Merge pull request #425 from pumpkinperson996/agent/fix-whisper-model-selection

Fix Whisper model selection caching

backend/app/transcriber/transcriber_provider.py

@@ -1,5 +1,6 @@
 import os
 import platform
+import threading
 from enum import Enum
 
 from app.transcriber.groq import GroqTranscriber
@@ -38,17 +39,29 @@ _transcribers = {
     TranscriberType.GROQ: None,
 }
 
+# Cache instances together with their constructor configuration. The
+# transcriber choice and Whisper model size can be changed from the frontend,
+# so caching by transcriber type alone would keep using the first loaded model.
+_transcriber_configs = {key: None for key in _transcribers}
+_transcriber_init_lock = threading.Lock()
+
 # 公共实例初始化函数
 def _init_transcriber(key: TranscriberType, cls, *args, **kwargs):
-    if _transcribers[key] is None:
-        logger.info(f'创建 {cls.__name__} 实例: {key}')
-        try:
-            _transcribers[key] = cls(*args, **kwargs)
+    init_config = (args, tuple(sorted(kwargs.items())))
+    with _transcriber_init_lock:
+        instance = _transcribers[key]
+        if instance is None or _transcriber_configs[key] != init_config:
+            action = "创建" if instance is None else "按新配置重新创建"
+            logger.info(f'{action} {cls.__name__} 实例: {key}')
+            try:
+                new_instance = cls(*args, **kwargs)
+            except Exception as e:
+                logger.error(f"{cls.__name__} 创建失败: {e}")
+                raise
+            _transcribers[key] = new_instance
+            _transcriber_configs[key] = init_config
             logger.info(f'{cls.__name__} 创建成功')
-        except Exception as e:
-            logger.error(f"{cls.__name__} 创建失败: {e}")
-            raise
-    return _transcribers[key]
+        return _transcribers[key]
 
 # 各类型获取方法
 def get_groq_transcriber():
@@ -70,13 +83,13 @@ def get_mlx_whisper_transcriber(model_size="base"):
     return _init_transcriber(TranscriberType.MLX_WHISPER, MLXWhisperTranscriber, model_size=model_size)
 
 # 通用入口
-def get_transcriber(transcriber_type="fast-whisper", model_size="base", device="cuda"):
+def get_transcriber(transcriber_type="fast-whisper", model_size=None, device="cuda"):
     """
     获取指定类型的转录器实例
 
     参数:
         transcriber_type: 支持 "fast-whisper", "mlx-whisper", "bcut", "kuaishou", "groq"
-        model_size: 模型大小，适用于 whisper 类
+        model_size: 模型大小，适用于 whisper 类；未提供时才读取环境变量默认值
         device: 设备类型（如 cuda / cpu），仅 whisper 使用
 
     返回:
@@ -90,7 +103,9 @@ def get_transcriber(transcriber_type="fast-whisper", model_size="base", device="
         logger.warning(f'未知转录器类型 "{transcriber_type}"，默认使用 fast-whisper')
         transcriber_enum = TranscriberType.FAST_WHISPER
 
-    whisper_model_size = os.environ.get("WHISPER_MODEL_SIZE", model_size)
+    # The explicit value normally comes from the persisted frontend setting and
+    # must take precedence over Docker's startup default.
+    whisper_model_size = model_size or os.environ.get("WHISPER_MODEL_SIZE", "base")
 
     if transcriber_enum == TranscriberType.FAST_WHISPER:
         return get_whisper_transcriber(whisper_model_size, device=device)