feat: 新增模型管理和供应商配置功能

### v1.1.0
- #### Added
  - 新增 AI 笔记风格选择
  - 新增 AI 笔记返回格式选择
  - 添加 AI 自定义笔记备注 Prompt
  - 添加任务失败重试
  - 添加全局设置页，可在设置页进行模型设置

- #### Optimize
  - 优化前端样式，优化用户体验
  - 增加生成中间产物，可用于失败后加快生成速度
- #### Fix
  - 修复视频截图视频过早删除错误

backend/app/transcriber/bcut.py

@@ -0,0 +1,251 @@
+import json
+import logging
+import time
+from typing import Optional, List, Dict, Union
+
+import requests
+
+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 events import transcription_finished
+
+__version__ = "0.0.3"
+
+API_BASE_URL = "https://member.bilibili.com/x/bcut/rubick-interface"
+
+# 申请上传
+API_REQ_UPLOAD = API_BASE_URL + "/resource/create"
+
+# 提交上传
+API_COMMIT_UPLOAD = API_BASE_URL + "/resource/create/complete"
+
+# 创建任务
+API_CREATE_TASK = API_BASE_URL + "/task"
+
+# 查询结果
+API_QUERY_RESULT = API_BASE_URL + "/task/result"
+
+logger = get_logger(__name__)
+
+class BcutTranscriber(Transcriber):
+    """必剪 语音识别接口"""
+    headers = {
+        'User-Agent': 'Bilibili/1.0.0 (https://www.bilibili.com)',
+        'Content-Type': 'application/json'
+    }
+
+    def __init__(self):
+        self.session = requests.Session()
+        self.task_id = None
+        self.__etags = []
+
+        self.__in_boss_key: Optional[str] = None
+        self.__resource_id: Optional[str] = None
+        self.__upload_id: Optional[str] = None
+        self.__upload_urls: List[str] = []
+        self.__per_size: Optional[int] = None
+        self.__clips: Optional[int] = None
+
+        self.__etags: List[str] = []
+        self.__download_url: Optional[str] = None
+        self.task_id: Optional[str] = None
+        
+    def _load_file(self, file_path: str) -> bytes:
+        """读取文件内容"""
+        with open(file_path, 'rb') as f:
+            return f.read()
+
+    def _upload(self, file_path: str) -> None:
+        """申请上传"""
+        file_binary = self._load_file(file_path)
+        if not file_binary:
+            raise ValueError("无法读取文件数据")
+            
+        payload = json.dumps({
+            "type": 2,
+            "name": "audio.mp3",
+            "size": len(file_binary),
+            "ResourceFileType": "mp3",
+            "model_id": "8",
+        })
+
+        resp = self.session.post(
+            API_REQ_UPLOAD,
+            data=payload,
+            headers=self.headers
+        )
+        resp.raise_for_status()
+        resp = resp.json()
+        resp_data = resp["data"]
+
+        self.__in_boss_key = resp_data["in_boss_key"]
+        self.__resource_id = resp_data["resource_id"]
+        self.__upload_id = resp_data["upload_id"]
+        self.__upload_urls = resp_data["upload_urls"]
+        self.__per_size = resp_data["per_size"]
+        self.__clips = len(resp_data["upload_urls"])
+
+        logger.info(
+            f"申请上传成功, 总计大小{resp_data['size'] // 1024}KB, {self.__clips}分片, 分片大小{resp_data['per_size'] // 1024}KB: {self.__in_boss_key}"
+        )
+        self.__upload_part(file_binary)
+        self.__commit_upload()
+
+    def __upload_part(self, file_binary: bytes) -> None:
+        """上传音频数据"""
+        for clip in range(self.__clips):
+            start_range = clip * self.__per_size
+            end_range = min((clip + 1) * self.__per_size, len(file_binary))
+            logger.info(f"开始上传分片{clip}: {start_range}-{end_range}")
+            resp = self.session.put(
+                self.__upload_urls[clip],
+                data=file_binary[start_range:end_range],
+                headers={'Content-Type': 'application/octet-stream'}
+            )
+            resp.raise_for_status()
+            etag = resp.headers.get("Etag", "").strip('"')
+            self.__etags.append(etag)
+            logger.info(f"分片{clip}上传成功: {etag}")
+
+    def __commit_upload(self) -> None:
+        """提交上传数据"""
+        data = json.dumps({
+            "InBossKey": self.__in_boss_key,
+            "ResourceId": self.__resource_id,
+            "Etags": ",".join(self.__etags),
+            "UploadId": self.__upload_id,
+            "model_id": "8",
+        })
+        resp = self.session.post(
+            API_COMMIT_UPLOAD,
+            data=data,
+            headers=self.headers
+        )
+        resp.raise_for_status()
+        resp = resp.json()
+        print('Bili',resp)
