From 4746e0363f5ba14210eae3b07829491250cb7c15 Mon Sep 17 00:00:00 2001 From: catShark <1716967236@qq.com> Date: Sun, 16 Aug 2026 23:58:25 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20VRSub=20=E5=8D=95=E4=BD=93=E5=BA=94?= =?UTF-8?q?=E7=94=A8=EF=BC=88WOV=20=E5=8D=95=E6=9C=BA=E7=89=88=EF=BC=89?= =?UTF-8?q?=E5=88=9D=E5=A7=8B=E6=8F=90=E4=BA=A4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 为视频生成 VR 双眼字幕的单体实现：FastAPI 后端、调度器与全部节点 （提音/转写/翻译/ASS/抽帧/OCR/LLM 过滤）在单进程内运行。 - 节点协议（wov_sdk 数据模型）与分布式版保持一致，预留回退桥梁 - 工作流即数据：DAG 存于 workflows/*.json，模型/链路改动只改数据 - 调度器：拓扑顺序执行、断点续跑（产物重建）、任务暂停/继续 - 抽帧按帧间隔（select 按帧号精确取帧），VLM OCR 与 LLM 过滤使用 自适应线程池弹性并发，并打印数据处理速度进度日志 - 100% 行覆盖率（pytest --cov-fail-under=100） --- .gitignore | 22 + AGENTS.md | 339 +++++++ README.md | 45 + manifests/ass.json | 21 + manifests/echo.json | 22 + manifests/ffmpeg.json | 21 + manifests/frame-extract.json | 15 + manifests/llm-filter.json | 15 + manifests/llm.json | 21 + manifests/subtitle-ocr.json | 15 + manifests/vlm.json | 15 + manifests/whisper.json | 21 + nodes/__init__.py | 9 + nodes/adaptive_pool.py | 177 ++++ nodes/ass.py | 98 ++ nodes/echo.py | 50 + nodes/ffmpeg.py | 79 ++ nodes/frame_extract.py | 178 ++++ nodes/llm.py | 103 ++ nodes/llm_filter.py | 171 ++++ nodes/subtitle_ocr.py | 162 +++ nodes/vlm.py | 193 ++++ nodes/whisper.py | 274 +++++ pyproject.toml | 41 + src/wov_app/__init__.py | 5 + src/wov_app/config.py | 28 + src/wov_app/db.py | 398 ++++++++ src/wov_app/logging.py | 31 + src/wov_app/main.py | 82 ++ src/wov_app/maintenance.py | 129 +++ src/wov_app/registry.py | 110 ++ src/wov_app/routers/__init__.py | 5 + src/wov_app/routers/apps.py | 213 ++++ src/wov_app/routers/workflows.py | 140 +++ src/wov_app/scheduler.py | 308 ++++++ src/wov_app/schemas.py | 22 + src/wov_app/seed.py | 48 + src/wov_sdk/__init__.py | 28 + src/wov_sdk/models.py | 321 ++++++ testdata/ocr_notext.png | Bin 0 -> 1532 bytes testdata/ocr_text.png | Bin 0 -> 13849 bytes testdata/speech_60s.wav | Bin 0 -> 1920052 bytes testdata/subtitle_10s.mp4 | Bin 0 -> 19770 bytes testdata/test_real_hav_sub.png | Bin 0 -> 130314 bytes tests/conftest.py | 43 + tests/test_adaptive_pool.py | 148 +++ tests/test_api.py | 34 + tests/test_apps_api.py | 313 ++++++ tests/test_crop_js.js | 32 + tests/test_db.py | 294 ++++++ tests/test_frontend_crop.py | 28 + tests/test_integration_subtitle_ocr.py | 90 ++ tests/test_integration_vlm.py | 65 ++ tests/test_integration_whisper.py | 52 + tests/test_llm_filter.py | 257 +++++ tests/test_maintenance.py | 165 +++ tests/test_models.py | 174 ++++ tests/test_nodes.py | 1295 ++++++++++++++++++++++++ tests/test_ocr_flow.py | 401 ++++++++ tests/test_registry.py | 116 +++ tests/test_scheduler.py | 655 ++++++++++++ tests/test_seed.py | 108 ++ tests/test_uvicorn_smoke.py | 34 + tests/test_workflow_api.py | 117 +++ uv.lock | 1106 ++++++++++++++++++++ web/admin.html | 46 + web/assets/app.js | 647 ++++++++++++ web/assets/crop.js | 38 + web/assets/styles.css | 348 +++++++ web/index.html | 70 ++ web/tasks.html | 49 + web/workflow.html | 63 ++ workflows/demo.json | 78 ++ workflows/ocr-subtitle.json | 62 ++ workflows/zh-direct.json | 66 ++ 75 files changed, 10969 insertions(+) create mode 100644 .gitignore