rnnt继承ASRTask

aky15

2023-05-17 9d01231fa69672fc7c9b4bf81ef466bb0189788c

 funasr/build_utils/build_asr_model.py

@@ -19,12 +19,15 @@
)
from funasr.models.decoder.transformer_decoder import ParaformerDecoderSAN
from funasr.models.decoder.transformer_decoder import TransformerDecoder
from funasr.models.decoder.rnnt_decoder import RNNTDecoder
from funasr.models.joint_net.joint_network import JointNetwork
from funasr.models.e2e_asr import ASRModel
from funasr.models.e2e_asr_mfcca import MFCCA
from funasr.models.e2e_asr_paraformer import Paraformer, ParaformerBert, BiCifParaformer, ContextualParaformer
from funasr.models.e2e_tp import TimestampPredictor
from funasr.models.e2e_uni_asr import UniASR
from funasr.models.encoder.conformer_encoder import ConformerEncoder
from funasr.models.e2e_asr_transducer import TransducerModel, UnifiedTransducerModel
from funasr.models.encoder.conformer_encoder import ConformerEncoder, ConformerChunkEncoder
from funasr.models.encoder.data2vec_encoder import Data2VecEncoder
from funasr.models.encoder.mfcca_encoder import MFCCAEncoder
from funasr.models.encoder.rnn_encoder import RNNEncoder
@@ -97,6 +100,7 @@
        sanm_chunk_opt=SANMEncoderChunkOpt,
        data2vec_encoder=Data2VecEncoder,
        mfcca_enc=MFCCAEncoder,
        chunk_conformer=ConformerChunkEncoder,
    ),
    default="rnn",
)
@@ -171,6 +175,23 @@
    default="stride_conv1d",
    optional=True,
)
rnnt_decoder_choices = ClassChoices(
    name="rnnt_decoder",
    classes=dict(
        rnnt=RNNTDecoder,
    ),
    default="rnnt",
    optional=True,
)
joint_network_choices = ClassChoices(
    name="joint_network",
    classes=dict(
        joint_network=JointNetwork,
    ),
    default="joint_network",
    optional=True,
)

class_choices_list = [
    # --frontend and --frontend_conf
    frontend_choices,
@@ -194,6 +215,10 @@
    predictor_choices2,
    # --stride_conv and --stride_conv_conf
    stride_conv_choices,
    # --rnnt_decoder and --rnnt_decoder_conf
    rnnt_decoder_choices,
    # --joint_network and --joint_network_conf
    joint_network_choices,
]


@@ -342,6 +367,63 @@
            token_list=token_list,
            **args.model_conf,
        )
    elif args.model == "rnnt":
        # 5. Decoder
        encoder_output_size = encoder.output_size()

        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
        if hasattr(encoder, 'unified_model_training') and encoder.unified_model_training:
            model = UnifiedTransducerModel(
                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,
            )

        else:
            model = TransducerModel(
                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,
            )
    else:
        raise NotImplementedError("Not supported model: {}".format(args.model))

@@ -349,4 +431,4 @@
    if args.init is not None:
        initialize(model, args.init)

    return model
    return model