+        if resp.get("code") != 0:
+            error_msg = f"上传提交失败: {resp.get('message', '未知错误')}"
+            logger.error(error_msg)
+            raise Exception(error_msg)
+            
+        self.__download_url = resp["data"]["download_url"]
+        logger.info(f"提交成功，下载链接: {self.__download_url}")
+
+    def _create_task(self) -> str:
+        """开始创建转换任务"""
+        resp = self.session.post(
+            API_CREATE_TASK, json={"resource": self.__download_url, "model_id": "8"}, headers=self.headers
+        )
+        resp.raise_for_status()
+        resp = resp.json()
+        if resp.get("code") != 0:
+            error_msg = f"创建任务失败: {resp.get('message', '未知错误')}"
+            logger.error(error_msg)
+            raise Exception(error_msg)
+            
+        self.task_id = resp["data"]["task_id"]
+        logger.info(f"任务已创建: {self.task_id}")
+        return self.task_id
+
+    def _query_result(self) -> dict:
+        """查询转换结果"""
+        resp = self.session.get(
+            API_QUERY_RESULT, 
+            params={"model_id": 7, "task_id": self.task_id}, 
+            headers=self.headers
+        )
+        resp.raise_for_status()
+        resp = resp.json()
+        if resp.get("code") != 0:
+            error_msg = f"查询结果失败: {resp.get('message', '未知错误')}"
+            logger.error(error_msg)
+            raise Exception(error_msg)
+            
+        return resp["data"]
+
+    @timeit
+    def transcript(self, file_path: str) -> TranscriptResult:
+        """执行识别过程，符合 Transcriber 接口"""
+        try:
+            logger.info(f"开始处理文件: {file_path}")
+            
+            # 上传文件
+            logger.info("正在上传文件...")
+            self._upload(file_path)
+            
+            # 创建任务
+            logger.info("提交转录任务...")
+            self._create_task()
+            
+            # 轮询检查任务状态
+            logger.info("等待转录结果...")
+            task_resp = None
+            max_retries = 500
+            for i in range(max_retries):
+                task_resp = self._query_result()
+                
+                if task_resp["state"] == 4:  # 完成状态
+                    break
+                elif task_resp["state"] == 3:  # 失败状态
+                    error_msg = f"B站ASR任务失败，状态码: {task_resp['state']}"
+                    logger.error(error_msg)
+                    raise Exception(error_msg)
+                    
+                # 每隔一段时间打印进度
+                if i % 10 == 0:
+                    logger.info(f"转录进行中... {i}/{max_retries}")
+                    
+                time.sleep(1)
+                
+            if not task_resp or task_resp["state"] != 4:
+                error_msg = f"B站ASR任务未能完成，状态: {task_resp.get('state') if task_resp else 'Unknown'}"
+                logger.error(error_msg)
+                raise Exception(error_msg)
+                
+            # 解析结果
+            logger.info("转录成功，处理结果...")
+            result_json = json.loads(task_resp["result"])
+            
+            # 提取分段数据
+            segments = []
+            full_text = ""
+            
+            for u in result_json.get("utterances", []):
+                text = u.get("transcript", "").strip()
+                # B站ASR返回的时间戳是毫秒，需要转换为秒
+                start_time = float(u.get("start_time", 0)) / 1000.0
+                end_time = float(u.get("end_time", 0)) / 1000.0
+                
+                full_text += text + " "
+                segments.append(TranscriptSegment(
+                    start=start_time,
+                    end=end_time,
+                    text=text
+                ))
+            
+            # 创建结果对象
+            result = TranscriptResult(
+                language=result_json.get("language", "zh"),
+                full_text=full_text.strip(),
+                segments=segments,
+                raw=result_json
+            )
+            
+            # 触发完成事件
+            # self.on_finish(file_path, result)
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"B站ASR处理失败: {str(e)}")
+            raise
+
+    def on_finish(self, video_path: str, result: TranscriptResult) -> None:
+        """转录完成的回调"""
+        logger.info(f"B站ASR转写完成: {video_path}")
+        transcription_finished.send({
+            "file_path": video_path,
+        })

