diff --git a/examples/industrial_data_pretraining/transducer/demo.py b/examples/industrial_data_pretraining/transducer/demo.py
new file mode 100644
index 000000000..ef12aac07
--- /dev/null
+++ b/examples/industrial_data_pretraining/transducer/demo.py
@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+from funasr import AutoModel
+
+# Transducer, BAT and RWKV_BAT models are just same to use, use the correct model_revision
+# https://modelscope.cn/models?name=transducer&page=1&tasks=auto-speech-recognition&type=audio
+model = AutoModel(model="iic/speech_bat_asr-zh-cn-16k-aishell1-vocab4234-pytorch", 
+                  model_revision="v2.0.2",
+                 )
+
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
+print(res)
\ No newline at end of file
diff --git a/funasr/models/bat/model.py b/funasr/models/bat/model.py
index 8e76b458a..bdfcba6e1 100644
--- a/funasr/models/bat/model.py
+++ b/funasr/models/bat/model.py
@@ -13,6 +13,7 @@ from distutils.version import LooseVersion
 from funasr.register import tables
 from funasr.utils import postprocess_utils
 from funasr.utils.datadir_writer import DatadirWriter
+from funasr.models.transducer.model import Transducer
 from funasr.train_utils.device_funcs import force_gatherable
 from funasr.models.transformer.scorers.ctc import CTCPrefixScorer
 from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
@@ -32,488 +33,5 @@ else:
 
 
 @tables.register("model_classes", "BAT")  # TODO: BAT training
-class BAT(torch.nn.Module):
-    def __init__(
-        self,
-        frontend: Optional[str] = None,
-        frontend_conf: Optional[Dict] = None,
-        specaug: Optional[str] = None,
-        specaug_conf: Optional[Dict] = None,
-        normalize: str = None,
-        normalize_conf: Optional[Dict] = None,
-        encoder: str = None,
-        encoder_conf: Optional[Dict] = None,
-        decoder: str = None,
-        decoder_conf: Optional[Dict] = None,
-        joint_network: str = None,
-        joint_network_conf: Optional[Dict] = None,
-        transducer_weight: float = 1.0,
-        fastemit_lambda: float = 0.0,
-        auxiliary_ctc_weight: float = 0.0,
-        auxiliary_ctc_dropout_rate: float = 0.0,
-        auxiliary_lm_loss_weight: float = 0.0,
-        auxiliary_lm_loss_smoothing: float = 0.0,
-        input_size: int = 80,
-        vocab_size: int = -1,
-        ignore_id: int = -1,
-        blank_id: int = 0,
-        sos: int = 1,
-        eos: int = 2,
-        lsm_weight: float = 0.0,
-        length_normalized_loss: bool = False,
-        # report_cer: bool = True,
-        # report_wer: bool = True,
-        # sym_space: str = "<space>",
-        # sym_blank: str = "<blank>",
-        # extract_feats_in_collect_stats: bool = True,
-        share_embedding: bool = False,
-        # preencoder: Optional[AbsPreEncoder] = None,
-        # postencoder: Optional[AbsPostEncoder] = None,
-        **kwargs,
-    ):
-
-        super().__init__()
-
-        if specaug is not None:
-            specaug_class = tables.specaug_classes.get(specaug)
-            specaug = specaug_class(**specaug_conf)
-        if normalize is not None:
-            normalize_class = tables.normalize_classes.get(normalize)
-            normalize = normalize_class(**normalize_conf)
-        encoder_class = tables.encoder_classes.get(encoder)
-        encoder = encoder_class(input_size=input_size, **encoder_conf)
-        encoder_output_size = encoder.output_size()
-
-        decoder_class = tables.decoder_classes.get(decoder)
-        decoder = decoder_class(
-            vocab_size=vocab_size,
-            **decoder_conf,
-        )
-        decoder_output_size = decoder.output_size
-
-        joint_network_class = tables.joint_network_classes.get(joint_network)
-        joint_network = joint_network_class(
-            vocab_size,
-            encoder_output_size,
-            decoder_output_size,
-            **joint_network_conf,
-        )
-        
-        self.criterion_transducer = None
-        self.error_calculator = None
-        
-        self.use_auxiliary_ctc = auxiliary_ctc_weight > 0
-        self.use_auxiliary_lm_loss = auxiliary_lm_loss_weight > 0
-        
-        if self.use_auxiliary_ctc:
-            self.ctc_lin = torch.nn.Linear(encoder.output_size(), vocab_size)
-            self.ctc_dropout_rate = auxiliary_ctc_dropout_rate
-        
-        if self.use_auxiliary_lm_loss:
