diff --git a/funasr/models/paraformer_streaming/export_meta.py b/funasr/models/paraformer_streaming/export_meta.py
new file mode 100644
index 000000000..0193dc83d
--- /dev/null
+++ b/funasr/models/paraformer_streaming/export_meta.py
@@ -0,0 +1,168 @@
+#!/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)
+
+import types
+import torch
+from funasr.register import tables
+
+
+def export_rebuild_model(model, **kwargs):
+        model.device = kwargs.get("device")
+        is_onnx = kwargs.get("type", "onnx") == "onnx"
+        encoder_class = tables.encoder_classes.get(kwargs["encoder"]+"Export")
+        model.encoder = encoder_class(model.encoder, onnx=is_onnx)
+        
+        predictor_class = tables.predictor_classes.get(kwargs["predictor"]+"Export")
+        model.predictor = predictor_class(model.predictor, onnx=is_onnx)
+
+
+        decoder_class = tables.decoder_classes.get(kwargs["decoder"]+"Export")
+        model.decoder = decoder_class(model.decoder, onnx=is_onnx)
+        
+        from funasr.utils.torch_function import sequence_mask
+        model.make_pad_mask = sequence_mask(kwargs['max_seq_len'], flip=False)
+        
+        model.forward = types.MethodType(export_forward, model)
+        model.export_dummy_inputs = types.MethodType(export_dummy_inputs, model)
+        model.export_input_names = types.MethodType(export_input_names, model)
+        model.export_output_names = types.MethodType(export_output_names, model)
+        model.export_dynamic_axes = types.MethodType(export_dynamic_axes, model)
+        model.export_name = types.MethodType(export_name, model)
+        
+        return model
+
+
+def export_rebuild_model(model, **kwargs):
+    # self.device = kwargs.get("device")
+    is_onnx = kwargs.get("type", "onnx") == "onnx"
+    encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
+    model.encoder = encoder_class(model.encoder, onnx=is_onnx)
+    
+    predictor_class = tables.predictor_classes.get(kwargs["predictor"] + "Export")
+    model.predictor = predictor_class(model.predictor, onnx=is_onnx)
+    
+    if kwargs["decoder"] == "ParaformerSANMDecoder":
+        kwargs["decoder"] = "ParaformerSANMDecoderOnline"
+    decoder_class = tables.decoder_classes.get(kwargs["decoder"] + "Export")
+    model.decoder = decoder_class(model.decoder, onnx=is_onnx)
+    
+    from funasr.utils.torch_function import sequence_mask
+    
+    model.make_pad_mask = sequence_mask(max_seq_len=None, flip=False)
+    
+    import copy
+    import types
+    encoder_model = copy.copy(model)
+    decoder_model = copy.copy(model)
+    
+    # encoder
+    encoder_model.forward = types.MethodType(export_encoder_forward, encoder_model)
+    encoder_model.export_dummy_inputs = types.MethodType(export_encoder_dummy_inputs, encoder_model)
+    encoder_model.export_input_names = types.MethodType(export_encoder_input_names, encoder_model)
+    encoder_model.export_output_names = types.MethodType(export_encoder_output_names, encoder_model)
+    encoder_model.export_dynamic_axes = types.MethodType(export_encoder_dynamic_axes, encoder_model)
+    encoder_model.export_name = types.MethodType(export_encoder_name, encoder_model)
+    
+    # decoder
+    decoder_model.forward = types.MethodType(export_decoder_forward, decoder_model)
+    decoder_model.export_dummy_inputs = types.MethodType(export_decoder_dummy_inputs, decoder_model)
+    decoder_model.export_input_names = types.MethodType(export_decoder_input_names, decoder_model)
+    decoder_model.export_output_names = types.MethodType(export_decoder_output_names, decoder_model)
+    decoder_model.export_dynamic_axes = types.MethodType(export_decoder_dynamic_axes, decoder_model)
+    decoder_model.export_name = types.MethodType(export_decoder_name, decoder_model)
+    
+    return encoder_model, decoder_model
+
+
+def export_encoder_forward(
+    self,
+    speech: torch.Tensor,
