Merge remote-tracking branch 'origin/master' into dev

# Conflicts:
#	backend/app/transcriber/transcriber_provider.py

.env.example

@@ -25,4 +25,8 @@ QWEN_API_BASE_URL=
 QWEN_MODEL=
 MODEl_PROVIDER= #如果不是openai 请修改 deepseek/qwen
 # FFMPEG 配置
-FFMPEG_BIN_PATH=
+FFMPEG_BIN_PATH=
+
+# transcriber 相关配置
+TRANSCRIBER_TYPE=fast-whisper # fast-whisper/bcut/kuaishou/mlx-whisper(仅Apple平台)
+WHISPER_MODEL_SIZE=base

README.md

@@ -29,7 +29,7 @@ BiliNote 是一个开源的 AI 视频笔记助手，支持通过哔哩哔哩、Y
 注意：由于 项目部署在 Cloudflare Pages，访问速度可能存在一些问题，请耐心等待。
 
 ## 📦 Windows 打包版
-本项目提供了 Windows 系统的 exe 文件，可在[release](https://github.com/JefferyHcool/BiliNote/releases/tag/v1.0.1) 进行下载。
+本项目提供了 Windows 系统的 exe 文件，可在[release](https://github.com/JefferyHcool/BiliNote/releases/tag/v1.0.1) 进行下载。**注意一定要在没有中文路径的环境下运行。**
 
 
 ## 🔧 功能特性
@@ -145,7 +145,7 @@ QWEN_API_KEY=xxx
 - BiliNote 交流QQ群：785367111
 - BiliNote 交流微信群:
   
-  <img src="./doc/wechat.png" alt="wechat" style="zoom:33%;" />
+  <img src="https://common-1304618721.cos.ap-chengdu.myqcloud.com/20250424091751.png" alt="wechat" style="zoom:33%;" />
 
 ## 📜 License
 

backend/.env.example

@@ -21,3 +21,7 @@ QWEN_API_KEY=
 QWEN_API_BASE_URL=
 QWEN_MODEL=
 MODEl_PROVIDER= #如果不是openai 请修改 deepseek/qwen
+
+# transcriber 相关配置
+TRANSCRIBER_TYPE=fast-whisper # fast-whisper/bcut/kuaishou
+WHISPER_MODEL_SIZE=base

backend/app/services/note.py

@@ -18,7 +18,7 @@ from app.models.notes_model import AudioDownloadResult
 from app.enmus.note_enums import DownloadQuality
 from app.models.transcriber_model import TranscriptResult
 from app.transcriber.base import Transcriber
-from app.transcriber.transcriber_provider import get_transcriber
+from app.transcriber.transcriber_provider import get_transcriber,_transcribers
 from app.transcriber.whisper import WhisperTranscriber
 import re
 
@@ -43,7 +43,7 @@ class NoteGenerator:
     def __init__(self):
         self.model_size: str = 'base'
         self.device: Union[str, None] = None
-        self.transcriber_type = 'fast-whisper'
+        self.transcriber_type = os.getenv('TRANSCRIBER_TYPE','fast-whisper')
         self.transcriber = self.get_transcriber()
         # TODO 需要更换为可调节
 
@@ -86,9 +86,9 @@ class NoteGenerator:
         :param transcriber: 选择的转义器
         :return:
         '''
-        if self.transcriber_type == 'fast-whisper':
+        if self.transcriber_type in _transcribers.keys():
-            logger.info("使用Whisper")
+            logger.info(f"使用{self.transcriber_type}转义器")
-            return get_transcriber()
+            return get_transcriber(transcriber_type=self.transcriber_type)
         else:
             logger.warning("不支持的转义器")
             raise ValueError(f"不支持的转义器：{self.transcriber}")

backend/app/transcriber/mlx_whisper_transcriber.py

@@ -0,0 +1,88 @@
+import mlx_whisper
+from pathlib import Path
+import os
+import platform
+from huggingface_hub import snapshot_download
+
+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"
+    ):
+        # 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"mlx-community/whisper-{model_size}"
+        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)
+        # 检查并下载模型
+        if not Path(self.model_path).exists():
+            logger.info(f"模型 {self.model_name} 不存在，开始下载...")
+            snapshot_download(
+                self.model_name,
+                local_dir=self.model_path,
+                local_dir_use_symlinks=False,
+            )
+            logger.info("模型下载完成")
+        
+        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"{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

