import logging
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from funasr.layers.global_mvn import GlobalMVN
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from funasr.layers.utterance_mvn import UtteranceMVN
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from funasr.models.ctc import CTC
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from funasr.models.decoder.abs_decoder import AbsDecoder
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from funasr.models.decoder.contextual_decoder import ContextualParaformerDecoder
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from funasr.models.decoder.rnn_decoder import RNNDecoder
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from funasr.models.decoder.sanm_decoder import ParaformerSANMDecoder, FsmnDecoderSCAMAOpt
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from funasr.models.decoder.transformer_decoder import (
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DynamicConvolution2DTransformerDecoder, # noqa: H301
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)
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from funasr.models.decoder.transformer_decoder import DynamicConvolutionTransformerDecoder
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from funasr.models.decoder.transformer_decoder import (
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LightweightConvolution2DTransformerDecoder, # noqa: H301
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)
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from funasr.models.decoder.transformer_decoder import (
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LightweightConvolutionTransformerDecoder, # noqa: H301
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)
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from funasr.models.decoder.transformer_decoder import ParaformerDecoderSAN
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from funasr.models.decoder.transformer_decoder import TransformerDecoder
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from funasr.models.decoder.rnnt_decoder import RNNTDecoder
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from funasr.models.joint_net.joint_network import JointNetwork
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from funasr.models.e2e_asr import ASRModel
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from funasr.models.e2e_asr_mfcca import MFCCA
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from funasr.models.e2e_asr_paraformer import Paraformer, ParaformerBert, BiCifParaformer, ContextualParaformer
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from funasr.models.e2e_tp import TimestampPredictor
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from funasr.models.e2e_uni_asr import UniASR
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from funasr.models.e2e_asr_transducer import TransducerModel, UnifiedTransducerModel
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from funasr.models.encoder.conformer_encoder import ConformerEncoder, ConformerChunkEncoder
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from funasr.models.encoder.data2vec_encoder import Data2VecEncoder
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from funasr.models.encoder.mfcca_encoder import MFCCAEncoder
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from funasr.models.encoder.rnn_encoder import RNNEncoder
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from funasr.models.encoder.sanm_encoder import SANMEncoder, SANMEncoderChunkOpt
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from funasr.models.encoder.transformer_encoder import TransformerEncoder
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from funasr.models.frontend.default import DefaultFrontend
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from funasr.models.frontend.default import MultiChannelFrontend
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from funasr.models.frontend.fused import FusedFrontends
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from funasr.models.frontend.s3prl import S3prlFrontend
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from funasr.models.frontend.wav_frontend import WavFrontend
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from funasr.models.frontend.windowing import SlidingWindow
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from funasr.models.predictor.cif import CifPredictor, CifPredictorV2, CifPredictorV3
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from funasr.models.specaug.specaug import SpecAug
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from funasr.models.specaug.specaug import SpecAugLFR
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from funasr.modules.subsampling import Conv1dSubsampling
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from funasr.torch_utils.initialize import initialize
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from funasr.train.class_choices import ClassChoices
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frontend_choices = ClassChoices(
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name="frontend",
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classes=dict(
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default=DefaultFrontend,
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sliding_window=SlidingWindow,
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s3prl=S3prlFrontend,
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fused=FusedFrontends,
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wav_frontend=WavFrontend,
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multichannelfrontend=MultiChannelFrontend,
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),
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default="default",
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)
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specaug_choices = ClassChoices(
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name="specaug",
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classes=dict(
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specaug=SpecAug,
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specaug_lfr=SpecAugLFR,
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),
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default=None,
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optional=True,
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)
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normalize_choices = ClassChoices(
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"normalize",
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classes=dict(
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global_mvn=GlobalMVN,
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utterance_mvn=UtteranceMVN,
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),
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default=None,
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optional=True,
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)
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model_choices = ClassChoices(
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"model",
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classes=dict(
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asr=ASRModel,
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uniasr=UniASR,
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paraformer=Paraformer,
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paraformer_bert=ParaformerBert,
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bicif_paraformer=BiCifParaformer,
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contextual_paraformer=ContextualParaformer,
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mfcca=MFCCA,
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timestamp_prediction=TimestampPredictor,
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),
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default="asr",
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)
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encoder_choices = ClassChoices(
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"encoder",
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classes=dict(
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conformer=ConformerEncoder,
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transformer=TransformerEncoder,
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rnn=RNNEncoder,
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sanm=SANMEncoder,
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sanm_chunk_opt=SANMEncoderChunkOpt,
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data2vec_encoder=Data2VecEncoder,
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mfcca_enc=MFCCAEncoder,
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chunk_conformer=ConformerChunkEncoder,
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),
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default="rnn",
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)
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encoder_choices2 = ClassChoices(
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"encoder2",
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classes=dict(
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conformer=ConformerEncoder,
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transformer=TransformerEncoder,
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rnn=RNNEncoder,
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sanm=SANMEncoder,
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sanm_chunk_opt=SANMEncoderChunkOpt,
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),
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default="rnn",
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)
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decoder_choices = ClassChoices(
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"decoder",
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classes=dict(
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transformer=TransformerDecoder,
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lightweight_conv=LightweightConvolutionTransformerDecoder,
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lightweight_conv2d=LightweightConvolution2DTransformerDecoder,
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dynamic_conv=DynamicConvolutionTransformerDecoder,
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dynamic_conv2d=DynamicConvolution2DTransformerDecoder,
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rnn=RNNDecoder,
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fsmn_scama_opt=FsmnDecoderSCAMAOpt,
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paraformer_decoder_sanm=ParaformerSANMDecoder,
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paraformer_decoder_san=ParaformerDecoderSAN,