+    speech_lengths: torch.Tensor,
+):
+    # a. To device
+    batch = {"speech": speech, "speech_lengths": speech_lengths, "online": True}
+    # batch = to_device(batch, device=self.device)
+    
+    enc, enc_len = self.encoder(**batch)
+    mask = self.make_pad_mask(enc_len)[:, None, :]
+    alphas, _ = self.predictor.forward_cnn(enc, mask)
+    
+    return enc, enc_len, alphas
+
+
+def export_encoder_dummy_inputs(self):
+    speech = torch.randn(2, 30, 560)
+    speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
+    return (speech, speech_lengths)
+
+
+def export_encoder_input_names(self):
+    return ['speech', 'speech_lengths']
+
+
+def export_encoder_output_names(self):
+    return ['enc', 'enc_len', 'alphas']
+
+
+def export_encoder_dynamic_axes(self):
+    return {
+        'speech': {
+            0: 'batch_size',
+            1: 'feats_length'
+        },
+        'speech_lengths': {
+            0: 'batch_size',
+        },
+        'enc': {
+            0: 'batch_size',
+            1: 'feats_length'
+        },
+        'enc_len': {
+            0: 'batch_size',
+        },
+        'alphas': {
+            0: 'batch_size',
+            1: 'feats_length'
+        },
+    }
+
+
+def export_encoder_name(self):
+    return "model.onnx"
+
+
+def export_decoder_forward(
+    self,
+    enc: torch.Tensor,
+    enc_len: torch.Tensor,
+    acoustic_embeds: torch.Tensor,
+    acoustic_embeds_len: torch.Tensor,
+    *args,
+):
+    decoder_out, out_caches = self.decoder(enc, enc_len, acoustic_embeds, acoustic_embeds_len, *args)
+    sample_ids = decoder_out.argmax(dim=-1)
+    
+    return decoder_out, sample_ids, out_caches
+
+
+def export_decoder_dummy_inputs(self):
+    dummy_inputs = self.decoder.get_dummy_inputs(enc_size=self.encoder._output_size)
+    return dummy_inputs
+
+
+def export_decoder_input_names(self):
+    return self.decoder.get_input_names()
+
+
+def export_decoder_output_names(self):
+    return self.decoder.get_output_names()
+
+
+def export_decoder_dynamic_axes(self):
+    return self.decoder.get_dynamic_axes()
+
+
+def export_decoder_name(self):
+    return "decoder.onnx"
\ No newline at end of file
diff --git a/funasr/models/paraformer_streaming/model.py b/funasr/models/paraformer_streaming/model.py
index a53aad594..5daa73a4b 100644
--- a/funasr/models/paraformer_streaming/model.py
+++ b/funasr/models/paraformer_streaming/model.py
@@ -562,130 +562,8 @@ class ParaformerStreaming(Paraformer):
             ibest_writer["text"][key[0]] = text_postprocessed
 
         return result, meta_data
-
-    def export(
-        self,
-        max_seq_len=512,
-        **kwargs,
-    ):
-        self.device = kwargs.get("device")
-        is_onnx = kwargs.get("type", "onnx") == "onnx"
-        encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
-        self.encoder = encoder_class(self.encoder, onnx=is_onnx)
     
-        predictor_class = tables.predictor_classes.get(kwargs["predictor"] + "Export")
-        self.predictor = predictor_class(self.predictor, onnx=is_onnx)
-        
-        if kwargs["decoder"] == "ParaformerSANMDecoder":
-            kwargs["decoder"] = "ParaformerSANMDecoderOnline"
-        decoder_class = tables.decoder_classes.get(kwargs["decoder"] + "Export")
-        self.decoder = decoder_class(self.decoder, onnx=is_onnx)
-    
-        from funasr.utils.torch_function import sequence_mask
-
-
-        self.make_pad_mask = sequence_mask(max_seq_len, flip=False)
-
-        import copy
-        import types
-        encoder_model = copy.copy(self)
-        decoder_model = copy.copy(self)
-
-        # encoder
-        encoder_model.forward = types.MethodType(ParaformerStreaming.export_encoder_forward, encoder_model)
-        encoder_model.export_dummy_inputs = types.MethodType(ParaformerStreaming.export_encoder_dummy_inputs, encoder_model)
-        encoder_model.export_input_names = types.MethodType(ParaformerStreaming.export_encoder_input_names, encoder_model)
