优化部分报红代码

asr_service.py

@@ -184,6 +184,10 @@ class ASRService:
         print(f"正在识别: {audio_path}")
 
         # 执行识别
+        # 确保模型已正确加载
+        if self._model is None:
+            raise RuntimeError("模型加载失败，无法执行识别")
+
         result = self._model.generate(
             input=str(audio_path),
             batch_size_s=batch_size_s,
@@ -330,7 +334,11 @@ def recognize_audio(
         List[Sentence]: 识别结果
     """
     service = ASRService(model_name=model_name, device=device)
-    return service.recognize(audio_path)
+    result = service.recognize(audio_path)
+    # 如果返回的是字典（return_raw=True的情况），则解析为Sentence列表
+    if isinstance(result, dict):
+        return service._parse_result([result])
+    return result
 
 
 if __name__ == "__main__":

test_asr.py

@@ -59,11 +59,11 @@ def test_single_audio(audio_path: str, model_name: str = "paraformer-zh"):
 
     # 导出 JSON
     json_path = f"{base_name}_result.json"
-    service.export_to_json(sentences, json_path)
+    service.export_to_json(sentences, json_path)  # type: ignore
 
     # 导出 SRT 字幕
     srt_path = f"{base_name}_result.srt"
-    service.export_to_srt(sentences, srt_path)
+    service.export_to_srt(sentences, srt_path)  # type: ignore
 
     print("\n" + "=" * 70)
     print("📁 输出文件:")
@@ -103,7 +103,7 @@ def test_batch(audio_dir: str, model_name: str = "paraformer-zh"):
 
             # 导出
             base_name = audio_path.stem
-            service.export_to_json(sentences, f"{base_name}_result.json")
+            service.export_to_json(sentences, f"{base_name}_result.json")  # type: ignore
         except Exception as e:
             print(f"  ✗ 失败: {e}")