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
193 lines
7.5 KiB
Python
193 lines
7.5 KiB
Python
"""FastAPI 应用工厂（对齐 server/main.py 风格）。
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路由概览：
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GET / -> 版本号
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GET /health -> 存活探针
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GET /logs -> 实时日志页（公开）
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GET /docs -> Swagger UI（Basic Auth）
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POST /api/tasks/chunk-uploads/... -> 分片上传（建会话/查状态/传片/complete）
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GET /api/tasks -> 任务列表
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GET /api/tasks/{id} -> 任务状态
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GET /api/tasks/{id}/subtitle -> 下载字幕
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GET /api/logs -> 查询日志（按级别过滤）
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"""
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from __future__ import annotations
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import asyncio
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import logging
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import threading
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from contextlib import asynccontextmanager
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from fastapi import Depends, FastAPI
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from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
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from fastapi.responses import HTMLResponse, JSONResponse
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from sqlalchemy.orm import Session
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from .config import get_settings
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from .controllers import log_router, task_router, upload_router
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from .database import get_db, init_db_schema
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from .security import require_docs_auth
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from .services.cache_cleaner import purge_expired_cache, run_forever as run_cache_cleaner
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from .services.log_buffer import init_log_buffer
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from .services.reaper import reap_stale_sessions
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from .views.history_html import render as render_history_html
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from .views.home_html import render as render_home_html
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from .views.logs_html import render as render_logs_html
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# 日志分层：
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# - audio2text logger 始终设 DEBUG，确保所有记录（含子步骤）都能产生。
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# - 控制台 handler 用配置的 level：config=info 时控制台只显示简略。
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# - 内存 handler 始终 DEBUG（全收），/api/logs?level= 查询时按参数过滤可见级别。
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# 这样 /logs 页面切到 DEBUG 能看到详细，而控制台仍按 config.level 简略输出。
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_LOG_LEVEL_MAP = {"debug": logging.DEBUG, "info": logging.INFO,
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"warning": logging.WARNING, "error": logging.ERROR}
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_console_level = _LOG_LEVEL_MAP.get(get_settings().logging.level, logging.INFO)
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logging.basicConfig(
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level=logging.DEBUG,
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format="%(asctime)s %(levelname)s %(name)s: %(message)s",
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)
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# 控制台 handler（basicConfig 安装在 root）按配置级别过滤
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for _h in logging.getLogger().handlers:
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_h.setLevel(_console_level)
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logger = logging.getLogger("audio2text")
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logger.setLevel(logging.DEBUG)
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# 内存日志缓冲：捕获所有 audio2text.* 日志，供 /logs 页面查询。
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# handler 级别设为 DEBUG（最低），由 /api/logs 查询时按 level 参数过滤。
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_memory_handler = init_log_buffer(get_settings().logging.buffer_size)
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_memory_handler.setLevel(logging.DEBUG)
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logger.addHandler(_memory_handler)
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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try:
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init_db_schema()
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logger.info("SQLite 表已就绪。")
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except Exception as exc: # pragma: no cover
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logger.error("初始化数据库失败：%s", exc)
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logger.info(
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"audio2text 启动：ASR=%s/%s，翻译=%s",
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get_settings().asr.model, get_settings().asr.device, get_settings().translation.device,
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)
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# 启动时清理一次被放弃的分片会话
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try:
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await asyncio.to_thread(reap_stale_sessions)
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except Exception as exc: # pragma: no cover
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logger.warning("启动 reaper 失败：%s", exc)
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# 启动 GPU 调度线程（常驻，串行处理 ASR+翻译，模型复用）
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try:
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from .services.scheduler import start_scheduler
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start_scheduler()
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except Exception as exc: # pragma: no cover
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logger.warning("启动 GPU 调度线程失败：%s", exc)
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# 缓存清理：启动时跑一次 + 后台定时循环（守护线程，随进程退出）
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s = get_settings()
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try:
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await asyncio.to_thread(purge_expired_cache)
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except Exception as exc: # pragma: no cover
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logger.warning("启动缓存清理失败：%s", exc)
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try:
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t = threading.Thread(
