diff --git a/backend/app/routers/config.py b/backend/app/routers/config.py
index 2dcf094..0fecc48 100644
--- a/backend/app/routers/config.py
+++ b/backend/app/routers/config.py
@@ -87,13 +87,10 @@ _downloading: dict[str, str] = {}  # model_size -> status ("downloading" | "done
 
 
 def _check_whisper_model_exists(model_size: str, subdir: str = "whisper") -> bool:
-    """检查指定 whisper 模型是否已完整下载到本地。
-
-    只看目录会把"上次下载中断剩下的空目录"误判为已下载；以 model.bin 落盘为准。
-    """
+    """检查指定 whisper 模型是否已下载到本地。"""
     model_dir = get_model_dir(subdir)
-    model_path = Path(os.path.join(model_dir, f"whisper-{model_size}"))
-    return (model_path / "model.bin").exists()
+    model_path = os.path.join(model_dir, f"whisper-{model_size}")
+    return Path(model_path).exists()
 
 
 @router.get("/transcriber_models_status")
@@ -230,49 +227,30 @@ async def sys_check():
 
 @router.get("/deploy_status")
 async def deploy_status():
-    """返回部署监控所需的所有状态信息。
-
-    每一项都做容错：torch 没装 / Whisper 配置读取失败 / FFmpeg 不可用 都不应让整个
-    endpoint 500 把监控页打死。
-    """
+    """返回部署监控所需的所有状态信息"""
+    import torch
     import os
-
-    # CUDA 状态：torch 是 fast-whisper 路径才需要的依赖；轻量部署可能没装
-    cuda_info = {"available": False, "version": None, "gpu_name": None, "torch_installed": False}
-    try:
-        import torch
-        cuda_info["torch_installed"] = True
-        cuda_available = torch.cuda.is_available()
-        cuda_info["available"] = cuda_available
-        if cuda_available:
-            cuda_info["version"] = torch.version.cuda
-            try:
-                cuda_info["gpu_name"] = torch.cuda.get_device_name(0)
-            except Exception as e:
-                logger.warning(f"读取 GPU 名称失败: {e}")
-    except ImportError:
-        # torch 未安装：保持 available=False；插件 / web 监控页能识别
-        pass
-    except Exception as e:
-        logger.warning(f"CUDA 状态检测失败: {e}")
-
-    # Whisper 配置（任何失败回落到空，监控页不应被这个打死）
-    model_size = None
-    transcriber_type = None
-    try:
-        transcriber_cfg = transcriber_config_manager.get_config()
-        model_size = transcriber_cfg.get("whisper_model_size")
-        transcriber_type = transcriber_cfg.get("transcriber_type")
-    except Exception as e:
-        logger.warning(f"读取转写器配置失败: {e}")
-
+    
+    # CUDA 状态
+    cuda_available = torch.cuda.is_available()
+    cuda_info = {
+        "available": cuda_available,
+        "version": torch.version.cuda if cuda_available else None,
+        "gpu_name": torch.cuda.get_device_name(0) if cuda_available else None,
+    }
+    
+    # Whisper 模型状态（从配置文件读取，与前端设置同步）
+    transcriber_cfg = transcriber_config_manager.get_config()
+    model_size = transcriber_cfg["whisper_model_size"]
+    transcriber_type = transcriber_cfg["transcriber_type"]
+    
     # FFmpeg 状态
     try:
         ensure_ffmpeg_or_raise()
         ffmpeg_ok = True
-    except Exception:
+    except:
         ffmpeg_ok = False
-
+    
     return R.success(data={
         "backend": {"status": "running", "port": int(os.getenv("BACKEND_PORT", 8483))},
         "cuda": cuda_info,
diff --git a/backend/app/transcriber/whisper.py b/backend/app/transcriber/whisper.py
index 217394f..fca49f6 100644
--- a/backend/app/transcriber/whisper.py
+++ b/backend/app/transcriber/whisper.py
@@ -50,20 +50,12 @@ class WhisperTranscriber(Transcriber):
 
         model_dir = get_model_dir("whisper")
         model_path = os.path.join(model_dir, f"whisper-{model_size}")
-        # 仅看目录存在不够：一次失败的 / 中断的下载会留下空 / 半成品目录，
-        # 后面 WhisperModel 加载时会以 'Unable to open file model.bin' 抛错。
-        # 必须以 model.bin 这个核心权重文件落盘为准。
-        model_bin = Path(model_path) / "model.bin"
-        if not model_bin.exists():
-            if Path(model_path).exists():
-                logger.warning(
-                    f"检测到 {model_path} 目录存在但缺少 model.bin（可能上次下载中断），将重新拉取"
-                )
-            else:
-                logger.info(f"模型 whisper-{model_size} 不存在，开始下载...")
+        if not Path(model_path).exists():
+            logger.info(f"模型 whisper-{model_size} 不存在，开始下载...")
             repo_id = MODEL_MAP[model_size]
             model_path = snapshot_download(
                 repo_id,
+
                 local_dir=model_path,
             )
             logger.info("模型下载完成")