feat(transcriber): 添加 MLX Whisper 转录器支持

- 新增 MLXWhisperTranscriber 类，支持在 Apple 平台上进行转录。
- 更新 transcriber_provider.py，动态导入 MLX Whisper 转录器并添加相应的环境变量检查。
- 修改 .env.example 文件，更新 TRANSCRIBER_TYPE 配置说明以包含 mlx-whisper 选项。

.env.example

@@ -28,5 +28,5 @@ MODEl_PROVIDER= #如果不是openai 请修改 deepseek/qwen
 FFMPEG_BIN_PATH=
 
 # transcriber 相关配置
-TRANSCRIBER_TYPE=fast-whisper # fast-whisper/bcut/kuaishou
+TRANSCRIBER_TYPE=fast-whisper # fast-whisper/bcut/kuaishou/mlx-whisper(仅Apple平台)
 WHISPER_MODEL_SIZE=base

backend/app/transcriber/mlx_whisper_transcriber.py

@@ -0,0 +1,82 @@
+import mlx_whisper
+from pathlib import Path
+import os
+import platform
+
+from app.decorators.timeit import timeit
+from app.models.transcriber_model import TranscriptSegment, TranscriptResult
+from app.transcriber.base import Transcriber
+from app.utils.logger import get_logger
+from app.utils.path_helper import get_model_dir
+from events import transcription_finished
+
+logger = get_logger(__name__)
+
+class MLXWhisperTranscriber(Transcriber):
+    def __init__(
+            self,
+            model_size: str = "base",
+            device: str = None,  # MLX 会自动选择最佳设备
+    ):
+        # 检查平台
+        if platform.system() != "Darwin":
+            raise RuntimeError("MLX Whisper 仅支持 Apple 平台")
+            
+        # 检查环境变量
+        if os.environ.get("TRANSCRIBER_TYPE") != "mlx-whisper":
+            raise RuntimeError("必须设置环境变量 TRANSCRIBER_TYPE=mlx-whisper 才能使用 MLX Whisper")
+            
+        self.model_size = model_size
+        self.model_name = f"whisper-{model_size}-mlx"
+        self.model_path = None
+        
+        # 设置模型路径
+        model_dir = get_model_dir("mlx-whisper")
+        self.model_path = os.path.join(model_dir, self.model_name)
+        
+        # 确保模型目录存在
+        Path(model_dir).mkdir(parents=True, exist_ok=True)
+        
+        logger.info(f"初始化 MLX Whisper 转录器，模型：{self.model_name}")
+
+    @timeit
+    def transcript(self, file_path: str) -> TranscriptResult:
+        try:
+            # 使用 MLX Whisper 进行转录
+            result = mlx_whisper.transcribe(
+                file_path,
+                path_or_hf_repo=f"mlx-community/{self.model_name}"
+            )
+            
+            # 转换为标准格式
+            segments = []
+            full_text = ""
+            
+            for segment in result["segments"]:
+                text = segment["text"].strip()
+                full_text += text + " "
+                segments.append(TranscriptSegment(
+                    start=segment["start"],
+                    end=segment["end"],
+                    text=text
+                ))
+            
+            transcript_result = TranscriptResult(
+                language=result.get("language", "unknown"),
+                full_text=full_text.strip(),
+                segments=segments,
+                raw=result
+            )
+            
+            self.on_finish(file_path, transcript_result)
+            return transcript_result
+            
+        except Exception as e:
+            logger.error(f"MLX Whisper 转写失败：{e}")
+            raise e
+
+    def on_finish(self, video_path: str, result: TranscriptResult) -> None:
+        logger.info("MLX Whisper 转写完成")
+        transcription_finished.send({
+            "file_path": video_path,
+        }) 

backend/app/transcriber/transcriber_provider.py

@@ -1,4 +1,5 @@
 import os
+import platform
 
 from app.transcriber.whisper import WhisperTranscriber
 from app.transcriber.bcut import BcutTranscriber
@@ -6,13 +7,26 @@ from app.transcriber.kuaishou import KuaishouTranscriber
 from app.utils.logger import get_logger
 logger = get_logger(__name__)
 
+# 只在Apple平台且设置了环境变量时才导入MLX Whisper
+if platform.system() == "Darwin" and os.environ.get("TRANSCRIBER_TYPE") == "mlx-whisper":
+    try:
+        from app.transcriber.mlx_whisper_transcriber import MLXWhisperTranscriber
+        MLX_WHISPER_AVAILABLE = True
+        logger.info("MLX Whisper 可用，已导入")
+    except ImportError:
+        MLX_WHISPER_AVAILABLE = False
+        logger.warning("MLX Whisper 导入失败，可能未安装或平台不支持")
+else:
+    MLX_WHISPER_AVAILABLE = False
+
 logger.info('初始化转录服务提供器')
 
 # 维护各种转录器的单例实例
 _transcribers = {
     'whisper': None,
     'bcut': None,
-    'kuaishou': None
+    'kuaishou': None,
+    'mlx-whisper': None
 }
 
 def get_whisper_transcriber(model_size="base", device="cuda"):
@@ -51,13 +65,29 @@ def get_kuaishou_transcriber():
             raise
     return _transcribers['kuaishou']
 
+def get_mlx_whisper_transcriber(model_size="base"):
+    """获取 MLX Whisper 转录器实例"""
+    if not MLX_WHISPER_AVAILABLE:
+        logger.warning("MLX Whisper 不可用，请确保在Apple平台且已安装mlx_whisper")
+        raise ImportError("MLX Whisper 不可用，请确保在Apple平台且已安装mlx_whisper")
+        
+    if _transcribers['mlx-whisper'] is None:
+        logger.info(f'创建 MLX Whisper 转录器实例，参数：{model_size}')
+        try:
+            _transcribers['mlx-whisper'] = MLXWhisperTranscriber(model_size=model_size)
+            logger.info('MLX Whisper 转录器创建成功')
+        except Exception as e:
+            logger.error(f"MLX Whisper 转录器创建失败: {e}")
+            raise
+    return _transcribers['mlx-whisper']
+
 def get_transcriber(transcriber_type="fast-whisper", model_size="base", device="cuda"):
     """
     获取指定类型的转录器实例
     
     参数:
-        transcriber_type: 转录器类型，支持 "fast-whisper", "bcut", "kuaishou"
-        model_size: 模型大小，whisper 特有参数
+        transcriber_type: 转录器类型，支持 "fast-whisper", "bcut", "kuaishou", "mlx-whisper"(仅Apple平台)
+        model_size: 模型大小，whisper 和 mlx-whisper 特有参数
         device: 设备类型，whisper 特有参数
     
     返回:
@@ -67,6 +97,12 @@ def get_transcriber(transcriber_type="fast-whisper", model_size="base", device="
     if transcriber_type == "fast-whisper":
         whisper_model_size = os.environ.get("WHISPER_MODEL_SIZE",model_size)
         return get_whisper_transcriber(whisper_model_size, device=device)
+    elif transcriber_type == "mlx-whisper":
+        if not MLX_WHISPER_AVAILABLE:
+            logger.warning("MLX Whisper 不可用，回退到 fast-whisper")
+            whisper_model_size = os.environ.get("WHISPER_MODEL_SIZE",model_size)
+            return get_whisper_transcriber(whisper_model_size, device=device)
+        return get_mlx_whisper_transcriber(whisper_model_size)
     elif transcriber_type == "bcut":
         return get_bcut_transcriber()
     elif transcriber_type == "kuaishou":