-            self.lm_lin = torch.nn.Linear(decoder.output_size, vocab_size)
-            self.lm_loss_smoothing = auxiliary_lm_loss_smoothing
-        
-        self.transducer_weight = transducer_weight
-        self.fastemit_lambda = fastemit_lambda
-        
-        self.auxiliary_ctc_weight = auxiliary_ctc_weight
-        self.auxiliary_lm_loss_weight = auxiliary_lm_loss_weight
-        self.blank_id = blank_id
-        self.sos = sos if sos is not None else vocab_size - 1
-        self.eos = eos if eos is not None else vocab_size - 1
-        self.vocab_size = vocab_size
-        self.ignore_id = ignore_id
-        self.frontend = frontend
-        self.specaug = specaug
-        self.normalize = normalize
-        self.encoder = encoder
-        self.decoder = decoder
-        self.joint_network = joint_network
-
-        self.criterion_att = LabelSmoothingLoss(
-            size=vocab_size,
-            padding_idx=ignore_id,
-            smoothing=lsm_weight,
-            normalize_length=length_normalized_loss,
-        )
-
-        self.length_normalized_loss = length_normalized_loss
-        self.beam_search = None
-        self.ctc = None
-        self.ctc_weight = 0.0
-    
-    def forward(
-        self,
-        speech: torch.Tensor,
-        speech_lengths: torch.Tensor,
-        text: torch.Tensor,
-        text_lengths: torch.Tensor,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
-        """Encoder + Decoder + Calc loss
-        Args:
-                speech: (Batch, Length, ...)
-                speech_lengths: (Batch, )
-                text: (Batch, Length)
-                text_lengths: (Batch,)
-        """
-        if len(text_lengths.size()) > 1:
-            text_lengths = text_lengths[:, 0]
-        if len(speech_lengths.size()) > 1:
-            speech_lengths = speech_lengths[:, 0]
-        
-        batch_size = speech.shape[0]
-        # 1. Encoder
-        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
-        if hasattr(self.encoder, 'overlap_chunk_cls') and self.encoder.overlap_chunk_cls is not None:
-            encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out, encoder_out_lens,
-                                                                                        chunk_outs=None)
-        # 2. Transducer-related I/O preparation
-        decoder_in, target, t_len, u_len = get_transducer_task_io(
-            text,
-            encoder_out_lens,
-            ignore_id=self.ignore_id,
-        )
-        
-        # 3. Decoder
-        self.decoder.set_device(encoder_out.device)
-        decoder_out = self.decoder(decoder_in, u_len)
-        
-        # 4. Joint Network
-        joint_out = self.joint_network(
-            encoder_out.unsqueeze(2), decoder_out.unsqueeze(1)
-        )
-        
-        # 5. Losses
-        loss_trans, cer_trans, wer_trans = self._calc_transducer_loss(
-            encoder_out,
-            joint_out,
-            target,
-            t_len,
-            u_len,
-        )
-        
-        loss_ctc, loss_lm = 0.0, 0.0
-        
-        if self.use_auxiliary_ctc:
-            loss_ctc = self._calc_ctc_loss(
-                encoder_out,
-                target,
-                t_len,
-                u_len,
-            )
-        
-        if self.use_auxiliary_lm_loss:
-            loss_lm = self._calc_lm_loss(decoder_out, target)
-        
-        loss = (
-            self.transducer_weight * loss_trans
-            + self.auxiliary_ctc_weight * loss_ctc
-            + self.auxiliary_lm_loss_weight * loss_lm
-        )
-        
-        stats = dict(
-            loss=loss.detach(),
-            loss_transducer=loss_trans.detach(),
-            aux_ctc_loss=loss_ctc.detach() if loss_ctc > 0.0 else None,
-            aux_lm_loss=loss_lm.detach() if loss_lm > 0.0 else None,
-            cer_transducer=cer_trans,
-            wer_transducer=wer_trans,
-        )
-        
-        # force_gatherable: to-device and to-tensor if scalar for DataParallel
-        loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
-        
-        return loss, stats, weight
-
-    def encode(
-        self, speech: torch.Tensor, speech_lengths: torch.Tensor, **kwargs,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """Frontend + Encoder. Note that this method is used by asr_inference.py
-        Args:
-                speech: (Batch, Length, ...)