create mode 100644 AGENTS.md create mode 100644 README.md create mode 100755 manifests/ass.json create mode 100755 manifests/echo.json create mode 100755 manifests/ffmpeg.json create mode 100644 manifests/frame-extract.json create mode 100644 manifests/llm-filter.json create mode 100755 manifests/llm.json create mode 100644 manifests/subtitle-ocr.json create mode 100644 manifests/vlm.json create mode 100755 manifests/whisper.json create mode 100644 nodes/__init__.py create mode 100644 nodes/adaptive_pool.py create mode 100755 nodes/ass.py create mode 100755 nodes/echo.py create mode 100755 nodes/ffmpeg.py create mode 100644 nodes/frame_extract.py create mode 100755 nodes/llm.py create mode 100644 nodes/llm_filter.py create mode 100644 nodes/subtitle_ocr.py create mode 100644 nodes/vlm.py create mode 100755 nodes/whisper.py create mode 100644 pyproject.toml create mode 100644 src/wov_app/__init__.py create mode 100644 src/wov_app/config.py create mode 100644 src/wov_app/db.py create mode 100644 src/wov_app/logging.py create mode 100644 src/wov_app/main.py create mode 100644 src/wov_app/maintenance.py create mode 100644 src/wov_app/registry.py create mode 100644 src/wov_app/routers/__init__.py create mode 100755 src/wov_app/routers/apps.py create mode 100755 src/wov_app/routers/workflows.py create mode 100755 src/wov_app/scheduler.py create mode 100644 src/wov_app/schemas.py create mode 100644 src/wov_app/seed.py create mode 100644 src/wov_sdk/__init__.py create mode 100755 src/wov_sdk/models.py create mode 100644 testdata/ocr_notext.png create mode 100644 testdata/ocr_text.png create mode 100644 testdata/speech_60s.wav create mode 100644 testdata/subtitle_10s.mp4 create mode 100644 testdata/test_real_hav_sub.png create mode 100644 tests/conftest.py create mode 100644 tests/test_adaptive_pool.py create mode 100644 tests/test_api.py create mode 100644 tests/test_apps_api.py create mode 100644 tests/test_crop_js.js create mode 100644 tests/test_db.py create mode 100644 tests/test_frontend_crop.py create mode 100644 tests/test_integration_subtitle_ocr.py create mode 100644 tests/test_integration_vlm.py create mode 100644 tests/test_integration_whisper.py create mode 100644 tests/test_llm_filter.py create mode 100644 tests/test_maintenance.py create mode 100755 tests/test_models.py create mode 100644 tests/test_nodes.py create mode 100644 tests/test_ocr_flow.py create mode 100644 tests/test_registry.py create mode 100644 tests/test_scheduler.py create mode 100644 tests/test_seed.py create mode 100644 tests/test_uvicorn_smoke.py create mode 100644 tests/test_workflow_api.py create mode 100644 uv.lock create mode 100755 web/admin.html create mode 100644 web/assets/app.js create mode 100644 web/assets/crop.js create mode 100755 web/assets/styles.css create mode 100755 web/index.html create mode 100644 web/tasks.html create mode 100755 web/workflow.html create mode 100644 workflows/demo.json create mode 100644 workflows/ocr-subtitle.json create mode 100644 workflows/zh-direct.json diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..f4b673f --- /dev/null +++ b/.gitignore @@ -0,0 +1,22 @@ +# Python +__pycache__/ +*.py[cod] +.coverage +htmlcov/ +.venv/ +*.egg-info/ +build/ +dist/ + +# 本地环境变量（含 API 密钥，禁止入库） +.env + +# 本地数据与产物（上传文件、SQLite、节点中间产物） +data/ + +# 模型权重（大文件，不入库；如需共享请走对象存储或模型仓库） +model/ +# 编辑器与系统 +.DS_Store +Thumbs.db +.idea/ diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..94d99d8 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,339 @@ +# VRSub（单体版） + +本仓库是"为视频生成 VR 双眼字幕"的单体应用（WOV AI Workflow Platform 的 +单机实现）：FastAPI 后端、工作流调度器与全部节点（提音 / 转写 / 翻译 / +ASS）在**同一个进程**内运行，不再启动子进程、不再走节点 HTTP 协议。 +由原分布式多仓库（wov-api / wov-web / wov-sdk / wov-node-*）合并而来， +本 AGENTS.md 汇总了各仓库的约定与规范。 + +## 架构概览 + +``` +vrsub/ +├── src/wov_sdk/ # 协议数据模型（NodeManifest/InvokeRequest/InvokeResponse/ +│ # WorkflowDefinition 等），与分布式版保持一致 +├── src/wov_app/ # 应用层：main/config/db/registry/scheduler/seed/routers +│ └── routers/ # apps.py（用户端）、workflows.py（管理端） +├── nodes/ # 进程内节点实现：echo/ffmpeg/whisper/llm/ass +├── manifests/ # 各节点清单 JSON（echo.json/ffmpeg.json/...） +├── workflows/ # 默认工作流定义 JSON（模型/链路均为数据，改模型不改代码） +├── web/ # 静态前端（index/tasks/admin/workflow + assets） +├── model/ # 本地 whisper 权重（gitignored） +├── data/ # SQLite + 上传/产物存储（gitignored） +└── tests/ # 全部单元/API/冒烟测试（100% 覆盖率） +``` + +### 核心机制 + +- **节点注册表**（`src/wov_app/registry.py`）：启动时把 `manifests/*.json` 与 + `nodes/*.py` 的 `invoke` 处理器静态注册到进程内字典，调度器按 + `node_type` 直接调用。协议数据模型不变，为将来回退分布式保留兼容桥梁。 +- **调度器**（`src/wov_app/scheduler.py`）：后台线程轮询 SQLite 中的 QUEUED + 任务，按工作流 DAG 拓扑顺序调用节点，产物按 + `data/storage/runs//steps//` 落盘并登记到 artifacts 表。 +- **前端**：由 FastAPI 静态挂载 `web/`，节点注册/实例管理页面已移除， + 仅保留应用中心、任务管理、管理后台（工作流）与工作流编排。 + +## 节点输入/输出协议 + +节点统一签名 `invoke(request: InvokeRequest) -> InvokeResponse`，通过产物 URI +交换数据（节点之间不直接调用，不共享内存状态）。 + +| 节点 ID（node_type） | 输入 | 输出 | 说明 | +| --- | --- | --- | --- | +| `echo` | `text` / `file_uri` | `text`、`file_uri` | 示例节点，验证协议链路 | +| `ffmpeg-extract` | `video_uri` | `audio_uri`（WAV） | 参数：`sample_rate`、`channels` | +| `faster-whisper` | `audio_uri`（16kHz 单声道） | `srt_uri` | 参数：`language`、`task`、`model_path`、`device`、`compute_type`、`beam_size`、`vad_filter`（默认开）、`condition_on_previous_text`、`chunk_seconds` | +| `llm-translate` | `srt_uri` | `cn_srt_uri` | 参数：`target_language`、`model` | +| `vlm-ocr` | `image_uri` | `text`、`text_uri` | 直接调本地 Ollama 多模态模型（glm-ocr）的 `/api/chat` 做视频帧 OCR（流式 + 5s 上限），参数：`model`、`ollama_host`、`prompt`、`timeout_seconds`、`keep_alive`、`num_predict`、`temperature`、`repeat_penalty` | +| `frame-extract` | `video_uri` | `frames_manifest`、`frame_count` | 按**帧间隔**抽帧（解析 fps → step=round(间隔秒×fps)，ffmpeg select 按帧号精确取帧，帧时间=帧号/fps 无累计偏差）并 crop 裁切字幕区域，参数：`interval_seconds`（默认 0.5）、`crop`（[x,y,w,h] 0~1） | +| `subtitle-ocr` | `frames_manifest` | `srt_uri`、`count` | 自适应线程池并发逐帧调 vlm-ocr → 垃圾过滤（无文字帧）→ 相同字幕合并（记录最后可见帧）→ 组装 SRT，消失时间=最后可见帧+采样间隔（间隔从帧清单推导），参数：`min_chars`、`min_alnum_ratio`、`garbage_tokens`、`pool_min_workers`/`pool_max_workers`/`pool_window_seconds`/`pool_fast_threshold`/`pool_slow_threshold` | +| `llm-filter` | `srt_uri` | `srt_uri`、`kept`、`removed` | LLM 过滤无意义字幕（自适应线程池并发判断）：每条连同前后各 `context_size`（默认 10）条纯文本（不含时间戳）分批给 LLM，仅判断目标字幕是否多余/无意义，判定删除则该条连同时间戳移除并重新编号，参数：`context_size`、`model`、`pool_min_workers`/`pool_max_workers`/`pool_window_seconds`/`pool_fast_threshold`/`pool_slow_threshold` | +| `srt-to-dual-eye-ass` | `cn_srt_uri` | `ass_uri` | 参数：`resolution`，如 `3840x1920` | + +### 模型权重解析（本地优先） + +whisper 节点按以下顺序解析模型路径，默认避免从远端下载： + +1. 