def METHOD_NAME(task_args):
    task_id, key_list, [speech_dict, ref_speech_dict], args = task_args
    if args.gpu_inference:
        device = args.gpu_devices[task_id % len(args.gpu_devices)]
        torch.cuda.set_device("cuda:".format(device))
    inference_func = inference_modelscope(
        output_dir=None,
        batch_size=1,
        dtype="float32",
        ngpu=1 if args.gpu_inference else 0,
        seed=0,
        num_workers=0,
        log_level=logging.INFO,
        key_file=None,
        sv_train_config="sv.yaml",
        sv_model_file="sv.pb",
        model_tag=None,
        allow_variable_data_keys=True,
        streaming=False,
        embedding_node="resnet1_dense",
        sv_threshold=0.9465,
    )
    results = {}
    for key in key_list:
        speech = soundfile.read(speech_dict[key])[0]
        ref_speech = soundfile.read(ref_speech_dict[key])[0]
        ret = inference_func(None, (speech, ref_speech))
        results[key] = ret["value"]
    return results