@@ -1,19 +1,113 @@
+import os
+import platform
+
 from app.transcriber.whisper import WhisperTranscriber
+from app.transcriber.bcut import BcutTranscriber
+from app.transcriber.kuaishou import KuaishouTranscriber
 from app.utils.logger import get_logger
 logger = get_logger(__name__)
 
-logger.info('实例化transcriber')
+# 只在Apple平台且设置了环境变量时才导入MLX Whisper
-# TODO:后面需要加入逻辑选择
+if platform.system() == "Darwin" and os.environ.get("TRANSCRIBER_TYPE") == "mlx-whisper":
-_transcriber = None
+    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
 
-def get_transcriber(model_size="base", device="cuda"):
+logger.info('初始化转录服务提供器')
-    global _transcriber
 
-    if _transcriber is None:
+# 维护各种转录器的单例实例
-        logger.info('不存在 transcriber ，开始实例化transcriber。')
+_transcribers = {
+    'bcut': None,
+    'kuaishou': None,
+    'mlx-whisper': None,
+    'fast-whisper':None
+}
+
+def get_whisper_transcriber(model_size="base", device="cuda"):
+    """获取 Whisper 转录器实例"""
+    if  _transcribers['fast-whisper'] is None:
+        logger.info(f'创建 Whisper 转录器实例，参数：{model_size}, {device}')
         try:
-            _transcriber = WhisperTranscriber(model_size=model_size, device=device)
+            _transcribers['whisper'] = WhisperTranscriber(model_size=model_size, device=device)
-            logger.info(f'实例化transcriber成功。参数：{model_size}, {device} ')
+            logger.info('Whisper 转录器创建成功')
         except Exception as e:
-            logger.error(f"实例化transcriber失败，请检查是否安装whisper。{e}")
+            logger.error(f"Whisper 转录器创建失败: {e}")
-    return _transcriber
+            raise
+    return _transcribers['whisper']
+
+def get_bcut_transcriber():
+    """获取 Bcut 转录器实例"""
+    if _transcribers['bcut'] is None:
+        logger.info('创建 Bcut 转录器实例')
+        try:
+            _transcribers['bcut'] = BcutTranscriber()
+            logger.info('Bcut 转录器创建成功')
+        except Exception as e:
+            logger.error(f"Bcut 转录器创建失败: {e}")
+            raise
+    return _transcribers['bcut']
+
+def get_kuaishou_transcriber():
+    """获取快手转录器实例"""
+    if _transcribers['kuaishou'] is None:
+        logger.info('创建快手转录器实例')
+        try:
+            _transcribers['kuaishou'] = KuaishouTranscriber()
+            logger.info('快手转录器创建成功')
+        except Exception as e:
+            logger.error(f"快手转录器创建失败: {e}")
+            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", "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:
+        对应类型的转录器实例
+    """
+    logger.info(f'获取转录器，类型: {transcriber_type}')
+    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":
+        whisper_model_size = os.environ.get("WHISPER_MODEL_SIZE",model_size)
+        if not MLX_WHISPER_AVAILABLE:
+            logger.warning("MLX Whisper 不可用，回退到 fast-whisper")
+            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":
+        return get_kuaishou_transcriber()
+    else:
+        logger.warning(f'未知转录器类型 "{transcriber_type}"，使用默认 whisper')
+        whisper_model_size = os.environ.get("WHISPER_MODEL_SIZE",model_size)
+        return get_whisper_transcriber(whisper_model_size, device)

backend/main.py

@@ -36,7 +36,7 @@ async def startup_event():
 async def startup_event():
     register_handler()
     ensure_ffmpeg_or_raise()
-    get_transcriber()
+    get_transcriber(transcriber_type=os.getenv("TRANSCRIBER_TYPE","fast-whisper"))
     init_video_task_table()
     init_provider_table()