-                speech_lengths: (Batch, )
-                ind: int
-        """
-        with autocast(False):
-
-            # Data augmentation
-            if self.specaug is not None and self.training:
-                speech, speech_lengths = self.specaug(speech, speech_lengths)
-            
-            # Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
-            if self.normalize is not None:
-                speech, speech_lengths = self.normalize(speech, speech_lengths)
-        
-        # Forward encoder
-        # feats: (Batch, Length, Dim)
-        # -> encoder_out: (Batch, Length2, Dim2)
-        encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
-        intermediate_outs = None
-        if isinstance(encoder_out, tuple):
-            intermediate_outs = encoder_out[1]
-            encoder_out = encoder_out[0]
-        
-        if intermediate_outs is not None:
-            return (encoder_out, intermediate_outs), encoder_out_lens
-        
-        return encoder_out, encoder_out_lens
-    
-    def _calc_transducer_loss(
-        self,
-        encoder_out: torch.Tensor,
-        joint_out: torch.Tensor,
-        target: torch.Tensor,
-        t_len: torch.Tensor,
-        u_len: torch.Tensor,
-    ) -> Tuple[torch.Tensor, Optional[float], Optional[float]]:
-        """Compute Transducer loss.
-
-        Args:
-            encoder_out: Encoder output sequences. (B, T, D_enc)
-            joint_out: Joint Network output sequences (B, T, U, D_joint)
-            target: Target label ID sequences. (B, L)
-            t_len: Encoder output sequences lengths. (B,)
-            u_len: Target label ID sequences lengths. (B,)
-
-        Return:
-            loss_transducer: Transducer loss value.
-            cer_transducer: Character error rate for Transducer.
-            wer_transducer: Word Error Rate for Transducer.
-
-        """
-        if self.criterion_transducer is None:
-            try:
-                from warp_rnnt import rnnt_loss as RNNTLoss
-                self.criterion_transducer = RNNTLoss
-            
-            except ImportError:
-                logging.error(
-                    "warp-rnnt was not installed."
-                    "Please consult the installation documentation."
-                )
-                exit(1)
-        
-        log_probs = torch.log_softmax(joint_out, dim=-1)
-        
-        loss_transducer = self.criterion_transducer(
-            log_probs,
-            target,
-            t_len,
-            u_len,
-            reduction="mean",
-            blank=self.blank_id,
-            fastemit_lambda=self.fastemit_lambda,
-            gather=True,
-        )
-        
-        if not self.training and (self.report_cer or self.report_wer):
-            if self.error_calculator is None:
-                from funasr.metrics import ErrorCalculatorTransducer as ErrorCalculator
-                
-                self.error_calculator = ErrorCalculator(
-                    self.decoder,
-                    self.joint_network,
-                    self.token_list,
-                    self.sym_space,
-                    self.sym_blank,
-                    report_cer=self.report_cer,
-                    report_wer=self.report_wer,
-                )
-            
-            cer_transducer, wer_transducer = self.error_calculator(encoder_out, target, t_len)
-            
-            return loss_transducer, cer_transducer, wer_transducer
-        
-        return loss_transducer, None, None
-    
-    def _calc_ctc_loss(
-        self,
-        encoder_out: torch.Tensor,
-        target: torch.Tensor,
-        t_len: torch.Tensor,
-        u_len: torch.Tensor,
-    ) -> torch.Tensor:
-        """Compute CTC loss.
-
-        Args:
-            encoder_out: Encoder output sequences. (B, T, D_enc)
-            target: Target label ID sequences. (B, L)
-            t_len: Encoder output sequences lengths. (B,)
-            u_len: Target label ID sequences lengths. (B,)
-
-        Return:
-            loss_ctc: CTC loss value.
-
-        """
-        ctc_in = self.ctc_lin(
-            torch.nn.functional.dropout(encoder_out, p=self.ctc_dropout_rate)
-        )
-        ctc_in = torch.log_softmax(ctc_in.transpose(0, 1), dim=-1)
-        
-        target_mask = target != 0
-        ctc_target = target[target_mask].cpu()
-        
-        with torch.backends.cudnn.flags(deterministic=True):
-            loss_ctc = torch.nn.functional.ctc_loss(
-                ctc_in,
-                ctc_target,
-                t_len,
-                u_len,
-                zero_infinity=True,
-                reduction="sum",
-            )
-        loss_ctc /= target.size(0)
-        
-        return loss_ctc
-    
-    def _calc_lm_loss(
-        self,
-        decoder_out: torch.Tensor,
-        target: torch.Tensor,
-    ) -> torch.Tensor:
-        """Compute LM loss.
-
-        Args:
-            decoder_out: Decoder output sequences. (B, U, D_dec)
-            target: Target label ID sequences. (B, L)
-
-        Return:
-            loss_lm: LM loss value.
