diff --git a/utils/emotion2vec_utils.py b/utils/emotion2vec_utils.py
index 92564ea..bd15afb 100644
--- a/utils/emotion2vec_utils.py
+++ b/utils/emotion2vec_utils.py
@@ -1,37 +1,54 @@
-from modelscope.pipelines import pipeline
-from modelscope.utils.constant import Tasks
-import json
-
-model_args = {
-    "task": Tasks.emotion_recognition,
-    "model": "iic/emotion2vec_base_finetuned"    # Alternative: iic/emotion2vec_plus_seed, iic/emotion2vec_plus_base, iic/emotion2vec_plus_large and iic/emotion2vec_base_finetuned
-    # "device": 不用指定，"device" 默认为gpu
-}
-
-class EmotionRecognition:
-    def __init__(self) -> None:
-        self.initialize(model_args=model_args)
-
-    # 初始化模型
-    def initialize(self, model_args=model_args):
-        self.inference_pipeline = pipeline(**model_args) 
-
-    def emotion_recognition(self, 
-                            audio:bytes,
-                            granularity="utterance", # 中间特征的维度，"utterance": [*768], "frame": [T*768]
-                            extract_embedding=False, # 是否保留提取到的特征，False表示不保留中间特征，只保留最终结果
-                            output_dir="./outputs"  # 中间特征的保存位置(extract_embedding为true时有效)
-                            ):
-        rec_result = self.inference_pipeline(audio, granularity=granularity, extract_embedding=extract_embedding, output_dir=output_dir)
-
-        # 保存结果
-        json_list = []
-        for emotion, score in zip(rec_result[0]["labels"], rec_result[0]["scores"]):
-            json_list.append({"emotion": emotion.split("/")[-1], "weight": round(score,4)})
-        recognize_result = json.dumps(json_list)
-        return recognize_result
-
-if __name__ == "__main__":
-    model = EmotionRecognition()
-    recognize_result = model.emotion_recognition("https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+import json
+
+model_args = {
+    "task": Tasks.emotion_recognition,
+    "model": "iic/emotion2vec_base_finetuned"    # Alternative: iic/emotion2vec_plus_seed, iic/emotion2vec_plus_base, iic/emotion2vec_plus_large and iic/emotion2vec_base_finetuned
+    # "device": 不用指定，"device" 默认为gpu
+}
+
+class EmotionRecognition:
+"""
+    9-class emotion
+    0: angry
+    1: disgusted
+    2: fearful
+    3: happy
+    4: neutral
+    5: other
+    6: sad
+    7: surprised
+    8: unknown
+    return :  
+    [{"emotion": "angry", "weight": },
+     {"emotion": "disgusted", "weight": },
+     ...
+    ]
+"""
+    def __init__(self) -> None:
+        self.initialize(model_args=model_args)
+
+    # 初始化模型
+    def initialize(self, model_args=model_args):
+        self.inference_pipeline = pipeline(**model_args) 
+
+    def emotion_recognition(self, 
+                            audio:bytes,
+                            granularity="utterance", # 中间特征的维度，"utterance": [*768], "frame": [T*768]
+                            extract_embedding=False, # 是否保留提取到的特征，False表示不保留中间特征，只保留最终结果
+                            output_dir="./outputs"  # 中间特征的保存位置(extract_embedding为true时有效)
+                            ):
+        rec_result = self.inference_pipeline(audio, granularity=granularity, extract_embedding=extract_embedding, output_dir=output_dir)
+
+        # 保存结果
+        json_list = []
+        for emotion, score in zip(rec_result[0]["labels"], rec_result[0]["scores"]):
+            json_list.append({"emotion": emotion.split("/")[-1], "weight": round(score,4)})
+        recognize_result = json.dumps(json_list)
+        return recognize_result
+
+if __name__ == "__main__":
+    model = EmotionRecognition()
+    recognize_result = model.emotion_recognition("https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
     print(recognize_result)
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