| | |
| | | token_list=token_list, |
| | | **args.model_conf, |
| | | ) |
| | | elif args.model == "paraformer": |
| | | elif args.model in ["paraformer", "paraformer_bert", "bicif_paraformer", "contextual_paraformer"]: |
| | | # predictor |
| | | predictor_class = predictor_choices.get_class(args.predictor) |
| | | predictor = predictor_class(**args.predictor_conf) |
| | |
| | | stride_conv=stride_conv, |
| | | **args.model_conf, |
| | | ) |
| | | |
| | | elif args.model == "timestamp_prediction": |
| | | model_class = model_choices.get_class(args.model) |
| | | model = model_class( |
| | | frontend=frontend, |
| | | encoder=encoder, |
| | | token_list=token_list, |
| | | **args.model_conf, |
| | | ) |
| | | else: |
| | | raise NotImplementedError("Not supported model: {}".format(args.model)) |
| | | |