funasr pages

游雁

2023-11-16 4ace5a95b052d338947fc88809a440ccd55cf6b4

 funasr/tasks/asr.py

@@ -13,8 +13,6 @@
import numpy as np
import torch
import yaml
from typeguard import check_argument_types
from typeguard import check_return_type

from funasr.datasets.collate_fn import CommonCollateFn
from funasr.datasets.preprocessor import CommonPreprocessor
@@ -38,18 +36,26 @@
from funasr.models.decoder.transformer_decoder import ParaformerDecoderSAN
from funasr.models.decoder.transformer_decoder import TransformerDecoder
from funasr.models.decoder.contextual_decoder import ContextualParaformerDecoder
from funasr.models.e2e_asr import ESPnetASRModel
from funasr.models.e2e_asr_paraformer import Paraformer, ParaformerBert, BiCifParaformer, ContextualParaformer
from funasr.models.decoder.transformer_decoder import SAAsrTransformerDecoder
from funasr.models.e2e_asr import ASRModel
from funasr.models.decoder.rnnt_decoder import RNNTDecoder
from funasr.models.joint_net.joint_network import JointNetwork
from funasr.models.e2e_asr_paraformer import Paraformer, ParaformerOnline, ParaformerBert, BiCifParaformer, ContextualParaformer
from funasr.models.e2e_asr_contextual_paraformer import NeatContextualParaformer
from funasr.models.e2e_tp import TimestampPredictor
from funasr.models.e2e_asr_mfcca import MFCCA
from funasr.models.e2e_sa_asr import SAASRModel
from funasr.models.e2e_uni_asr import UniASR
from funasr.models.e2e_asr_transducer import TransducerModel, UnifiedTransducerModel
from funasr.models.e2e_asr_bat import BATModel
from funasr.models.encoder.abs_encoder import AbsEncoder
from funasr.models.encoder.conformer_encoder import ConformerEncoder
from funasr.models.encoder.conformer_encoder import ConformerEncoder, ConformerChunkEncoder
from funasr.models.encoder.data2vec_encoder import Data2VecEncoder
from funasr.models.encoder.rnn_encoder import RNNEncoder
from funasr.models.encoder.sanm_encoder import SANMEncoder, SANMEncoderChunkOpt
from funasr.models.encoder.transformer_encoder import TransformerEncoder
from funasr.models.encoder.mfcca_encoder import MFCCAEncoder
from funasr.models.encoder.resnet34_encoder import ResNet34Diar
from funasr.models.frontend.abs_frontend import AbsFrontend
from funasr.models.frontend.default import DefaultFrontend
from funasr.models.frontend.default import MultiChannelFrontend
@@ -61,7 +67,7 @@
from funasr.models.postencoder.hugging_face_transformers_postencoder import (
    HuggingFaceTransformersPostEncoder,  # noqa: H301
)
from funasr.models.predictor.cif import CifPredictor, CifPredictorV2, CifPredictorV3
from funasr.models.predictor.cif import CifPredictor, CifPredictorV2, CifPredictorV3, BATPredictor
from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
from funasr.models.preencoder.linear import LinearProjection
from funasr.models.preencoder.sinc import LightweightSincConvs
@@ -72,7 +78,7 @@
from funasr.tasks.abs_task import AbsTask
from funasr.text.phoneme_tokenizer import g2p_choices
from funasr.torch_utils.initialize import initialize
from funasr.train.abs_espnet_model import AbsESPnetModel
from funasr.models.base_model import FunASRModel
from funasr.train.class_choices import ClassChoices
from funasr.train.trainer import Trainer
from funasr.utils.get_default_kwargs import get_default_kwargs
@@ -118,16 +124,22 @@
model_choices = ClassChoices(
    "model",
    classes=dict(
        asr=ESPnetASRModel,
        asr=ASRModel,
        uniasr=UniASR,
        paraformer=Paraformer,
        paraformer_online=ParaformerOnline,
        paraformer_bert=ParaformerBert,
        bicif_paraformer=BiCifParaformer,
        contextual_paraformer=ContextualParaformer,
        neatcontextual_paraformer=NeatContextualParaformer,
        mfcca=MFCCA,
        timestamp_prediction=TimestampPredictor,
        rnnt=TransducerModel,
        rnnt_unified=UnifiedTransducerModel,
        bat=BATModel,
        sa_asr=SAASRModel,
    ),
    type_check=AbsESPnetModel,
    type_check=FunASRModel,
    default="asr",
)
preencoder_choices = ClassChoices(
@@ -150,6 +162,7 @@
        sanm_chunk_opt=SANMEncoderChunkOpt,
        data2vec_encoder=Data2VecEncoder,
        mfcca_enc=MFCCAEncoder,
        chunk_conformer=ConformerChunkEncoder,
    ),
    type_check=AbsEncoder,
    default="rnn",
@@ -165,6 +178,27 @@
    ),
    type_check=AbsEncoder,
    default="rnn",
)
asr_encoder_choices = ClassChoices(
    "asr_encoder",
    classes=dict(
        conformer=ConformerEncoder,
        transformer=TransformerEncoder,
        rnn=RNNEncoder,
        sanm=SANMEncoder,
        sanm_chunk_opt=SANMEncoderChunkOpt,
        data2vec_encoder=Data2VecEncoder,
        mfcca_enc=MFCCAEncoder,
    ),
    type_check=AbsEncoder,
    default="rnn",
)
spk_encoder_choices = ClassChoices(
    "spk_encoder",
    classes=dict(
        resnet34_diar=ResNet34Diar,
    ),
    default="resnet34_diar",
)
postencoder_choices = ClassChoices(
    name="postencoder",
@@ -188,6 +222,7 @@
        paraformer_decoder_sanm=ParaformerSANMDecoder,
        paraformer_decoder_san=ParaformerDecoderSAN,
        contextual_paraformer_decoder=ContextualParaformerDecoder,
        sa_decoder=SAAsrTransformerDecoder,
    ),
    type_check=AbsDecoder,
    default="rnn",
@@ -207,6 +242,25 @@
    type_check=AbsDecoder,
    default="rnn",
)

