Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add

游雁

2023-09-13 33d3d2084403fd34b79c835d2f2fe04f6cd8f738

 funasr/build_utils/build_asr_model.py

@@ -20,21 +20,27 @@
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.decoder.transformer_decoder import SAAsrTransformerDecoder
from funasr.models.e2e_asr import ASRModel
from funasr.models.e2e_asr_contextual_paraformer import NeatContextualParaformer
from funasr.models.e2e_asr_mfcca import MFCCA

from funasr.models.e2e_asr_transducer import TransducerModel, UnifiedTransducerModel
from funasr.models.e2e_asr_bat import BATModel

from funasr.models.e2e_sa_asr import SAASRModel
from funasr.models.e2e_asr_paraformer import Paraformer, ParaformerOnline, ParaformerBert, BiCifParaformer, ContextualParaformer

from funasr.models.e2e_tp import TimestampPredictor
from funasr.models.e2e_uni_asr import UniASR
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.resnet34_encoder import ResNet34Diar
from funasr.models.encoder.rnn_encoder import RNNEncoder
from funasr.models.encoder.sanm_encoder import SANMEncoder, SANMEncoderChunkOpt
from funasr.models.encoder.branchformer_encoder import BranchformerEncoder
from funasr.models.encoder.e_branchformer_encoder import EBranchformerEncoder
from funasr.models.encoder.transformer_encoder import TransformerEncoder
from funasr.models.frontend.default import DefaultFrontend
from funasr.models.frontend.default import MultiChannelFrontend
@@ -42,7 +48,8 @@
from funasr.models.frontend.s3prl import S3prlFrontend
from funasr.models.frontend.wav_frontend import WavFrontend
from funasr.models.frontend.windowing import SlidingWindow
from funasr.models.predictor.cif import CifPredictor, CifPredictorV2, CifPredictorV3
from funasr.models.joint_net.joint_network import JointNetwork
from funasr.models.predictor.cif import CifPredictor, CifPredictorV2, CifPredictorV3, BATPredictor
from funasr.models.specaug.specaug import SpecAug
from funasr.models.specaug.specaug import SpecAugLFR
from funasr.modules.subsampling import Conv1dSubsampling
@@ -89,12 +96,13 @@
        paraformer_bert=ParaformerBert,
        bicif_paraformer=BiCifParaformer,
        contextual_paraformer=ContextualParaformer,
        neatcontextual_paraformer=NeatContextualParaformer,
        mfcca=MFCCA,
        timestamp_prediction=TimestampPredictor,
        rnnt=TransducerModel,
        rnnt_unified=UnifiedTransducerModel,
        sa_asr=SAASRModel,

        bat=BATModel,
    ),
    default="asr",
)
@@ -107,6 +115,8 @@
        sanm=SANMEncoder,
        sanm_chunk_opt=SANMEncoderChunkOpt,
        data2vec_encoder=Data2VecEncoder,
        branchformer=BranchformerEncoder,
        e_branchformer=EBranchformerEncoder,
        mfcca_enc=MFCCAEncoder,
        chunk_conformer=ConformerChunkEncoder,
    ),
@@ -183,6 +193,7 @@
        ctc_predictor=None,
        cif_predictor_v2=CifPredictorV2,
        cif_predictor_v3=CifPredictorV3,
        bat_predictor=BATPredictor,
    ),
    default="cif_predictor",
    optional=True,
@@ -258,17 +269,22 @@

def build_asr_model(args):
    # token_list
    if args.token_list is not None:
        with open(args.token_list) as f:
    if isinstance(args.token_list, str):
        with open(args.token_list, encoding="utf-8") as f:
            token_list = [line.rstrip() for line in f]
        args.token_list = list(token_list)
        vocab_size = len(token_list)
        logging.info(f"Vocabulary size: {vocab_size}")
    elif isinstance(args.token_list, (tuple, list)):
        token_list = list(args.token_list)
        vocab_size = len(token_list)
        logging.info(f"Vocabulary size: {vocab_size}")
    else:
        token_list = None
        vocab_size = None

    # frontend
    if args.input_size is None:
    if hasattr(args, "input_size") and args.input_size is None:
        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)
@@ -279,7 +295,7 @@
        args.frontend = None
        args.frontend_conf = {}
        frontend = None
        input_size = args.input_size
        input_size = args.input_size if hasattr(args, "input_size") else None

    # data augmentation for spectrogram
    if args.specaug is not None:
@@ -291,7 +307,10 @@
    # normalization layer
    if args.normalize is not None:
        normalize_class = normalize_choices.get_class(args.normalize)
        normalize = normalize_class(**args.normalize_conf)
        if args.model == "mfcca":
            normalize = normalize_class(stats_file=args.cmvn_file, **args.normalize_conf)
        else:
            normalize = normalize_class(**args.normalize_conf)
    else:
        normalize = None

@@ -300,12 +319,15 @@
    encoder = encoder_class(input_size=input_size, **args.encoder_conf)

    # decoder
    decoder_class = decoder_choices.get_class(args.decoder)
    decoder = decoder_class(
        vocab_size=vocab_size,
        encoder_output_size=encoder.output_size(),
        **args.decoder_conf,
    )
    if hasattr(args, "decoder") and args.decoder is not None:
        decoder_class = decoder_choices.get_class(args.decoder)
        decoder = decoder_class(
            vocab_size=vocab_size,
            encoder_output_size=encoder.output_size(),
            **args.decoder_conf,
        )
    else:
        decoder = None

    # ctc
    ctc = CTC(
@@ -325,7 +347,8 @@
            token_list=token_list,
            **args.model_conf,
        )
    elif args.model in ["paraformer", "paraformer_online", "paraformer_bert", "bicif_paraformer", "contextual_paraformer"]:
    elif args.model in ["paraformer", "paraformer_online", "paraformer_bert", "bicif_paraformer",
                        "contextual_paraformer", "neatcontextual_paraformer"]:
        # predictor
        predictor_class = predictor_choices.get_class(args.predictor)
        predictor = predictor_class(**args.predictor_conf)
@@ -394,10 +417,15 @@
            **args.model_conf,
        )
    elif args.model == "timestamp_prediction":
        # predictor
        predictor_class = predictor_choices.get_class(args.predictor)
        predictor = predictor_class(**args.predictor_conf)
        
        model_class = model_choices.get_class(args.model)
        model = model_class(
            frontend=frontend,
            encoder=encoder,
            predictor=predictor,
            token_list=token_list,
            **args.model_conf,
        )
@@ -444,6 +472,53 @@
            joint_network=joint_network,
            **args.model_conf,
        )
    elif args.model == "bat":
        # 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,
        )

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

        model_class = model_choices.get_class(args.model)
        # 7. Build model
        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,
        )
    elif args.model == "sa_asr":
        asr_encoder_class = asr_encoder_choices.get_class(args.asr_encoder)
        asr_encoder = asr_encoder_class(input_size=input_size, **args.asr_encoder_conf)