modify diar pipeline

志浩

2023-03-16 49ded3a686daa816a58376fa67b8df782ffba312

 funasr/tasks/diar.py

@@ -499,7 +499,7 @@
            config_file: Union[Path, str] = None,
            model_file: Union[Path, str] = None,
            cmvn_file: Union[Path, str] = None,
            device: str = "cpu",
            device: Union[str, torch.device] = "cpu",
    ):
        """Build model from the files.

@@ -554,12 +554,27 @@
                model.load_state_dict(model_dict)
            else:
                model_dict = torch.load(model_file, map_location=device)
        model_dict = cls.fileter_model_dict(model_dict, model.state_dict())
        model.load_state_dict(model_dict)
        if model_name_pth is not None and not os.path.exists(model_name_pth):
            torch.save(model_dict, model_name_pth)
            logging.info("model_file is saved to pth: {}".format(model_name_pth))

        return model, args

    @classmethod
    def fileter_model_dict(cls, src_dict: dict, dest_dict: dict):
        from collections import OrderedDict
        new_dict = OrderedDict()
        for key, value in src_dict.items():
            if key in dest_dict:
                new_dict[key] = value
            else:
                logging.info("{} is no longer needed in this model.".format(key))
        for key, value in dest_dict.items():
            if key not in new_dict:
                logging.warning("{} is missed in checkpoint.".format(key))
        return new_dict

    @classmethod
    def convert_tf2torch(
@@ -573,19 +588,24 @@
        var_dict_torch = model.state_dict()
        var_dict_torch_update = dict()
        # speech encoder
        var_dict_torch_update_local = model.encoder.convert_tf2torch(var_dict_tf, var_dict_torch)
        var_dict_torch_update.update(var_dict_torch_update_local)
        if model.encoder is not None:
            var_dict_torch_update_local = model.encoder.convert_tf2torch(var_dict_tf, var_dict_torch)
            var_dict_torch_update.update(var_dict_torch_update_local)
        # speaker encoder
        var_dict_torch_update_local = model.speaker_encoder.convert_tf2torch(var_dict_tf, var_dict_torch)
        var_dict_torch_update.update(var_dict_torch_update_local)
        if model.speaker_encoder is not None:
            var_dict_torch_update_local = model.speaker_encoder.convert_tf2torch(var_dict_tf, var_dict_torch)
            var_dict_torch_update.update(var_dict_torch_update_local)
        # cd scorer
        var_dict_torch_update_local = model.cd_scorer.convert_tf2torch(var_dict_tf, var_dict_torch)
        var_dict_torch_update.update(var_dict_torch_update_local)
        if model.cd_scorer is not None:
            var_dict_torch_update_local = model.cd_scorer.convert_tf2torch(var_dict_tf, var_dict_torch)
            var_dict_torch_update.update(var_dict_torch_update_local)
        # ci scorer
        var_dict_torch_update_local = model.ci_scorer.convert_tf2torch(var_dict_tf, var_dict_torch)
        var_dict_torch_update.update(var_dict_torch_update_local)
        if model.ci_scorer is not None:
            var_dict_torch_update_local = model.ci_scorer.convert_tf2torch(var_dict_tf, var_dict_torch)
            var_dict_torch_update.update(var_dict_torch_update_local)
        # decoder
        var_dict_torch_update_local = model.decoder.convert_tf2torch(var_dict_tf, var_dict_torch)
        var_dict_torch_update.update(var_dict_torch_update_local)
        if model.decoder is not None:
            var_dict_torch_update_local = model.decoder.convert_tf2torch(var_dict_tf, var_dict_torch)
            var_dict_torch_update.update(var_dict_torch_update_local)

        return var_dict_torch_update