rnnt_decoder_choices = ClassChoices(
    "rnnt_decoder",
    classes=dict(
        rnnt=RNNTDecoder,
    ),
    type_check=RNNTDecoder,
    default="rnnt",
)

joint_network_choices = ClassChoices(
    name="joint_network",
    classes=dict(
        joint_network=JointNetwork,
    ),
    default="joint_network",
    optional=True,
)

predictor_choices = ClassChoices(
    name="predictor",
    classes=dict(
@@ -214,6 +268,7 @@
        ctc_predictor=None,
        cif_predictor_v2=CifPredictorV2,
        cif_predictor_v3=CifPredictorV3,
        bat_predictor=BATPredictor,
    ),
    type_check=None,
    default="cif_predictor",
@@ -263,6 +318,18 @@
        postencoder_choices,
        # --decoder and --decoder_conf
        decoder_choices,
        # --predictor and --predictor_conf
        predictor_choices,
        # --encoder2 and --encoder2_conf
        encoder_choices2,
        # --decoder2 and --decoder2_conf
        decoder_choices2,
        # --predictor2 and --predictor2_conf
        predictor_choices2,
        # --stride_conv and --stride_conv_conf
        stride_conv_choices,
        # --rnnt_decoder and --rnnt_decoder_conf
        rnnt_decoder_choices,
    ]

    # If you need to modify train() or eval() procedures, change Trainer class here
@@ -288,6 +355,12 @@
            type=str2bool,
            default=True,
            help="whether to split text using <space>",
        )
        group.add_argument(
            "--max_spk_num",
            type=int_or_none,
            default=None,
            help="A text mapping int-id to token",
        )
        group.add_argument(
            "--seg_dict_file",
@@ -322,12 +395,6 @@
            action=NestedDictAction,
            default=get_default_kwargs(CTC),
            help="The keyword arguments for CTC class.",
        )
        group.add_argument(
            "--joint_net_conf",
            action=NestedDictAction,
            default=None,
            help="The keyword arguments for joint network class.",
        )

        group = parser.add_argument_group(description="Preprocess related")
@@ -425,7 +492,6 @@
        [Collection[Tuple[str, Dict[str, np.ndarray]]]],
        Tuple[List[str], Dict[str, torch.Tensor]],
    ]:
        assert check_argument_types()
        # NOTE(kamo): int value = 0 is reserved by CTC-blank symbol
        return CommonCollateFn(float_pad_value=0.0, int_pad_value=-1)

