73110848f4
- scheduler: ffmpeg 异步线程 + GPU 串行调度 + 模型复用（2N→2 次加载） - pipeline: 阶段拆分（extract/asr/translate），中间数据存 Task 字段 - translate_service: 长度排序批处理，padding 浪费减少 91% - model_manager: ASR/翻译不共驻，BatchedInferencePipeline 批量解码 - 日志分级: INFO=任务流转里程碑，DEBUG=进度详情；默认 INFO - 前端: 日志最新在上+滚动感知+退避轮询；24h 时间；上传中状态显示 - /health: 返回完整 Whisper/NLLB 配置 - upload_service: 单事务 complete + 扩展名白名单 - task_router: 合并 UploadSession 虚拟任务到列表 - Dockerfile: CPU/GPU 独立构建链，deps 缓存稳定 - prefetch_models: 安装时预下载模型权重
75 lines
1.8 KiB
Python
75 lines
1.8 KiB
Python
"""任务与分片上传接口 DTO。"""
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from __future__ import annotations
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from datetime import datetime
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from pydantic import BaseModel, Field
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# ---------------- 分片上传（对齐 server 的协议） ----------------
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class CreateSessionRequest(BaseModel):
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"""创建一个分片上传会话。"""
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filename: str = Field(..., description="客户端原始文件名")
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size_bytes: int = Field(..., ge=0, description="文件总字节数")
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chunk_size: int = Field(..., gt=0, description="分片大小（字节）")
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total_chunks: int = Field(..., gt=0, description="分片总数")
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class CreateSessionResponse(BaseModel):
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upload_id: str
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filename: str
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size_bytes: int
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chunk_size: int
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total_chunks: int
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class SessionStatusResponse(BaseModel):
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upload_id: str
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filename: str
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size_bytes: int
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chunk_size: int
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total_chunks: int
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uploaded_chunks: list[int]
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completed: bool
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task_id: int | None = Field(None, description="completed=true 时指向 Task.id")
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class ChunkUploadResponse(BaseModel):
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upload_id: str
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index: int
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uploaded_chunks: list[int]
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class CompleteResponse(BaseModel):
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"""complete 的回执：创建出转写任务，返回 task_id。"""
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task_id: int
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filename: str
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size_bytes: int
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status: str
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# ---------------- 任务查询 ----------------
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class TaskResponse(BaseModel):
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id: int
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filename: str
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status: str
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progress: float
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error: str | None
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created_at: datetime
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updated_at: datetime
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# 上传会话虚拟任务用：is_upload=true 时 id 无意义（固定 0），
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# 前端用 upload_id 轮询 /api/tasks/chunk-uploads/{upload_id}/status
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size_bytes: int | None = None
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is_upload: bool = False
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upload_id: str | None = None
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class TaskListResponse(BaseModel):
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tasks: list[TaskResponse]
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total: int
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