fixbug for sd and sv

志浩

2023-02-27 267dddcdbb8a9200ebda5a673905d0bb512de80e

fixbug for sd and sv

 funasr/bin/diar_inference_launch.py

@@ -135,8 +135,10 @@
            "sv_train_config": "sv.yaml",
            "sv_model_file": "sv.pth",
        }
        if "param_dict" in kwargs:
            kwargs["param_dict"].update(param_dict)
        if "param_dict" in kwargs and kwargs["param_dict"] is not None:
            for key in param_dict:
                if key not in kwargs["param_dict"]:
                    kwargs["param_dict"][key] = param_dict[key]
        else:
            kwargs["param_dict"] = param_dict
        return inference_modelscope(**kwargs)

 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
@@ -257,6 +259,9 @@
        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,
@@ -320,7 +325,9 @@
                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