请求参数 `model_path`；裸模型名（不含路径分隔符）会在 `model/<名称>` 下解析。 +2. 环境变量 `WHISPER_MODEL_PATH`。 +3. 本地候选目录（存在且含 `model.bin` 即使用）： + - 单体根目录 `model/faster-whisper-large-v3`。 + - `nodes/model/faster-whisper-large-v3`。 +4. 兜底：`large-v3`（需要联网从 Hugging Face 下载）。 + +把权重放在 `model/` 目录即可完全离线运行。当前已下载模型： + +- `model/faster-whisper-large-v3`：通用转写模型（demo 工作流）。 +- `model/whisper-large-v2-translate-zh-v0.2-st-ct2`：中文直出模型 + （`chickenrice0721/whisper-large-v2-translate-zh-v0.2-st-ct2`），配合 + `task=translate` 直接生成中文，无需 LLM 翻译（zh-direct 工作流）。 +- `model/whisper-large-v3-translate-zh-v0.1-lt-ct2`：早期中文直出模型， + 已无工作流引用，保留在盘上待处理。 + +### 内置工作流 + +| ID | 名称 | 链路 | 说明 | +| --- | --- | --- | --- | +| `demo` | 视频字幕生成 | 提音 → 转写 → LLM 翻译 → ASS | 通用链路，翻译走 SiliconFlow | +| `zh-direct` | 中文直出字幕 | 提音 → 中文转写 → ASS | 中文直出模型，无 LLM 步骤 | +| `ocr-subtitle` | 字幕OCR提取 | 抽帧 → 逐帧 OCR → 汇总 SRT → LLM 过滤 | 提取烧录字幕做基准数据；前端框选 crop；LLM 过滤多余/无意义字幕 | + +最终产物按 `上传文件名.标识.时间戳` 重命名（如 `test01.zh-CN.20260815123000.srt`）， +标识优先取节点的 `target_language` 参数，否则用产物别名。 + +### 任务参数覆盖（前端框选） + +创建任务时可携带可选 `params` 表单字段（JSON）：`{"节点ID": {"参数": 值}}`， +随任务持久化（param_overrides），调度执行时合并进对应节点参数。字幕 OCR +前端把框选的 `crop` 按此传给 `frame-extract` 节点。 + +### 切换模型不改代码 + +- 模型是工作流 DAG 中 asr 节点的 `model_path` 参数（**数据**），两个内置工作流 + 均已显式声明：demo 用 `faster-whisper-large-v3`，zh-direct 用中文直出模型。 +- 切换模型 = 改 `workflows/*.json` 或管理页面 DAG JSON → 保存新版本 → 发布， + 全程不涉及代码；新库启动时从 JSON 重新 seed。 +- 默认工作流定义存放在 `workflows/*.json`（数据文件），代码只负责加载。 + +### 长音频处理 + +**当前策略：分块转写，默认每 1 分钟一块**（`chunk_seconds=60`，2026-08 调整）。 +whisper 节点内部用 ffmpeg 把音频切成块 → 逐块转写 → 按偏移合并为完整 SRT： + +- 内存/显存有界（模型 + 单块音频），任意时长可处理，失败粒度小。 +- 分块是**应用层工程策略**，与模型训练格式无关：whisper 训练/推理都按 30s + 窗口解码，任意块大小均适用。 +- 每块 `offset = 块序号 × chunk_seconds`，SRT 序号连续；切块失败自动回退 + 整段单次转写。 +- `chunk_seconds=0` 可关闭分块；大小按工作流 DAG 参数（数据）调整。 +- 同时默认 `condition_on_previous_text=false`（每块/每窗口独立解码，防重复）。 +- **`vad_filter` 默认开启**（2026-08 用户决定）：过滤静音段提速并减少无语音处 + 幻觉。注意 VAD 靠压缩时间轴回映射（SpeechTimestampsMap），长静音场景曾实测 + 错位（30s 静音致第二段语音从 ~40s 落到 10s）；如需极致对齐可显式传 + `vad_filter=false`。 +- **分块偏移按每块实际时长累积**（WAV 头精确）：ffmpeg 切出的块实际时长不等于 + 块长（如 60.05s），用 `块序号×块长` 的假设值会随块数累积漂移；改为按真实 + 时长累加后，字幕时间轴与原始音频严格一致。 +- 参考：openai/whisper 重复问题（issue #1026/#1046，PR #1052/#1253）、 + SYSTRAN/faster-whisper issue #465。 + +### glm-ocr 重复循环问题与源头修复（2026-08） + +- **根因**：glm-ocr 生成阶段存在已知 bug（M-RoPE delta 未传递，大图触发 + 重复循环；GitHub #454 / #16892）。`keep_alive` 与其无关（实测无效）。 +- **源头修复**： + 1. `frame-extract` 裁切后把帧**压缩到 720p 内**（仅缩小，保持宽高比）—— + 过大输入图是触发条件之一。 + 2. `vlm` 请求体 `options.repeat_penalty`（默认 1.2）+ `num_predict` + （默认 256）压制重复。 + 3. `subtitle-ocr` 增加 `max_result_chars`（默认 200）：模型输出超长视为 + 异常（重复循环等），**直接报错并跳过该帧**。 +- **glm-ocr 调用结构**：走 Ollama `/api/chat`，识别指令放**系统提示词**， + 用户消息只携带图片（content 为空、images 传 base64），`stream=True` + 逐行接收，`stop: ["\n", "\n答", "答"]` 命中即停止（输出首个换行即停 + 阻止“答：”式重复循环），`temperature` 默认 + 0.3、`repeat_penalty` 默认 1、`num_predict`（默认 256）随请求透传。 +- **gettext 标签防御性提取**（2026-08）：若模型输出含 `` + 标签（旧提示词要求）则取第一个标签内文本，多个标签取第一个防重复循环； + 未按格式输出时回退原文。当前默认提示词为"提取图像中的文字，不要描述 + 图片中的内容"（字幕流水线在 ocr-subtitle 工作流的 subtitle-ocr 节点参数 + 中显式指定，经 subtitle-ocr 透传给 vlm-ocr）。 +- **每次调用 5 秒上限**（2026-08 调整）：vlm 请求 `stream=True` 逐行读取， + 每次调用整体受 5 秒截止时间约束（`timeout_seconds` 参数 / + `VLM_TIMEOUT_SECONDS`，默认 5），超过即终止返回 failed，不再等待后续 + 流式块。 +- **不做文本加工**：除协议要求的 gettext 标签提取外，不再对模型输出做 + 过滤/去重等文本加工，结果原样使用，仅受长度上限约束。 + +### VLM OCR 自适应并发（2026-08） + +subtitle-ocr 逐帧调 vlm-ocr 时使用 `nodes/adaptive_pool.py` 的自适应线程池 +弹性并发： + +- 从 `pool_min_workers`（默认 1）起步，按**滚动窗口**（`pool_window_seconds`， + 默认 10s）统计已完成任务的平均响应时间； +- 平均响应 < `pool_fast_threshold`（默认 0.3s）→ 线程数 +1（上限 + `pool_max_workers`，默认 16）——服务端空闲就加大并发加速处理； +- 平均响应 > `pool_slow_threshold`（默认 1.0s）→ 线程数 -1（下限 1）—— + 服务端变慢就退避，避免盲目并发压垮本地 Ollama； +- 结果按帧顺序返回，SRT 时间轴不受并发影响；worker 需无共享可变状态 + （vlm-ocr 处理器为纯函数，线程安全）。 +### 前端 OCR 框选 + +首页选择工作流后，若 DAG 中存在声明 `crop` 参数的节点（frame-extract）， +自动切换到框选面板：视频预览 + 拖动框选字幕区域 → 生成 crop 比例 → +框选完成后才可提交（未框选时提交按钮禁用）。矩形↔crop 换算为纯函数 +（`web/assets/crop.js`，含 letterbox 处理），由 node 单测覆盖。 + +## 环境变量 + +| 变量 | 默认值 | 说明 | +| --- | --- | --- | +| `WOV_DATA_DIR` | `<根>/data` | 数据目录 | +| `WOV_DB_PATH` | `<根>/data/wov.db` | SQLite 路径 | +| `WOV_STORAGE_DIR` | `<根>/data/storage` | 上传与产物根目录 | +| `WOV_AUTO_SEED` | `1` | 启动时创建 demo 工作流 | +| `WOV_SCHEDULER_ENABLED` | `1` | 启动后台调度器 | +| `WOV_SCHEDULER_INTERVAL_SECONDS` | `1.0` | 调度轮询间隔 | +| `WOV_CLEANUP_ENABLED` | `1` | 开启孤儿数据定时清理 | +| `WOV_CLEANUP_INTERVAL_SECONDS` | `3600` | 孤儿清理扫描周期（秒） | +| `WOV_CLEANUP_GRACE_SECONDS` | `3600` | 孤儿清理宽限期（秒） | +| `WHISPER_MODEL_PATH` | 见上 | 显式指定 whisper 模型路径 | +| `WHISPER_DEVICE` | `auto` | 转写设备 | +| `LLM_API_BASE` | `https://api.siliconflow.cn/v1/chat/completions` | LLM 兼容接口 | +| `LLM_API_KEY` | 空（读 `.env`） | SiliconFlow Bearer Key，存于 gitignored 的 `.env` | +| `LLM_MODEL` | `Qwen/Qwen3.6-35B-A3B` | LLM 模型名 | +| `LLM_TIMEOUT_SECONDS` | `600` | LLM 单请求超时 | +| `OLLAMA_HOST` | `http://192.168.123.70:11434` | Ollama 服务地址 | +| `VLM_MODEL` | `glm-ocr:latest` | VLM OCR 模型 | +| `VLM_PROMPT` | 提取图像中的文字，不要描述图片中的内容 | OCR 提示词（字幕流水线在 ocr-subtitle 工作流的 subtitle-ocr 节点参数中显式指定同一提示词） | +| `VLM_TIMEOUT_SECONDS` | `5` | VLM 单请求整体超时上限（每次调用 5 秒，超时即终止；流式读取同样受此截止约束） | +| `FFMPEG_BIN` | 空 | 显式 ffmpeg 路径（否则 PATH → imageio-ffmpeg） | + +## 启动 + +```bash +uv sync +uv run uvicorn wov_app.main:app --reload +``` + +访问： + +``` +http://127.0.0.1:8000/ 应用中心（上传视频 → 字幕生成） +http://127.0.0.1:8000/tasks.html 任务管理 +http://127.0.0.1:8000/admin.html 管理后台（工作流） +http://127.0.0.1:8000/workflow.html 工作流编排（DAG JSON） +http://127.0.0.1:8000/docs API 文档 +``` + +## Python 环境与 uv 管理 + +- 统一使用 uv 管理虚拟环境和依赖，禁止直接使用 pip 修改依赖。 +- 基础命令：`uv sync`（安装含 dev 组依赖）、`uv run `、 + `uv add `、`uv lock`。 +- 虚拟环境位于 `.venv`，测试依赖在 `[dependency-groups] dev`。 +- 新增依赖时使用 `uv add`，不修改系统 Python 或全局环境。 + +## 孤儿数据清理 + +应用内置后台清理器（`src/wov_app/maintenance.py`），按周期自动清理死数据： + +- **自动删除**：无任务记录的上传/步骤残留目录；COMPLETED 且产物文件全部丢失、 + 超过宽限期（默认 1 小时）的任务记录（下载已全部 404）。 +- **绝不自动删除**：FAILED 任务（可重试）、QUEUED/RUNNING 任务、宽限期内的任务、 + 仍有产物文件的任务。 +- 手动删除任务仅通过删除接口（`DELETE /api/runs/{run_id}`）或管理界面进行。 + +## 任务暂停/继续（2026-08） + +- **状态机**：`QUEUED / RUNNING / PAUSED / COMPLETED / FAILED`。排队中或运行中的 + 任务可暂停（`POST /api/runs/{run_id}/pause`），PAUSED 可继续 + （`POST /api/runs/{run_id}/resume` → 恢复 QUEUED）。 +- **调度器语义**：`next_queued_run` 同时取 QUEUED 与 PAUSED；`execute_run` 在每个 + 节点边界检查状态，被暂停则停下保持 PAUSED（当前节点执行完后才停）； + 继续时从产物表（`restore_run_outputs`，剥去"节点ID."