@@ -433,14 +499,13 @@
    def build_preprocess_fn(
            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=args.bpemodel,
                non_linguistic_symbols=args.non_linguistic_symbols,
                non_linguistic_symbols=args.non_linguistic_symbols if hasattr(args, "non_linguistic_symbols") else None,
                text_cleaner=args.cleaner,
                g2p_type=args.g2p,
                split_with_space=args.split_with_space if hasattr(args, "split_with_space") else False,
@@ -463,7 +528,6 @@
            )
        else:
            retval = None
        assert check_return_type(retval)
        return retval

    @classmethod
@@ -482,12 +546,10 @@
            cls, train: bool = True, inference: bool = False
    ) -> Tuple[str, ...]:
        retval = ()
        assert check_return_type(retval)
        return retval

    @classmethod
    def build_model(cls, args: argparse.Namespace):
        assert check_argument_types()
        if isinstance(args.token_list, str):
            with open(args.token_list, encoding="utf-8") as f:
                token_list = [line.rstrip() for line in f]
@@ -592,7 +654,6 @@
        if args.init is not None:
            initialize(model, args.init)

        assert check_return_type(model)
        return model


@@ -635,7 +696,6 @@

    @classmethod
    def build_model(cls, args: argparse.Namespace):
        assert check_argument_types()
        if isinstance(args.token_list, str):
            with open(args.token_list, encoding="utf-8") as f:
                token_list = [line.rstrip() for line in f]
@@ -772,7 +832,6 @@
        if args.init is not None:
            initialize(model, args.init)

        assert check_return_type(model)
        return model

    # ~~~~~~~~~ The methods below are mainly used for inference ~~~~~~~~~
@@ -794,7 +853,6 @@
            device: Device type, "cpu", "cuda", or "cuda:N".

        """
        assert check_argument_types()
        if config_file is None:
            assert model_file is not None, (
                "The argument 'model_file' must be provided "
@@ -810,9 +868,9 @@
            args["cmvn_file"] = cmvn_file
        args = argparse.Namespace(**args)
        model = cls.build_model(args)
        if not isinstance(model, AbsESPnetModel):
        if not isinstance(model, FunASRModel):
            raise RuntimeError(
                f"model must inherit {AbsESPnetModel.__name__}, but got {type(model)}"
                f"model must inherit {FunASRModel.__name__}, but got {type(model)}"
            )
        model.to(device)
        model_dict = dict()
@@ -882,34 +940,33 @@
    # If you need more than one optimizers, change this value
    num_optimizers: int = 1

    # Add variable objects configurations
    class_choices_list = [
        # --frontend and --frontend_conf
        frontend_choices,
        # --specaug and --specaug_conf
        specaug_choices,
        # --normalize and --normalize_conf
        normalize_choices,
        # --model and --model_conf
        model_choices,
        # --preencoder and --preencoder_conf
        preencoder_choices,
        # --encoder and --encoder_conf
        encoder_choices,
        # --postencoder and --postencoder_conf
        postencoder_choices,
        # --decoder and --decoder_conf
        decoder_choices,
        # --predictor and --predictor_conf
        predictor_choices,
    ]
    # # Add variable objects configurations
    # class_choices_list = [
    #     # --frontend and --frontend_conf
    #     frontend_choices,
    #     # --specaug and --specaug_conf
    #     specaug_choices,
    #     # --normalize and --normalize_conf
    #     normalize_choices,
    #     # --model and --model_conf
    #     model_choices,
    #     # --preencoder and --preencoder_conf
    #     preencoder_choices,
    #     # --encoder and --encoder_conf
    #     encoder_choices,
    #     # --postencoder and --postencoder_conf
    #     postencoder_choices,
    #     # --decoder and --decoder_conf
    #     decoder_choices,
    #     # --predictor and --predictor_conf
    #     predictor_choices,
    # ]

    # If you need to modify train() or eval() procedures, change Trainer class here
    trainer = Trainer

    @classmethod
    def build_model(cls, args: argparse.Namespace):
        assert check_argument_types()
        if isinstance(args.token_list, str):
            with open(args.token_list, encoding="utf-8") as f:
                token_list = [line.rstrip() for line in f]
@@ -1019,7 +1076,6 @@
        if args.init is not None:
            initialize(model, args.init)

        assert check_return_type(model)
        return model

    # ~~~~~~~~~ The methods below are mainly used for inference ~~~~~~~~~
@@ -1041,7 +1097,6 @@
            device: Device type, "cpu", "cuda", or "cuda:N".

