# from .ctc import CTC
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# from .joint_network import JointNetwork
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#
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# # encoder
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# from espnet2.asr.encoder.rnn_encoder import RNNEncoder as espnetRNNEncoder
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# from espnet2.asr.encoder.vgg_rnn_encoder import VGGRNNEncoder as espnetVGGRNNEncoder
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# from espnet2.asr.encoder.contextual_block_transformer_encoder import ContextualBlockTransformerEncoder as espnetContextualTransformer
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# from espnet2.asr.encoder.contextual_block_conformer_encoder import ContextualBlockConformerEncoder as espnetContextualConformer
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# from espnet2.asr.encoder.transformer_encoder import TransformerEncoder as espnetTransformerEncoder
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# from espnet2.asr.encoder.conformer_encoder import ConformerEncoder as espnetConformerEncoder
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# from funasr.export.models.encoder.rnn import RNNEncoder
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# from funasr.export.models.encoders import TransformerEncoder
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# from funasr.export.models.encoders import ConformerEncoder
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# from funasr.export.models.encoder.contextual_block_xformer import ContextualBlockXformerEncoder
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#
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# # decoder
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# from espnet2.asr.decoder.rnn_decoder import RNNDecoder as espnetRNNDecoder
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# from espnet2.asr.transducer.transducer_decoder import TransducerDecoder as espnetTransducerDecoder
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# from funasr.export.models.decoder.rnn import (
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# RNNDecoder
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# )
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# from funasr.export.models.decoders import XformerDecoder
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# from funasr.export.models.decoders import TransducerDecoder
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#
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# # lm
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# from espnet2.lm.seq_rnn_lm import SequentialRNNLM as espnetSequentialRNNLM
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# from espnet2.lm.transformer_lm import TransformerLM as espnetTransformerLM
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# from .language_models.seq_rnn import SequentialRNNLM
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# from .language_models.transformer import TransformerLM
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#
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# # frontend
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# from espnet2.asr.frontend.s3prl import S3prlFrontend as espnetS3PRLModel
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# from .frontends.s3prl import S3PRLModel
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#
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# from espnet2.asr.encoder.sanm_encoder import SANMEncoder_tf, SANMEncoderChunkOpt_tf
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# from espnet_onnx.export.asr.models.encoders.transformer_sanm import TransformerEncoderSANM_tf
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# from espnet2.asr.decoder.transformer_decoder import FsmnDecoderSCAMAOpt_tf
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# from funasr.export.models.decoders import XformerDecoderSANM
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from funasr.models.e2e_asr_paraformer import Paraformer
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from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export
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def get_model(model, export_config=None):
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if isinstance(model, Paraformer):
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return Paraformer_export(model, **export_config)
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else:
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raise "The model is not exist!"
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|
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# def get_encoder(model, frontend, preencoder, predictor=None, export_config=None):
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# if isinstance(model, espnetRNNEncoder) or isinstance(model, espnetVGGRNNEncoder):
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# return RNNEncoder(model, frontend, preencoder, **export_config)
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# elif isinstance(model, espnetContextualTransformer) or isinstance(model, espnetContextualConformer):
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# return ContextualBlockXformerEncoder(model, **export_config)
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# elif isinstance(model, espnetTransformerEncoder):
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# return TransformerEncoder(model, frontend, preencoder, **export_config)
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# elif isinstance(model, espnetConformerEncoder):
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# return ConformerEncoder(model, frontend, preencoder, **export_config)
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# elif isinstance(model, SANMEncoder_tf) or isinstance(model, SANMEncoderChunkOpt_tf):
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# return TransformerEncoderSANM_tf(model, frontend, preencoder, predictor, **export_config)
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# else:
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# raise "The model is not exist!"
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|
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#
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# def get_decoder(model, export_config):
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# if isinstance(model, espnetRNNDecoder):
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# return RNNDecoder(model, **export_config)
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# elif isinstance(model, espnetTransducerDecoder):
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# return TransducerDecoder(model, **export_config)
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# elif isinstance(model, FsmnDecoderSCAMAOpt_tf):
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# return XformerDecoderSANM(model, **export_config)
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# else:
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# return XformerDecoder(model, **export_config)
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#
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#
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# def get_lm(model, export_config):
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# if isinstance(model, espnetSequentialRNNLM):
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# return SequentialRNNLM(model, **export_config)
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# elif isinstance(model, espnetTransformerLM):
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# return TransformerLM(model, **export_config)
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#
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#
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# def get_frontend_models(model, export_config):
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# if isinstance(model, espnetS3PRLModel):
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# return S3PRLModel(model, **export_config)
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# else:
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# return None
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#
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