前缀还原输出名）重建已完成 + 节点的输出，**跳过已完成节点断点续跑**，最后补做 final_outputs 收尾。 +- **前端**：任务管理页为 QUEUED/RUNNING 提供"暂停"、PAUSED 提供"继续"按钮。 +- **进度日志（数据处理速度）**： + - 调度器：每节点完成打印"任务 X 进度 i/N 节点: Y 耗时 Zs, 运行累计 Ws"； + - subtitle-ocr：`OCR 进度 X/Y 帧 (Z 帧/s)`；llm-filter：`字幕判定进度 X/Y 条 (Z 条/s)` + （线程池 `on_progress` 回调，每任务完成触发）； + - whisper：分块转写打印"分块 X/Y 完成 offset=... 耗时 Zs (Nx 实时, 累计 ...s)"。 + +## 测试与覆盖率 + +- 必须达到 100% 行覆盖率（pytest 已配置 `--cov-fail-under=100`，范围 + `src/` 与 `nodes/`）。 +- 测试必须调用真实代码路径，不得在测试类中重写业务逻辑来模拟被测功能。 +- **测试必须使用真实数据**：真实音频（合法 WAV/PCM）、真实 JSON/数据库/文件； + 禁止用占位字节（如 `b"x"`）或伪造结构冒充被测数据——假数据测试只能凑覆盖率， + 无法验证真实行为，视为无意义测试。 +- 只允许在 I/O 边界使用 mock/stub：文件系统、网络、子进程、环境变量、时间、 + **模型推理**（重模型不进入单元测试；注入的假模型必须返回结构真实的分段， + 且必须配套真实模型集成测试，见下）。 +- **真实模型集成测试**：使用真实 faster-whisper 模型 + 真实音频素材验证 + 端到端转写（`tests/test_integration_whisper.py`）；本地无模型或素材时跳过， + 有则必须执行，作为对假模型单测的校准。 +- **测试资产存放 `testdata/`**：图片（`ocr_text.png`）、语音（`speech_60s.wav`） + 等测试媒体一次性生成后入库，测试直接复用，**禁止在测试执行时再生成**； + 缺失时测试跳过而非现场生成。大体积视频素材放 `data/testdata/`（gitignored）。OCR 相关资产： +`ocr_text.png`（有文字）、`ocr_notext.png`（无文字帧）、`subtitle_10s.mp4` +（烧录 SUB 001@1-4s / SUB 002@6-9s 的 10s 测试视频）、`test_real_hav_sub.png` +（真实视频字幕截图，VLM 集成测试期望识别出"还有没有什么困扰 或者奇怪的地方吗"）。 +- **开发流程强制 TDD（红-绿-重构）**：任何新功能/修复必须先写失败测试（红）， + 再实现最小代码让其通过（绿），最后重构保持整洁；不允许先写实现后补测试。 +- 测试运行：`uv run pytest`；全部测试位于 `tests/`。 +- 100% 行覆盖率只保证代码路径被覆盖，不覆盖端口占用、防火墙、权限等 + 外部环境状态；端口问题用启动检查、端口检查与 uvicorn 冒烟测试补充。 +- 本地出现 `WinError 10013` / `WinError 10048` 时，先用 + `netstat -ano | findstr :` 确认是否有残留监听进程。 + +## 代码注释规范 + +- 本仓库所有源码（Python、JavaScript、HTML、CSS、TOML 等支持注释的文件） + 必须配有详细中文注释，说明模块/文件职责、核心类与函数的作用以及关键逻辑， + 确保后续维护人员无需通读全部实现即可快速理解工作原理。 +- 新增或修改代码时，必须同步补充或更新对应注释；不得删除已有注释。 +- 测试代码同样必须配有中文注释，说明每条测试验证的行为与覆盖的路径。 +- JSON 数据文件（`manifests/*.json`）按 JSON 规范不支持注释，字段语义以 + `src/wov_sdk/models.py` 的 `NodeManifest` 模型注释和本文档为准； + 修改 JSON 字段时须同步更新文档。 + +## 目标运行环境 + +- 本服务的最终部署目标是 Linux，通常以 Docker/Kubernetes 容器运行。 +- 当前 Windows 只作为本地开发环境，不允许在业务代码中写死 Windows 路径、 + 盘符或 Windows 专用命令。 +- 路径处理统一使用 `pathlib`。 +- ffmpeg 在 Linux 上可使用系统包，也允许通过 `imageio-ffmpeg` 使用内置 + 二进制，节点代码不能假设 ffmpeg 一定在 PATH。 +- 测试必须可以在 Windows 和 Linux 上运行；涉及平台分支的代码应同时覆盖 + 两种路径解析。 + +## Windows / PowerShell 执行规则 + +- 默认 shell 视为 Windows PowerShell 5.1；不要假设 Bash、zsh 或 PowerShell 7。 + 必要时先查 `$PSVersionTable.PSVersion`。 +- 禁止把 Bash 语法交给 PowerShell：`python - <<'PY'`、`cat <<$` 或引号时默认用单引号。 +- 外部程序路径可能有空格时，用 `& 'C:\path with spaces\tool.exe' arg1`。 +- 文件操作优先 PowerShell 原生命令和 `-LiteralPath`。 +- 复杂 Python 不用 `python -c`；涉及 SQL、JSON、中文、反斜杠路径、换行或 + 多层引号时，用仓库脚本或临时 `.py` 文件。 +- 禁止在 PowerShell 用 Bash here-doc。临时传 Python 源码只允许 PowerShell + here-string，且尽量保持 ASCII。 +- Python 源码含中文常量时，不通过 PowerShell 管道传给 `python -`；用 UTF-8 + 脚本文件、仓库脚本或 `\uXXXX`。 +- 搜索文本/文件优先 `rg` / `rg --files`。 +- 数据库或生产内容写操作前先查询当前数据；写入必须有明确筛选条件，禁止 + 无条件 `DELETE` / `UPDATE`。 +- 同一 PowerShell 命令连续失败两次后，停止微调长命令；改短命令、脚本文件、 + 数组 splatting 或分步验证。 + +## 关键设计约束（北极星不变式） + +- 节点之间不直接调用，只通过产物 URI 交换数据；中间产物落在共享存储 + （`data/storage`），不放在节点模块内部。 +- 工作流必须是数据文件或数据库记录（workflow_versions 表存 DAG JSON）， + 不允许把步骤顺序写死在应用代码里。 +- 节点注册表是节点调用的唯一入口；API 与调度器不绕过 registry 直接执行 + 节点逻辑。 +- 协议数据模型（wov_sdk）要长期稳定，宁可先少做功能，也不轻易改协议。 +- 存储、队列、调度器都要通过抽象边界隔离，方便从单机实现替换为分布式实现。 +- 用户端永远只看到"输入 -> 进度 -> 结果"，不暴露工作流细节。 +- 单体对分布式版的三处降级：无子进程隔离、无空闲 TTL 回收（模型常驻， + 仅懒加载）、慢任务无法强制中断（由节点自身超时兜底）。 + +## 单体化说明 + +- 由原 7 个独立仓库合并：wov-api、wov-web、wov-sdk、wov-node-echo、 + wov-node-ffmpeg、wov-node-whisper、wov-node-llm、wov-node-ass。 +- 删除内容：`NodeManager`（子进程生命周期）、节点 HTTP 服务端 + （`wov_sdk.server`）、节点注册/实例管理 API 与页面、node_instances 表、 + 各节点的 `__main__` 进程入口。 +- 保留内容：协议数据模型、工作流 DAG 数据化、调度拓扑执行、上传/进度/下载/ + 重试 API、静态前端、SQLite Repository 层、本地优先模型加载。 diff --git a/README.md b/README.md new file mode 100644 index 0000000..d01116b --- /dev/null +++ b/README.md @@ -0,0 +1,45 @@ +# VRSub + +为视频生成 **VR 双眼字幕**的单体应用：API、调度器与全部节点在同一进程内运行， +单仓库、单环境、单命令启动。由原分布式 WOV 多仓库合并而来，详细约定见 +[AGENTS.md](./AGENTS.md)。 + +## 快速开始 + +```bash +uv sync +uv run uvicorn wov_app.main:app --reload +``` + +访问 `http://127.0.0.1:8000/` 上传视频，自动执行"提音 → 转写 → 翻译 → ASS"。 + +- 模型权重本地优先：把 whisper 模型放在 `model/faster-whisper-large-v3` + （含 `model.bin`）即可完全离线运行。 +- LLM 翻译默认使用 SiliconFlow（`Qwen/Qwen3.6-35B-A3B`），API Key 放在 + gitignored 的 `.env`（`LLM_API_KEY=sk-...`），启动时自动加载；也可用 + `LLM_API_BASE` / `LLM_MODEL` 环境变量覆盖。 + +## 目录 + +| 路径 | 说明 | +| --- | --- | +| `src/wov_sdk/` | 协议数据模型（与分布式版兼容） | +| `src/wov_app/` | 应用层：API、注册表、调度器、数据库 | +| `nodes/` | 进程内节点实现（echo/ffmpeg/whisper/llm/ass） | +| `manifests/` | 节点清单 JSON | +| `web/` | 静态前端 | +| `model/` | 本地模型权重（gitignored） | +| `data/` | SQLite 与存储（gitignored） | +| `tests/` | 测试（100% 行覆盖率） | + +## 测试 + +```bash +uv run pytest +``` + +## 与分布式版的关系 + +- 协议数据模型、工作流 DAG 数据化、调度拓扑执行保持不变。 +- 已移除：子进程节点、节点 HTTP 协议、节点注册/实例管理、TTL 回收。 +- 未来回退分布式时，只需为 `registry.invoke` 重新加上进程边界。 diff --git a/manifests/ass.json b/manifests/ass.json new file mode 100755 index 0000000..8417ff9 --- /dev/null +++ b/manifests/ass.json @@ -0,0 +1,21 @@ +{ + "id": "srt-to-dual-eye-ass", + "name": "SRT to Dual-Eye ASS", + "version": "0.1.0", + "capability": "subtitle", + "repo_dir": "wov-node-ass", + "command": ["python", "-m", "wov_node_ass"], + "env": { + "WOV_NODE_PORT": "0" + }, + "input_schema": { + "cn_srt_uri": "file" + }, + "output_schema": { + "ass_uri": "file" + }, + "max_concurrency": 1, + "idle_ttl_seconds": 60, + "health_timeout_seconds": 10, + "keep_warm": false +} diff --git a/manifests/echo.json b/manifests/echo.json new file mode 100755 index 0000000..2838ca3 --- /dev/null +++ b/manifests/echo.json @@ -0,0 +1,22 @@ +{ + "id": "echo", + "name": "Echo Node", + "version": "0.1.0", + "capability": "echo", + "repo_dir": "wov-node-echo", + "command": ["python", "-m", "wov_node_echo"], + "env": { + "WOV_NODE_PORT": "0" + }, + "input_schema": { + "text": "string" + }, + "output_schema": { + "text": "string", + "file_uri": "file" + }, + "max_concurrency": 1, + "idle_ttl_seconds": 15, + "health_timeout_seconds": 10, + "keep_warm": false +} diff --git a/manifests/ffmpeg.json b/manifests/ffmpeg.json new file mode 100755 index 0000000..9537277 --- /dev/null +++ b/manifests/ffmpeg.json @@ -0,0 +1,21 @@ +{ + "id": "ffmpeg-extract", + "name": "FFmpeg Audio Extract", + "version": "0.1.0", + "capability": "media", + "repo_dir": "wov-node-ffmpeg", + "command": ["python", "-m", "wov_node_ffmpeg"], + "env": { + "WOV_NODE_PORT": "0" + }, + "input_schema": { + "video_uri": "file" + }, + "output_schema": { + "audio_uri": "file" + }, + "max_concurrency": 