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target=run_cache_cleaner,
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args=(s.storage.cache_cleanup_interval_hours,),
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name="cache-cleaner",
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daemon=True,
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)
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t.start()
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logger.info(
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"缓存清理调度已启动：保留期 %d 天，间隔 %d 小时。",
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s.storage.cache_retention_days, s.storage.cache_cleanup_interval_hours,
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)
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except Exception as exc: # pragma: no cover
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logger.warning("启动缓存清理调度失败：%s", exc)
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yield
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def create_app() -> FastAPI:
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s = get_settings()
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app = FastAPI(
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title="audio2text",
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description=(
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"音频/视频转双语字幕服务。\n\n"
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"- 上传视频 → ffmpeg 提取音频 → faster-whisper 识别英语 → 断句+时间戳重算\n"
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" → NLLB 翻译为中文 → 双语 SRT\n\n"
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"模型不共驻：ASR 与翻译分阶段加载，翻译时独占显存跑大 batch。\n"
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"/docs 需 Basic Auth，凭据见 config.yaml 的 docs 段。"
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),
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version="1.0.0",
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docs_url=None,
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redoc_url=None,
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openapi_url=None,
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lifespan=lifespan,
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)
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app.include_router(upload_router)
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app.include_router(task_router)
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app.include_router(log_router)
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# 受 Basic Auth 保护的文档接口
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@app.get("/openapi.json")
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def protected_openapi(_: str = Depends(require_docs_auth)) -> JSONResponse:
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return JSONResponse(app.openapi())
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@app.get("/docs")
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def protected_docs(_: str = Depends(require_docs_auth)):
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return get_swagger_ui_html(
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openapi_url="/openapi.json", title="audio2text docs", swagger_favicon_url=""
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)
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@app.get("/redoc")
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def protected_redoc(_: str = Depends(require_docs_auth)):
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return get_redoc_html(
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openapi_url="/openapi.json", title="audio2text docs", redoc_favicon_url=""
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)
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@app.get("/", response_class=HTMLResponse)
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def home_page() -> HTMLResponse:
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return HTMLResponse(render_home_html())
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@app.get("/health")
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def health() -> dict:
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"""存活探针 + 设备信息。
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返回 torch 版本、cuda 可用性、GPU 名称、配置的 device，
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便于一眼区分 CPU/GPU 容器是否正确调度到对应硬件。
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torch 导入失败时（理论上不会，因为镜像已装 torch）降级为仅 status。
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"""
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info: dict = {"status": "ok"}
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try:
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import torch
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info["torch"] = torch.__version__
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info["cuda_available"] = torch.cuda.is_available()
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if torch.cuda.is_available():
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info["gpu"] = torch.cuda.get_device_name(0)
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info["gpu_count"] = torch.cuda.device_count()
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except Exception as e: # pragma: no cover
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info["torch_error"] = str(e)
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s = get_settings()
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info["asr_device"] = s.asr.device
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info["asr_model"] = s.asr.model
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info["asr_compute_type"] = s.asr.compute_type
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info["asr_batch_size"] = s.asr.batch_size
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info["asr_language"] = s.asr.language
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info["translation_device"] = s.translation.device
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info["translation_model"] = s.translation.model
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info["translation_batch_size"] = s.translation.batch_size
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info["translation_sort_by_length"] = s.translation.sort_by_length
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return info
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@app.get("/history", response_class=HTMLResponse)
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def history_page() -> HTMLResponse:
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return HTMLResponse(render_history_html())
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@app.get("/logs", response_class=HTMLResponse)
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def logs_page() -> HTMLResponse:
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return HTMLResponse(render_logs_html())
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return app
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app = create_app()
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