Merge pull request #159 from alibaba-damo-academy/dev_dzh

zhifu gao

2023-02-27 8cc5bbf99a59694228aafcbe8712e09b9a4cb26b

 funasr/bin/sond_inference.py

@@ -33,6 +33,8 @@
from funasr.utils.types import str_or_none
from scipy.ndimage import median_filter
from funasr.utils.misc import statistic_model_parameters
from funasr.datasets.iterable_dataset import load_bytes


class Speech2Diarization:
    """Speech2Xvector class
@@ -229,6 +231,7 @@
        dur_threshold: int = 10,
        out_format: str = "vad",
        param_dict: Optional[dict] = None,
        mode: str = "sond",
        **kwargs,
):
    assert check_argument_types()
@@ -252,11 +255,14 @@
    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"]
        sv_model_file = param_dict["sv_model_file"]
        if "model_dir" in param_dict:
            sv_train_config = os.path.join(param_dict["model_dir"], sv_train_config)
            sv_model_file = os.path.join(param_dict["model_dir"], sv_model_file)
        from funasr.bin.sv_inference import Speech2Xvector
        speech2xvector_kwargs = dict(
            sv_train_config=sv_train_config,
@@ -307,20 +313,25 @@

    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)."

                def prepare_dataset():
                    for idx, example in enumerate(raw_inputs):
                        # read waveform file
                        example = [soundfile.read(x)[0] if isinstance(example[0], str) else x
                        example = [load_bytes(x) if isinstance(x, bytes) else x
                                   for x in example]
                        example = [soundfile.read(x)[0] if isinstance(x, str) else x
                                   for x in example]
                        # convert torch tensor to numpy array
                        example = [x.numpy() if isinstance(example[0], torch.Tensor) else x