diff --git a/funasr/bin/asr_inference_paraformer_streaming.py b/funasr/bin/asr_inference_paraformer_streaming.py
index c70baf023..ff8bb8c77 100644
--- a/funasr/bin/asr_inference_paraformer_streaming.py
+++ b/funasr/bin/asr_inference_paraformer_streaming.py
@@ -8,6 +8,7 @@ import os
 import codecs
 import tempfile
 import requests
+import yaml
 from pathlib import Path
 from typing import Optional
 from typing import Sequence
@@ -462,13 +463,23 @@ def inference_modelscope(
         array = np.frombuffer((middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32)
         return array
 
+    def _read_yaml(yaml_path: Union[str, Path]) -> Dict:
+        if not Path(yaml_path).exists():
+            raise FileExistsError(f'The {yaml_path} does not exist.')
+
+        with open(str(yaml_path), 'rb') as f:
+            data = yaml.load(f, Loader=yaml.Loader)
+        return data
+
     def _prepare_cache(cache: dict = {}, chunk_size=[5,10,5], batch_size=1):
         if len(cache) > 0:
             return cache
-
-        cache_en = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, 320)),
+        config = _read_yaml(asr_train_config)
+        enc_output_size = config["encoder_conf"]["output_size"]
+        feats_dims = config["frontend_conf"]["n_mels"] * config["frontend_conf"]["lfr_m"]
+        cache_en = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, enc_output_size)),
                     "cif_alphas": torch.zeros((batch_size, 1)), "chunk_size": chunk_size, "last_chunk": False,
-                    "feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], 560)), "tail_chunk": False}
+                    "feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], feats_dims)), "tail_chunk": False}
         cache["encoder"] = cache_en
 
         cache_de = {"decode_fsmn": None}
@@ -478,9 +489,12 @@ def inference_modelscope(
 
     def _cache_reset(cache: dict = {}, chunk_size=[5,10,5], batch_size=1):
         if len(cache) > 0:
-            cache_en = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, 320)),
+            config = _read_yaml(asr_train_config)
+            enc_output_size = config["encoder_conf"]["output_size"]
+            feats_dims = config["frontend_conf"]["n_mels"] * config["frontend_conf"]["lfr_m"]
+            cache_en = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, enc_output_size)),
                         "cif_alphas": torch.zeros((batch_size, 1)), "chunk_size": chunk_size, "last_chunk": False,
-                        "feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], 560)), "tail_chunk": False}
+                        "feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], feats_dims)), "tail_chunk": False}
             cache["encoder"] = cache_en
 
             cache_de = {"decode_fsmn": None}
@@ -720,4 +734,3 @@ if __name__ == "__main__":
     #
     # rec_result = inference_16k_pipline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
     # print(rec_result)
-