update

speech_asr

2023-03-16 2ba4683eb2ce42eec91250debe88b424cbc2d67f

 funasr/tasks/diar.py

@@ -23,6 +23,7 @@
from funasr.layers.label_aggregation import LabelAggregate
from funasr.layers.utterance_mvn import UtteranceMVN
from funasr.models.e2e_diar_sond import DiarSondModel
from funasr.models.e2e_diar_eend_ola import DiarEENDOLAModel
from funasr.models.encoder.abs_encoder import AbsEncoder
from funasr.models.encoder.conformer_encoder import ConformerEncoder
from funasr.models.encoder.data2vec_encoder import Data2VecEncoder
@@ -103,6 +104,7 @@
    "model",
    classes=dict(
        sond=DiarSondModel,
        eend_ola=DiarEENDOLAModel,
    ),
    type_check=AbsESPnetModel,
    default="sond",
@@ -551,7 +553,7 @@
                if ".bin" in model_name:
                    model_name_pth = os.path.join(model_dir, model_name.replace('.bin', '.pb'))
                else:
                    model_name_pth = os.path.join(model_dir, "{}.pth".format(model_name))
                    model_name_pth = os.path.join(model_dir, "{}.pb".format(model_name))
                if os.path.exists(model_name_pth):
                    logging.info("model_file is load from pth: {}".format(model_name_pth))
                    model_dict = torch.load(model_name_pth, map_location=device)
@@ -748,47 +750,47 @@
            cls, args: argparse.Namespace, train: bool
    ) -> Optional[Callable[[str, Dict[str, np.array]], Dict[str, np.ndarray]]]:
        assert check_argument_types()
        if args.use_preprocessor:
            retval = CommonPreprocessor(
                train=train,
                token_type=args.token_type,
                token_list=args.token_list,
                bpemodel=None,
                non_linguistic_symbols=None,
                text_cleaner=None,
                g2p_type=None,
                split_with_space=args.split_with_space if hasattr(args, "split_with_space") else False,
                seg_dict_file=args.seg_dict_file if hasattr(args, "seg_dict_file") else None,
                # NOTE(kamo): Check attribute existence for backward compatibility
                rir_scp=args.rir_scp if hasattr(args, "rir_scp") else None,
                rir_apply_prob=args.rir_apply_prob
                if hasattr(args, "rir_apply_prob")
                else 1.0,
                noise_scp=args.noise_scp if hasattr(args, "noise_scp") else None,
                noise_apply_prob=args.noise_apply_prob
                if hasattr(args, "noise_apply_prob")
                else 1.0,
                noise_db_range=args.noise_db_range
                if hasattr(args, "noise_db_range")
                else "13_15",
                speech_volume_normalize=args.speech_volume_normalize
                if hasattr(args, "rir_scp")
                else None,
            )
        else:
            retval = None
        assert check_return_type(retval)
        return retval
        # if args.use_preprocessor:
        #     retval = CommonPreprocessor(
        #         train=train,
        #         token_type=args.token_type,
        #         token_list=args.token_list,
        #         bpemodel=None,
        #         non_linguistic_symbols=None,
        #         text_cleaner=None,
        #         g2p_type=None,
        #         split_with_space=args.split_with_space if hasattr(args, "split_with_space") else False,
        #         seg_dict_file=args.seg_dict_file if hasattr(args, "seg_dict_file") else None,
        #         # NOTE(kamo): Check attribute existence for backward compatibility
        #         rir_scp=args.rir_scp if hasattr(args, "rir_scp") else None,
        #         rir_apply_prob=args.rir_apply_prob
        #         if hasattr(args, "rir_apply_prob")
        #         else 1.0,
        #         noise_scp=args.noise_scp if hasattr(args, "noise_scp") else None,
        #         noise_apply_prob=args.noise_apply_prob
        #         if hasattr(args, "noise_apply_prob")
        #         else 1.0,
        #         noise_db_range=args.noise_db_range
        #         if hasattr(args, "noise_db_range")
        #         else "13_15",
        #         speech_volume_normalize=args.speech_volume_normalize
        #         if hasattr(args, "rir_scp")
        #         else None,
        #     )
        # else:
        #     retval = None
        # assert check_return_type(retval)
        return None

    @classmethod
    def required_data_names(
            cls, train: bool = True, inference: bool = False
    ) -> Tuple[str, ...]:
        if not inference:
            retval = ("speech", "profile", "binary_labels")
            retval = ("speech", )
        else:
            # Recognition mode
            retval = ("speech")
            retval = ("speech", )
        return retval

    @classmethod
@@ -821,7 +823,7 @@

        # 2. Encoder
        encoder_class = encoder_choices.get_class(args.encoder)
        encoder = encoder_class(input_size=input_size, **args.encoder_conf)
        encoder = encoder_class(**args.encoder_conf)

        # 3. EncoderDecoderAttractor
        encoder_decoder_attractor_class = encoder_decoder_attractor_choices.get_class(args.encoder_decoder_attractor)