-
-        """
-        lm_loss_in = self.lm_lin(decoder_out[:, :-1, :]).view(-1, self.vocab_size)
-        lm_target = target.view(-1).type(torch.int64)
-        
-        with torch.no_grad():
-            true_dist = lm_loss_in.clone()
-            true_dist.fill_(self.lm_loss_smoothing / (self.vocab_size - 1))
-            
-            # Ignore blank ID (0)
-            ignore = lm_target == 0
-            lm_target = lm_target.masked_fill(ignore, 0)
-            
-            true_dist.scatter_(1, lm_target.unsqueeze(1), (1 - self.lm_loss_smoothing))
-        
-        loss_lm = torch.nn.functional.kl_div(
-            torch.log_softmax(lm_loss_in, dim=1),
-            true_dist,
-            reduction="none",
-        )
-        loss_lm = loss_lm.masked_fill(ignore.unsqueeze(1), 0).sum() / decoder_out.size(
-            0
-        )
-        
-        return loss_lm
-    
-    def init_beam_search(self,
-                         **kwargs,
-                         ):
-    
-        # 1. Build ASR model
-        scorers = {}
-        
-        if self.ctc != None:
-            ctc = CTCPrefixScorer(ctc=self.ctc, eos=self.eos)
-            scorers.update(
-                ctc=ctc
-            )
-        token_list = kwargs.get("token_list")
-        scorers.update(
-            length_bonus=LengthBonus(len(token_list)),
-        )
-
-        # 3. Build ngram model
-        # ngram is not supported now
-        ngram = None
-        scorers["ngram"] = ngram
-        
-        beam_search = BeamSearchTransducer(
-            self.decoder,
-            self.joint_network,
-            kwargs.get("beam_size", 2),
-            nbest=1,
-        )
-        # beam_search.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
-        # for scorer in scorers.values():
-        #     if isinstance(scorer, torch.nn.Module):
-        #         scorer.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
-        self.beam_search = beam_search
-        
-    def inference(self,
-                  data_in: list,
-                  data_lengths: list=None,
-                  key: list=None,
-                  tokenizer=None,
-                  **kwargs,
-                  ):
-        
-        if kwargs.get("batch_size", 1) > 1:
-            raise NotImplementedError("batch decoding is not implemented")
-        
-        # init beamsearch
-        is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
-        is_use_lm = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
-        # if self.beam_search is None and (is_use_lm or is_use_ctc):
-        logging.info("enable beam_search")
-        self.init_beam_search(**kwargs)
-        self.nbest = kwargs.get("nbest", 1)
-        
-        meta_data = {}
-        # extract fbank feats
-        time1 = time.perf_counter()
-        audio_sample_list = load_audio_text_image_video(data_in, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
-        time2 = time.perf_counter()
-        meta_data["load_data"] = f"{time2 - time1:0.3f}"
-        speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=self.frontend)
-        time3 = time.perf_counter()
-        meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
-        meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
-        
-        speech = speech.to(device=kwargs["device"])
-        speech_lengths = speech_lengths.to(device=kwargs["device"])
-
-        # Encoder
-        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
-        if isinstance(encoder_out, tuple):
-            encoder_out = encoder_out[0]
-        
-        # c. Passed the encoder result and the beam search
-        nbest_hyps = self.beam_search(encoder_out[0], is_final=True)
-        nbest_hyps = nbest_hyps[: self.nbest]
-
-        results = []
-        b, n, d = encoder_out.size()
-        for i in range(b):
-
-            for nbest_idx, hyp in enumerate(nbest_hyps):
-                ibest_writer = None
-                if kwargs.get("output_dir") is not None:
-                    if not hasattr(self, "writer"):
-                        self.writer = DatadirWriter(kwargs.get("output_dir"))
-                    ibest_writer = self.writer[f"{nbest_idx + 1}best_recog"]
-                # remove sos/eos and get results
-                last_pos = -1
-                if isinstance(hyp.yseq, list):
-                    token_int = hyp.yseq#[1:last_pos]
-                else:
-                    token_int = hyp.yseq#[1:last_pos].tolist()
-                    
-                # remove blank symbol id, which is assumed to be 0
-                token_int = list(filter(lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int))
-                
-                # Change integer-ids to tokens
-                token = tokenizer.ids2tokens(token_int)
-                text = tokenizer.tokens2text(token)
-                
-                text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
-                result_i = {"key": key[i], "token": token, "text": text, "text_postprocessed": text_postprocessed}
-                results.append(result_i)
-                
-                if ibest_writer is not None:
-                    ibest_writer["token"][key[i]] = " ".join(token)
-                    ibest_writer["text"][key[i]] = text
-                    ibest_writer["text_postprocessed"][key[i]] = text_postprocessed
-        
-        return results, meta_data
-
+class BAT(Transducer):
+    pass
\ No newline at end of file