diff --git a/README.md b/README.md
index 05f6364f1..eff05936d 100644
--- a/README.md
+++ b/README.md
@@ -97,7 +97,7 @@ model = AutoModel(model="paraformer-zh", model_revision="v2.0.2", \
                   punc_model="ct-punc-c", punc_model_revision="v2.0.2", \
                   spk_model="cam++", spk_model_revision="v2.0.2")
 res = model(input=f"{model.model_path}/example/asr_example.wav", 
-            batch_size=16, 
+            batch_size=64, 
             hotword='魔搭')
 print(res)
 ```
@@ -135,7 +135,6 @@ Note: `chunk_size` is the configuration for streaming latency.` [0,10,5]` indica
 from funasr import AutoModel
 
 model = AutoModel(model="fsmn-vad", model_revision="v2.0.2")
-
 wav_file = f"{model.model_path}/example/asr_example.wav"
 res = model(input=wav_file)
 print(res)
@@ -167,7 +166,6 @@ for i in range(total_chunk_num):
 from funasr import AutoModel
 
 model = AutoModel(model="ct-punc", model_revision="v2.0.2")
-
 res = model(input="那今天的会就到这里吧 happy new year 明年见")
 print(res)
 ```
@@ -176,9 +174,8 @@ print(res)
 from funasr import AutoModel
 
 model = AutoModel(model="fa-zh", model_revision="v2.0.2")
-
 wav_file = f"{model.model_path}/example/asr_example.wav"
-text_file = f"{model.model_path}/example/asr_example.wav"
+text_file = f"{model.model_path}/example/text.txt"
 res = model(input=(wav_file, text_file), data_type=("sound", "text"))
 print(res)
 ```
diff --git a/README_zh.md b/README_zh.md
index 62d251b4c..2da217170 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -86,12 +86,15 @@ funasr +model=paraformer-zh +vad_model="fsmn-vad" +punc_model="ct-punc" +input=a
 ### 非实时语音识别
 ```python
 from funasr import AutoModel
-
-model = AutoModel(model="paraformer-zh")
-# for the long duration wav, you could add vad model
-# model = AutoModel(model="paraformer-zh", vad_model="fsmn-vad", punc_model="ct-punc")
-
-res = model(input="asr_example_zh.wav", batch_size=64)
+# paraformer-zh is a multi-functional asr model
+# use vad, punc, spk or not as you need
+model = AutoModel(model="paraformer-zh", model_revision="v2.0.2", \
+                  vad_model="fsmn-vad", vad_model_revision="v2.0.2", \
+                  punc_model="ct-punc-c", punc_model_revision="v2.0.2", \
+                  spk_model="cam++", spk_model_revision="v2.0.2")
+res = model(input=f"{model.model_path}/example/asr_example.wav", 
+            batch_size=64, 
+            hotword='魔搭')
 print(res)
 ```
 注：`model_hub`：表示模型仓库，`ms`为选择modelscope下载，`hf`为选择huggingface下载。
@@ -105,7 +108,7 @@ chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
 encoder_chunk_look_back = 4 #number of chunks to lookback for encoder self-attention
 decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cross-attention
 
-model = AutoModel(model="paraformer-zh-streaming", model_revision="v2.0.0")
+model = AutoModel(model="paraformer-zh-streaming", model_revision="v2.0.2")
 
 import soundfile
 import os
@@ -163,7 +166,7 @@ for i in range(total_chunk_num):
 ```python
 from funasr import AutoModel
 
-model = AutoModel(model="ct-punc", model_revision="v2.0.1")
+model = AutoModel(model="ct-punc", model_revision="v2.0.2")
 
 res = model(input="那今天的会就到这里吧 happy new year 明年见")
 print(res)
@@ -176,7 +179,7 @@ from funasr import AutoModel
 model = AutoModel(model="fa-zh", model_revision="v2.0.0")
 
 wav_file = f"{model.model_path}/example/asr_example.wav"
-text_file = f"{model.model_path}/example/asr_example.wav"
+text_file = f"{model.model_path}/example/text.txt"
 res = model(input=(wav_file, text_file), data_type=("sound", "text"))
 print(res)
 ```