        """
        assert check_argument_types()
        if config_file is None:
            assert model_file is not None, (
                "The argument 'model_file' must be provided "
@@ -1057,9 +1112,9 @@
            args["cmvn_file"] = cmvn_file
        args = argparse.Namespace(**args)
        model = cls.build_model(args)
        if not isinstance(model, AbsESPnetModel):
        if not isinstance(model, FunASRModel):
            raise RuntimeError(
                f"model must inherit {AbsESPnetModel.__name__}, but got {type(model)}"
                f"model must inherit {FunASRModel.__name__}, but got {type(model)}"
            )
        model.to(device)
        model_dict = dict()
@@ -1144,7 +1199,6 @@

    @classmethod
    def build_model(cls, args: argparse.Namespace):
        assert check_argument_types()
        if isinstance(args.token_list, str):
            with open(args.token_list, encoding="utf-8") as f:
                token_list = [line.rstrip() for line in f]
@@ -1242,7 +1296,6 @@
        if args.init is not None:
            initialize(model, args.init)

        assert check_return_type(model)
        return model


@@ -1267,7 +1320,6 @@

    @classmethod
    def build_model(cls, args: argparse.Namespace):
        assert check_argument_types()
        if isinstance(args.token_list, str):
            with open(args.token_list, encoding="utf-8") as f:
                token_list = [line.rstrip() for line in f]
@@ -1322,7 +1374,6 @@
        if args.init is not None:
            initialize(model, args.init)

        assert check_return_type(model)
        return model

    @classmethod
@@ -1331,3 +1382,381 @@
    ) -> Tuple[str, ...]:
        retval = ("speech", "text")
        return retval


class ASRTransducerTask(ASRTask):
    """ASR Transducer Task definition."""

    num_optimizers: int = 1

    class_choices_list = [
        model_choices,
        frontend_choices,
        specaug_choices,
        normalize_choices,
        encoder_choices,
        rnnt_decoder_choices,
        joint_network_choices,
    ]

    trainer = Trainer

    @classmethod
    def build_model(cls, args: argparse.Namespace) -> TransducerModel:
        """Required data depending on task mode.
        Args:
            cls: ASRTransducerTask object.
            args: Task arguments.
        Return:
            model: ASR Transducer model.
        """

        if isinstance(args.token_list, str):
            with open(args.token_list, encoding="utf-8") as f:
                token_list = [line.rstrip() for line in f]

            # Overwriting token_list to keep it as "portable".
            args.token_list = list(token_list)
        elif isinstance(args.token_list, (tuple, list)):
            token_list = list(args.token_list)
        else:
            raise RuntimeError("token_list must be str or list")
        vocab_size = len(token_list)
        logging.info(f"Vocabulary size: {vocab_size }")

        # 1. frontend
        if args.input_size is None:
            # Extract features in the model
            frontend_class = frontend_choices.get_class(args.frontend)
            frontend = frontend_class(**args.frontend_conf)
            input_size = frontend.output_size()
        else:
            # Give features from data-loader
            frontend = None
            input_size = args.input_size

        # 2. Data augmentation for spectrogram
        if args.specaug is not None:
            specaug_class = specaug_choices.get_class(args.specaug)
            specaug = specaug_class(**args.specaug_conf)
        else:
            specaug = None

        # 3. Normalization layer
        if args.normalize is not None:
            normalize_class = normalize_choices.get_class(args.normalize)
            normalize = normalize_class(**args.normalize_conf)
        else:
            normalize = None

        # 4. Encoder
        if getattr(args, "encoder", None) is not None:
            encoder_class = encoder_choices.get_class(args.encoder)
            encoder = encoder_class(input_size, **args.encoder_conf)
        else:
            encoder = Encoder(input_size, **args.encoder_conf)
        encoder_output_size = encoder.output_size()