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contextual_paraformer_decoder=ContextualParaformerDecoder,
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),
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default="rnn",
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)
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decoder_choices2 = ClassChoices(
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"decoder2",
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classes=dict(
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transformer=TransformerDecoder,
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lightweight_conv=LightweightConvolutionTransformerDecoder,
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lightweight_conv2d=LightweightConvolution2DTransformerDecoder,
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dynamic_conv=DynamicConvolutionTransformerDecoder,
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dynamic_conv2d=DynamicConvolution2DTransformerDecoder,
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rnn=RNNDecoder,
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fsmn_scama_opt=FsmnDecoderSCAMAOpt,
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paraformer_decoder_sanm=ParaformerSANMDecoder,
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),
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type_check=AbsDecoder,
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default="rnn",
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)
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predictor_choices = ClassChoices(
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name="predictor",
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classes=dict(
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cif_predictor=CifPredictor,
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ctc_predictor=None,
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cif_predictor_v2=CifPredictorV2,
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cif_predictor_v3=CifPredictorV3,
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),
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default="cif_predictor",
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optional=True,
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)
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predictor_choices2 = ClassChoices(
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name="predictor2",
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classes=dict(
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cif_predictor=CifPredictor,
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ctc_predictor=None,
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cif_predictor_v2=CifPredictorV2,
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),
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default="cif_predictor",
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optional=True,
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)
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stride_conv_choices = ClassChoices(
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name="stride_conv",
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classes=dict(
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stride_conv1d=Conv1dSubsampling
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),
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default="stride_conv1d",
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optional=True,
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)
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rnnt_decoder_choices = ClassChoices(
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name="rnnt_decoder",
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classes=dict(
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rnnt=RNNTDecoder,
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),
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default="rnnt",
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optional=True,
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)
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joint_network_choices = ClassChoices(
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name="joint_network",
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classes=dict(
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joint_network=JointNetwork,
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),
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default="joint_network",
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optional=True,
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)
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class_choices_list = [
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# --frontend and --frontend_conf
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frontend_choices,
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# --specaug and --specaug_conf
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specaug_choices,
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# --normalize and --normalize_conf
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normalize_choices,
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# --model and --model_conf
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model_choices,
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# --encoder and --encoder_conf
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encoder_choices,
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# --decoder and --decoder_conf
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decoder_choices,
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# --predictor and --predictor_conf
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predictor_choices,
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# --encoder2 and --encoder2_conf
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encoder_choices2,
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# --decoder2 and --decoder2_conf
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decoder_choices2,
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# --predictor2 and --predictor2_conf
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predictor_choices2,
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# --stride_conv and --stride_conv_conf
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stride_conv_choices,
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# --rnnt_decoder and --rnnt_decoder_conf
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rnnt_decoder_choices,
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# --joint_network and --joint_network_conf
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joint_network_choices,
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]
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def build_asr_model(args):
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# token_list
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if args.token_list is not None:
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with open(args.token_list) as f:
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token_list = [line.rstrip() for line in f]
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args.token_list = list(token_list)
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vocab_size = len(token_list)
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logging.info(f"Vocabulary size: {vocab_size}")
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else:
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vocab_size = None
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# frontend
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if args.input_size is None:
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frontend_class = frontend_choices.get_class(args.frontend)
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if args.frontend == 'wav_frontend':
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frontend = frontend_class(cmvn_file=args.cmvn_file, **args.frontend_conf)
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else:
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frontend = frontend_class(**args.frontend_conf)
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input_size = frontend.output_size()
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else:
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args.frontend = None
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args.frontend_conf = {}
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frontend = None
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input_size = args.input_size
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# data augmentation for spectrogram
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if args.specaug is not None:
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specaug_class = specaug_choices.get_class(args.specaug)
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specaug = specaug_class(**args.specaug_conf)
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else:
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specaug = None
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# normalization layer
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if args.normalize is not None:
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normalize_class = normalize_choices.get_class(args.normalize)
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normalize = normalize_class(**args.normalize_conf)
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else:
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normalize = None
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# encoder
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encoder_class = encoder_choices.get_class(args.encoder)
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encoder = encoder_class(input_size=input_size, **args.encoder_conf)
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# decoder
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decoder_class = decoder_choices.get_class(args.decoder)
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decoder = decoder_class(
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vocab_size=vocab_size,
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encoder_output_size=encoder.output_size(),
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**args.decoder_conf,
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)
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# ctc
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ctc = CTC(
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odim=vocab_size, encoder_output_size=encoder.output_size(), **args.ctc_conf
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)
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if args.model in ["asr", "mfcca"]:
