diff --git a/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py b/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py
new file mode 100644
index 000000000..f2024fbd2
--- /dev/null
+++ b/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py
@@ -0,0 +1,52 @@
+import torch
+import torchaudio
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipeline = pipeline(
+    task=Tasks.auto_speech_recognition,
+    model='damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online',
+    model_revision='v1.0.2')
+
+waveform, sample_rate = torchaudio.load("asr_example_zh.wav")
+speech_length = waveform.shape[1]
+speech = waveform[0]
+
+cache_en = {"start_idx": 0, "pad_left": 0, "stride": 10, "pad_right": 5, "cif_hidden": None, "cif_alphas": None}
+cache_de = {"decode_fsmn": None}
+cache = {"encoder": cache_en, "decoder": cache_de}
+param_dict = {}
+param_dict["cache"] = cache
+
+first_chunk = True
+speech_buffer = speech
+speech_cache = []
+final_result = ""
+
+while len(speech_buffer) > 0:
+    if first_chunk:
+        if len(speech_buffer) >= 14400:
+            rec_result = inference_pipeline(audio_in=speech_buffer[0:14400], param_dict=param_dict)
+            speech_buffer = speech_buffer[4800:]
+        else:
+            cache_en["stride"] = len(speech_buffer) // 960
+            cache_en["pad_right"] = 0
+            rec_result = inference_pipeline(audio_in=speech_buffer, param_dict=param_dict)
+            speech_buffer = []
+        cache_en["start_idx"] = -5
+        first_chunk = False
+    else:
+        cache_en["start_idx"] += 10
+        if len(speech_buffer) >= 4800:
+            cache_en["pad_left"] = 5
+            rec_result = inference_pipeline(audio_in=speech_buffer[:19200], param_dict=param_dict)
+            speech_buffer = speech_buffer[9600:]
+        else:
+            cache_en["stride"] = len(speech_buffer) // 960
+            cache_en["pad_right"] = 0
+            rec_result = inference_pipeline(audio_in=speech_buffer, param_dict=param_dict)
+            speech_buffer = []
+    if rec_result['text'] != "sil":
+        final_result += rec_result['text']
+    print(rec_result)
+print(final_result)