backend/app/transcriber/kuaishou.py

@@ -0,0 +1,115 @@
+import requests
+import logging
+import os
+from typing import Union, List, Dict, Optional
+
+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 events import transcription_finished
+
+logger = get_logger(__name__)
+
+class KuaishouTranscriber(Transcriber):
+    """快手语音识别实现"""
+    
+    API_URL = "https://ai.kuaishou.com/api/effects/subtitle_generate"
+    
+    def __init__(self):
+        pass
+
+    def _load_file(self, file_path: str) -> bytes:
+        """读取文件内容"""
+        with open(file_path, 'rb') as f:
+            return f.read()
+
+    def _submit(self, file_path: str) -> dict:
+        """提交识别请求"""
+        try:
+            file_binary = self._load_file(file_path)
+            
+            payload = {
+                "typeId": "1"
+            }
+            
+            # 使用文件名作为上传文件名
+            file_name = os.path.basename(file_path)
+            files = [('file', (file_name, file_binary, 'audio/mpeg'))]
+            
+            logger.info(f"开始向快手API提交请求，文件: {file_name}")
+            response = requests.post(self.API_URL, data=payload, files=files, timeout=300)
+            response.raise_for_status()  # 检查HTTP错误
+            
+            result = response.json()
+            print('result',result)
+            # 检查快手API返回是否包含错误
+            if "data" not in result or result.get("code", 0) != 0:
+                error_msg = f"快手API返回错误: {result.get('message', '未知错误')}"
+                logger.error(error_msg)
+                raise Exception(error_msg)
+                
+            return result
+            
+        except requests.exceptions.RequestException as e:
+            error_msg = f"快手ASR请求网络错误: {str(e)}"
+            logger.error(error_msg)
+            raise
+        except Exception as e:
+            error_msg = f"快手ASR请求处理错误: {str(e)}"
+            logger.error(error_msg)
+            raise
+
+    @timeit
+    def transcript(self, file_path: str) -> TranscriptResult:
+        """执行转录过程，符合 Transcriber 接口"""
+        try:
+            logger.info(f"开始处理文件: {file_path}")
+            
+            # 提交请求并获取结果
+            logger.info("向快手API提交识别请求...")
+            result_data = self._submit(file_path)
+            
+            logger.info("请求成功，处理结果...")
+            
+            # 提取分段数据
+            segments = []
+            full_text = ""
+            
+            # 解析快手API返回的文本段
+            texts = result_data.get('data', {}).get('text', [])
+            for u in texts:
+                text = u.get('text', '').strip()
+                start_time = float(u.get('start_time', 0))
+                end_time = float(u.get('end_time', 0))
+                
+                full_text += text + " "
+                segments.append(TranscriptSegment(
+                    start=start_time,
+                    end=end_time,
+                    text=text
+                ))
+            
+            # 创建结果对象
+            result = TranscriptResult(
+                language="zh",  # 快手API可能不返回语言信息，默认为中文
+                full_text=full_text.strip(),
+                segments=segments,
+                raw=result_data
+            )
+            
+            # 触发完成事件
+            # self.on_finish(file_path, result)
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"快手ASR处理失败: {str(e)}")
+            raise
+
+    def on_finish(self, video_path: str, result: TranscriptResult) -> None:
+        """转录完成的回调"""
+        logger.info(f"快手ASR转写完成: {video_path}")
+        transcription_finished.send({
+            "file_path": video_path,
+        })

