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游雁

2023-03-13 fc08b62d05723cdc1ce021bb8ba044ca014fb1f7

 funasr/bin/sond_inference.py

@@ -231,6 +231,7 @@
        dur_threshold: int = 10,
        out_format: str = "vad",
        param_dict: Optional[dict] = None,
        mode: str = "sond",
        **kwargs,
):
    assert check_argument_types()
@@ -254,7 +255,7 @@
    set_all_random_seed(seed)

    # 2a. Build speech2xvec [Optional]
    if param_dict is not None and "extract_profile" in param_dict and param_dict["extract_profile"]:
    if mode == "sond_demo" and param_dict is not None and "extract_profile" in param_dict and param_dict["extract_profile"]:
        assert "sv_train_config" in param_dict, "sv_train_config must be provided param_dict."
        assert "sv_model_file" in param_dict, "sv_model_file must be provided in param_dict."
        sv_train_config = param_dict["sv_train_config"]
@@ -312,13 +313,16 @@

    def _forward(
            data_path_and_name_and_type: Sequence[Tuple[str, str, str]] = None,
            raw_inputs: List[List[Union[np.ndarray, torch.Tensor, str]]] = None,
            raw_inputs: List[List[Union[np.ndarray, torch.Tensor, str, bytes]]] = None,
            output_dir_v2: Optional[str] = None,
            param_dict: Optional[dict] = None,
    ):
        logging.info("param_dict: {}".format(param_dict))
        if data_path_and_name_and_type is None and raw_inputs is not None:
            if isinstance(raw_inputs, (list, tuple)):
                if not isinstance(raw_inputs[0], List):
                    raw_inputs = [raw_inputs]

                assert all([len(example) >= 2 for example in raw_inputs]), \
                    "The length of test case in raw_inputs must larger than 1 (>=2)."