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model_class = model_choices.get_class(args.model)
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model = model_class(
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vocab_size=vocab_size,
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frontend=frontend,
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specaug=specaug,
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normalize=normalize,
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encoder=encoder,
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decoder=decoder,
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ctc=ctc,
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token_list=token_list,
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**args.model_conf,
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)
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elif args.model in ["paraformer", "paraformer_bert", "bicif_paraformer", "contextual_paraformer"]:
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# predictor
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predictor_class = predictor_choices.get_class(args.predictor)
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predictor = predictor_class(**args.predictor_conf)
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model_class = model_choices.get_class(args.model)
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model = model_class(
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vocab_size=vocab_size,
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frontend=frontend,
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specaug=specaug,
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normalize=normalize,
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encoder=encoder,
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decoder=decoder,
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ctc=ctc,
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token_list=token_list,
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predictor=predictor,
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**args.model_conf,
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)
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elif args.model == "uniasr":
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# stride_conv
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stride_conv_class = stride_conv_choices.get_class(args.stride_conv)
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stride_conv = stride_conv_class(**args.stride_conv_conf, idim=input_size + encoder.output_size(),
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odim=input_size + encoder.output_size())
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stride_conv_output_size = stride_conv.output_size()
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# encoder2
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encoder_class2 = encoder_choices2.get_class(args.encoder2)
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encoder2 = encoder_class2(input_size=stride_conv_output_size, **args.encoder2_conf)
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# decoder2
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decoder_class2 = decoder_choices2.get_class(args.decoder2)
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decoder2 = decoder_class2(
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vocab_size=vocab_size,
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encoder_output_size=encoder2.output_size(),
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**args.decoder2_conf,
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)
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# ctc2
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ctc2 = CTC(
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odim=vocab_size, encoder_output_size=encoder2.output_size(), **args.ctc_conf
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)
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# predictor
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predictor_class = predictor_choices.get_class(args.predictor)
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predictor = predictor_class(**args.predictor_conf)
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# predictor2
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predictor_class = predictor_choices2.get_class(args.predictor2)
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predictor2 = predictor_class(**args.predictor2_conf)
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model_class = model_choices.get_class(args.model)
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model = model_class(
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vocab_size=vocab_size,
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frontend=frontend,
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specaug=specaug,
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normalize=normalize,
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encoder=encoder,
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decoder=decoder,
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ctc=ctc,
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token_list=token_list,
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predictor=predictor,
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ctc2=ctc2,
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encoder2=encoder2,
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decoder2=decoder2,
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predictor2=predictor2,
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stride_conv=stride_conv,
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**args.model_conf,
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)
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elif args.model == "timestamp_prediction":
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model_class = model_choices.get_class(args.model)
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model = model_class(
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frontend=frontend,
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encoder=encoder,
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token_list=token_list,
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**args.model_conf,
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)
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elif args.model == "rnnt":
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# 5. Decoder
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encoder_output_size = encoder.output_size()
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rnnt_decoder_class = rnnt_decoder_choices.get_class(args.rnnt_decoder)
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decoder = rnnt_decoder_class(
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vocab_size,
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**args.rnnt_decoder_conf,
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)
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decoder_output_size = decoder.output_size
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if getattr(args, "decoder", None) is not None:
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att_decoder_class = decoder_choices.get_class(args.decoder)
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att_decoder = att_decoder_class(
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vocab_size=vocab_size,
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encoder_output_size=encoder_output_size,
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**args.decoder_conf,
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)
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else:
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att_decoder = None
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# 6. Joint Network
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joint_network = JointNetwork(
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vocab_size,
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encoder_output_size,
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decoder_output_size,
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**args.joint_network_conf,
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)
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# 7. Build model
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if hasattr(encoder, 'unified_model_training') and encoder.unified_model_training:
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model = UnifiedTransducerModel(
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vocab_size=vocab_size,
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token_list=token_list,
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frontend=frontend,
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specaug=specaug,
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normalize=normalize,
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encoder=encoder,
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decoder=decoder,
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att_decoder=att_decoder,
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joint_network=joint_network,
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**args.model_conf,
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)
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else:
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model = TransducerModel(
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vocab_size=vocab_size,
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token_list=token_list,
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frontend=frontend,
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specaug=specaug,
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normalize=normalize,
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encoder=encoder,
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decoder=decoder,
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att_decoder=att_decoder,
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joint_network=joint_network,
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**args.model_conf,
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)
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else:
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raise NotImplementedError("Not supported model: {}".format(args.model))
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# initialize
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if args.init is not None:
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initialize(model, args.init)
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return model
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