-        encoder_model.export_output_names = types.MethodType(ParaformerStreaming.export_encoder_output_names, encoder_model)
-        encoder_model.export_dynamic_axes = types.MethodType(ParaformerStreaming.export_encoder_dynamic_axes, encoder_model)
-        encoder_model.export_name = types.MethodType(ParaformerStreaming.export_encoder_name, encoder_model)
-        
-        # decoder
-        decoder_model.forward = types.MethodType(ParaformerStreaming.export_decoder_forward, decoder_model)
-        decoder_model.export_dummy_inputs = types.MethodType(ParaformerStreaming.export_decoder_dummy_inputs, decoder_model)
-        decoder_model.export_input_names = types.MethodType(ParaformerStreaming.export_decoder_input_names, decoder_model)
-        decoder_model.export_output_names = types.MethodType(ParaformerStreaming.export_decoder_output_names, decoder_model)
-        decoder_model.export_dynamic_axes = types.MethodType(ParaformerStreaming.export_decoder_dynamic_axes, decoder_model)
-        decoder_model.export_name = types.MethodType(ParaformerStreaming.export_decoder_name, decoder_model)
-    
-        return encoder_model, decoder_model
-
-    def export_encoder_forward(
-        self,
-        speech: torch.Tensor,
-        speech_lengths: torch.Tensor,
-    ):
-        # a. To device
-        batch = {"speech": speech, "speech_lengths": speech_lengths, "online": True}
-        # batch = to_device(batch, device=self.device)
-    
-        enc, enc_len = self.encoder(**batch)
-        mask = self.make_pad_mask(enc_len)[:, None, :]
-        alphas, _ = self.predictor.forward_cnn(enc, mask)
-    
-        return enc, enc_len, alphas
-
-    def export_encoder_dummy_inputs(self):
-        speech = torch.randn(2, 30, 560)
-        speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
-        return (speech, speech_lengths)
-
-    def export_encoder_input_names(self):
-        return ['speech', 'speech_lengths']
-
-    def export_encoder_output_names(self):
-        return ['enc', 'enc_len', 'alphas']
-
-    def export_encoder_dynamic_axes(self):
-        return {
-            'speech': {
-                0: 'batch_size',
-                1: 'feats_length'
-            },
-            'speech_lengths': {
-                0: 'batch_size',
-            },
-            'enc': {
-                0: 'batch_size',
-                1: 'feats_length'
-            },
-            'enc_len': {
-                0: 'batch_size',
-            },
-            'alphas': {
-                0: 'batch_size',
-                1: 'feats_length'
-            },
-        }
-    
-    def export_encoder_name(self):
-        return "model.onnx"
-    
-    def export_decoder_forward(
-        self,
-        enc: torch.Tensor,
-        enc_len: torch.Tensor,
-        acoustic_embeds: torch.Tensor,
-        acoustic_embeds_len: torch.Tensor,
-        *args,
-    ):
-        decoder_out, out_caches = self.decoder(enc, enc_len, acoustic_embeds, acoustic_embeds_len, *args)
-        sample_ids = decoder_out.argmax(dim=-1)
-    
-        return decoder_out, sample_ids, out_caches
-
-    def export_decoder_dummy_inputs(self):
-        dummy_inputs = self.decoder.get_dummy_inputs(enc_size=self.encoder._output_size)
-        return dummy_inputs
-
-    def export_decoder_input_names(self):
-    
-        return self.decoder.get_input_names()
-
-    def export_decoder_output_names(self):
-    
-        return self.decoder.get_output_names()
-
-    def export_decoder_dynamic_axes(self):
-        return self.decoder.get_dynamic_axes()
-    def export_decoder_name(self):
-        return "decoder.onnx"
\ No newline at end of file
+    def export(self, **kwargs):
+        from .export_meta import export_rebuild_model
+        models = export_rebuild_model(model=self, **kwargs)
+        return models
\ No newline at end of file