
        default=False,
        help="""If sound file should be used to read the raw dataset""",
    )
    parser.add_argument(
        "--dataset-size",
        default=None,
        type=int,
        help="Dataset size to use for benchmarking",
    )
    parser.add_argument(
        "--dump-speech-waveforms-dir",
        default=None,
        type=str,
        help="Directory to dump the speech waveforms computed on the dataset.",
    )
    parser.add_argument(
        "--dump-waveform-file-prefix",
        default="",
        type=str,
        help="File name prefix for the saved speech waveforms",
    )
    parser.add_argument(
        "--feat-dim", default=80, type=int, help="Input feature dimension"
    )
    parser.add_argument(
        "--target-sr",
        default=16000,
        type=int,
        help="Target sample rate for dumping waveforms",
    )

    options.add_generation_args(parser)
    options.get_interactive_generation_parser(parser)
    return parser
Note: As the names indicate use s2x_args(ex:ST, ASR etc) for models with speech input,
    x2s_args for models with speech output(ex:TTS) and mt_args for translation models (ex: mt, T2U etc).
    For direct S2ST models, use x2s_args to provide model details.
    
structured_prompting/fairseq-version/fairseq/examples/speech_to_speech/benchmarking/get_metrics.py