backend/app/transcriber/transcriber_provider.py

@@ -31,15 +31,15 @@ _transcribers = {
 
 def get_whisper_transcriber(model_size="base", device="cuda"):
     """获取 Whisper 转录器实例"""
-    if _transcribers['fast-whisper'] is None:
+    if  _transcribers['fast-whisper'] is None:
         logger.info(f'创建 Whisper 转录器实例，参数：{model_size}, {device}')
         try:
-            _transcribers['fast-whisper'] = WhisperTranscriber(model_size=model_size, device=device)
+            _transcribers['whisper'] = WhisperTranscriber(model_size=model_size, device=device)
             logger.info('Whisper 转录器创建成功')
         except Exception as e:
             logger.error(f"Whisper 转录器创建失败: {e}")
             raise
-    return _transcribers['fast-whisper']
+    return _transcribers['whisper']
 
 def get_bcut_transcriber():
     """获取 Bcut 转录器实例"""

backend/app/transcriber/whisper.py

@@ -4,14 +4,19 @@ from app.decorators.timeit import timeit
 from app.models.transcriber_model import TranscriptSegment, TranscriptResult
 from app.transcriber.base import Transcriber
 from app.utils.env_checker import is_cuda_available, is_torch_installed
+from app.utils.logger import get_logger
 from app.utils.path_helper import get_model_dir
 
 from events import transcription_finished
+from pathlib import Path
+import os
+from tqdm import tqdm
+from huggingface_hub import snapshot_download
 
 '''
  Size of the model to use (tiny, tiny.en, base, base.en, small, small.en, distil-small.en, medium, medium.en, distil-medium.en, large-v1, large-v2, large-v3, large, distil-large-v2, distil-large-v3, large-v3-turbo, or turbo
 '''
-
+logger=get_logger(__name__)
 
 class WhisperTranscriber(Transcriber):
     # TODO:修改为可配置
@@ -31,15 +36,25 @@ class WhisperTranscriber(Transcriber):
 
         self.compute_type = compute_type or ("float16" if self.device == "cuda" else "int8")
 
-        model_path = get_model_dir("whisper")
+        model_dir = get_model_dir("whisper")
+        model_path = os.path.join(model_dir, f"whisper-{model_size}")
+        if not Path(model_path).exists():
+            logger.info(f"模型 whisper-{model_size} 不存在，开始下载...")
+            repo_id = f"guillaumekln/faster-whisper-{model_size}"
+            snapshot_download(
+                repo_id,
+                local_dir=model_path,
+                local_dir_use_symlinks=False,
+            )
+            logger.info("模型下载完成")
+
         self.model = WhisperModel(
             model_size,
             device=self.device,
-            # compute_type="int8",  # 或 "float16"
+            compute_type=self.compute_type,
             cpu_threads=cpu_threads,
-            download_root=model_path
+            download_root=model_dir
         )
-
     @staticmethod
     def is_torch_installed() -> bool:
         try:
@@ -88,7 +103,7 @@ class WhisperTranscriber(Transcriber):
                 segments=segments,
                 raw=info
             )
-            self.on_finish(file_path, result)
+            # self.on_finish(file_path, result)
             return result
         except Exception as e:
             print(f"转写失败：{e}")