        # 5. Decoder
        rnnt_decoder_class = rnnt_decoder_choices.get_class(args.rnnt_decoder)
        decoder = rnnt_decoder_class(
            vocab_size,
            **args.rnnt_decoder_conf,
        )
        decoder_output_size = decoder.output_size

        if getattr(args, "decoder", None) is not None:
            att_decoder_class = decoder_choices.get_class(args.decoder)

            att_decoder = att_decoder_class(
                vocab_size=vocab_size,
                encoder_output_size=encoder_output_size,
                **args.decoder_conf,
            )
        else:
            att_decoder = None
        # 6. Joint Network
        joint_network = JointNetwork(
            vocab_size,
            encoder_output_size,
            decoder_output_size,
            **args.joint_network_conf,
        )

        # 7. Build model
        try:
            model_class = model_choices.get_class(args.model)
        except AttributeError:
            model_class = model_choices.get_class("rnnt_unified")

        model = model_class(
            vocab_size=vocab_size,
            token_list=token_list,
            frontend=frontend,
            specaug=specaug,
            normalize=normalize,
            encoder=encoder,
            decoder=decoder,
            att_decoder=att_decoder,
            joint_network=joint_network,
            **args.model_conf,
        )
        # 8. Initialize model
        if args.init is not None:
            raise NotImplementedError(
                "Currently not supported.",
                "Initialization part will be reworked in a short future.",
            )


        return model

class ASRBATTask(ASRTask):
    """ASR Boundary Aware Transducer Task definition."""

    num_optimizers: int = 1

    class_choices_list = [
        model_choices,
        frontend_choices,
        specaug_choices,
        normalize_choices,
        encoder_choices,
        rnnt_decoder_choices,
        joint_network_choices,
        predictor_choices,
    ]

    trainer = Trainer

    @classmethod
    def build_model(cls, args: argparse.Namespace) -> BATModel:
        """Required data depending on task mode.
        Args:
            cls: ASRBATTask object.
            args: Task arguments.
        Return:
            model: ASR BAT model.
        """

        if isinstance(args.token_list, str):
            with open(args.token_list, encoding="utf-8") as f:
                token_list = [line.rstrip() for line in f]

            # Overwriting token_list to keep it as "portable".
            args.token_list = list(token_list)
        elif isinstance(args.token_list, (tuple, list)):
            token_list = list(args.token_list)
        else:
            raise RuntimeError("token_list must be str or list")
        vocab_size = len(token_list)
        logging.info(f"Vocabulary size: {vocab_size }")

        # 1. frontend
        if args.input_size is None:
            # Extract features in the model
            frontend_class = frontend_choices.get_class(args.frontend)
            frontend = frontend_class(**args.frontend_conf)
            input_size = frontend.output_size()
        else:
            # Give features from data-loader
            frontend = None
            input_size = args.input_size

        # 2. Data augmentation for spectrogram
        if args.specaug is not None:
            specaug_class = specaug_choices.get_class(args.specaug)
            specaug = specaug_class(**args.specaug_conf)
        else:
            specaug = None

        # 3. Normalization layer
        if args.normalize is not None:
            normalize_class = normalize_choices.get_class(args.normalize)
            normalize = normalize_class(**args.normalize_conf)
        else:
            normalize = None

        # 4. Encoder
        if getattr(args, "encoder", None) is not None:
            encoder_class = encoder_choices.get_class(args.encoder)
            encoder = encoder_class(input_size, **args.encoder_conf)
        else:
            encoder = Encoder(input_size, **args.encoder_conf)
        encoder_output_size = encoder.output_size()

        # 5. Decoder
        rnnt_decoder_class = rnnt_decoder_choices.get_class(args.rnnt_decoder)
        decoder = rnnt_decoder_class(
            vocab_size,
            **args.rnnt_decoder_conf,
        )
        decoder_output_size = decoder.output_size

        if getattr(args, "decoder", None) is not None:
            att_decoder_class = decoder_choices.get_class(args.decoder)

            att_decoder = att_decoder_class(
                vocab_size=vocab_size,
                encoder_output_size=encoder_output_size,
                **args.decoder_conf,
            )
        else:
            att_decoder = None
        # 6. Joint Network
        joint_network = JointNetwork(
            vocab_size,
            encoder_output_size,
            decoder_output_size,
            **args.joint_network_conf,
        )

        predictor_class = predictor_choices.get_class(args.predictor)
        predictor = predictor_class(**args.predictor_conf)

        # 7. Build model
        try:
            model_class = model_choices.get_class(args.model)
        except AttributeError:
            model_class = model_choices.get_class("rnnt_unified")

        model = model_class(
            vocab_size=vocab_size,
            token_list=token_list,
            frontend=frontend,
            specaug=specaug,
            normalize=normalize,
            encoder=encoder,
            decoder=decoder,
            att_decoder=att_decoder,
            joint_network=joint_network,
            predictor=predictor,
            **args.model_conf,
        )
        # 8. Initialize model
        if args.init is not None:
            raise NotImplementedError(
                "Currently not supported.",
                "Initialization part will be reworked in a short future.",
            )

        #assert check_return_type(model)

        return model

class ASRTaskSAASR(ASRTask):
    # If you need more than one optimizers, change this value
    num_optimizers: int = 1

    # Add variable objects configurations
    class_choices_list = [
        # --frontend and --frontend_conf
        frontend_choices,
        # --specaug and --specaug_conf
        specaug_choices,
        # --normalize and --normalize_conf
        normalize_choices,
        # --model and --model_conf
        model_choices,
        # --preencoder and --preencoder_conf
        preencoder_choices,
        # --encoder and --encoder_conf
        # --asr_encoder and --asr_encoder_conf
        asr_encoder_choices,
        # --spk_encoder and --spk_encoder_conf
        spk_encoder_choices,
        # --decoder and --decoder_conf
        decoder_choices,
    ]

    # If you need to modify train() or eval() procedures, change Trainer class here
    trainer = Trainer

    @classmethod
    def build_model(cls, args: argparse.Namespace):
        if isinstance(args.token_list, str):
            with open(args.token_list, encoding="utf-8") as f:
                token_list = [line.rstrip() for line in f]

            # Overwriting token_list to keep it as "portable".
            args.token_list = list(token_list)
        elif isinstance(args.token_list, (tuple, list)):
            token_list = list(args.token_list)
        else:
            raise RuntimeError("token_list must be str or list")
        vocab_size = len(token_list)
        logging.info(f"Vocabulary size: {vocab_size}")

        # 1. frontend
        if args.input_size is None:
            # Extract features in the model
            frontend_class = frontend_choices.get_class(args.frontend)
            if args.frontend == 'wav_frontend' or args.frontend == "multichannelfrontend":
                frontend = frontend_class(cmvn_file=args.cmvn_file, **args.frontend_conf)
            else:
                frontend = frontend_class(**args.frontend_conf)
            input_size = frontend.output_size()
        else:
            # Give features from data-loader
            args.frontend = None
            args.frontend_conf = {}
            frontend = None
            input_size = args.input_size

        # 2. Data augmentation for spectrogram
        if args.specaug is not None:
            specaug_class = specaug_choices.get_class(args.specaug)
            specaug = specaug_class(**args.specaug_conf)
        else:
            specaug = None

        # 3. Normalization layer
        if args.normalize is not None:
            normalize_class = normalize_choices.get_class(args.normalize)
            normalize = normalize_class(**args.normalize_conf)
        else:
            normalize = None

        # 5. Encoder
        asr_encoder_class = asr_encoder_choices.get_class(args.asr_encoder)
        asr_encoder = asr_encoder_class(input_size=input_size, **args.asr_encoder_conf)
        spk_encoder_class = spk_encoder_choices.get_class(args.spk_encoder)
        spk_encoder = spk_encoder_class(input_size=input_size, **args.spk_encoder_conf)

        # 7. Decoder
        decoder_class = decoder_choices.get_class(args.decoder)
        decoder = decoder_class(
            vocab_size=vocab_size,
            encoder_output_size=asr_encoder.output_size(),
            **args.decoder_conf,
        )

        # 8. CTC
        ctc = CTC(
            odim=vocab_size, encoder_output_size=asr_encoder.output_size(), **args.ctc_conf
        )

        # import ipdb;ipdb.set_trace()
        # 9. Build model
        try:
            model_class = model_choices.get_class(args.model)
        except AttributeError:
            model_class = model_choices.get_class("asr")
        model = model_class(
            vocab_size=vocab_size,
            frontend=frontend,
            specaug=specaug,
            normalize=normalize,
            asr_encoder=asr_encoder,
            spk_encoder=spk_encoder,
            decoder=decoder,
            ctc=ctc,
            token_list=token_list,
            **args.model_conf,
        )

        # 10. Initialize
        if args.init is not None:
            initialize(model, args.init)

        return model