1, + "idle_ttl_seconds": 60, + "health_timeout_seconds": 10, + "keep_warm": false +} diff --git a/manifests/frame-extract.json b/manifests/frame-extract.json new file mode 100644 index 0000000..bc725b4 --- /dev/null +++ b/manifests/frame-extract.json @@ -0,0 +1,15 @@ +{ + "id": "frame-extract", + "name": "Frame Extract", + "version": "0.1.0", + "capability": "frame-extract", + "repo_dir": "nodes", + "command": ["python", "-m", "frame_extract"], + "env": {}, + "input_schema": { "video_uri": "file" }, + "output_schema": { "frames_manifest": "file", "frame_count": "integer" }, + "max_concurrency": 1, + "idle_ttl_seconds": 300, + "health_timeout_seconds": 10, + "keep_warm": false +} diff --git a/manifests/llm-filter.json b/manifests/llm-filter.json new file mode 100644 index 0000000..80b8e73 --- /dev/null +++ b/manifests/llm-filter.json @@ -0,0 +1,15 @@ +{ + "id": "llm-filter", + "name": "LLM Subtitle Filter", + "version": "0.1.0", + "capability": "llm-filter", + "repo_dir": "nodes", + "command": ["python", "-m", "llm_filter"], + "env": {}, + "input_schema": { "srt_uri": "file" }, + "output_schema": { "srt_uri": "file", "kept": "integer", "removed": "integer" }, + "max_concurrency": 1, + "idle_ttl_seconds": 300, + "health_timeout_seconds": 10, + "keep_warm": false +} diff --git a/manifests/llm.json b/manifests/llm.json new file mode 100755 index 0000000..86d2670 --- /dev/null +++ b/manifests/llm.json @@ -0,0 +1,21 @@ +{ + "id": "llm-translate", + "name": "LLM Subtitle Translate", + "version": "0.1.0", + "capability": "llm", + "repo_dir": "wov-node-llm", + "command": ["python", "-m", "wov_node_llm"], + "env": { + "WOV_NODE_PORT": "0" + }, + "input_schema": { + "srt_uri": "file" + }, + "output_schema": { + "cn_srt_uri": "file" + }, + "max_concurrency": 1, + "idle_ttl_seconds": 60, + "health_timeout_seconds": 10, + "keep_warm": false +} diff --git a/manifests/subtitle-ocr.json b/manifests/subtitle-ocr.json new file mode 100644 index 0000000..0dab369 --- /dev/null +++ b/manifests/subtitle-ocr.json @@ -0,0 +1,15 @@ +{ + "id": "subtitle-ocr", + "name": "Subtitle OCR", + "version": "0.1.0", + "capability": "subtitle-ocr", + "repo_dir": "nodes", + "command": ["python", "-m", "subtitle_ocr"], + "env": {}, + "input_schema": { "frames_manifest": "file" }, + "output_schema": { "srt_uri": "file", "count": "integer" }, + "max_concurrency": 1, + "idle_ttl_seconds": 300, + "health_timeout_seconds": 10, + "keep_warm": false +} diff --git a/manifests/vlm.json b/manifests/vlm.json new file mode 100644 index 0000000..8d9043e --- /dev/null +++ b/manifests/vlm.json @@ -0,0 +1,15 @@ +{ + "id": "vlm-ocr", + "name": "VLM OCR", + "version": "0.1.0", + "capability": "ocr", + "repo_dir": "nodes", + "command": ["python", "-m", "vlm_ocr"], + "env": {}, + "input_schema": { "image_uri": "file" }, + "output_schema": { "text": "string", "text_uri": "file" }, + "max_concurrency": 1, + "idle_ttl_seconds": 300, + "health_timeout_seconds": 10, + "keep_warm": false +} diff --git a/manifests/whisper.json b/manifests/whisper.json new file mode 100755 index 0000000..e26b636 --- /dev/null +++ b/manifests/whisper.json @@ -0,0 +1,21 @@ +{ + "id": "faster-whisper", + "name": "Faster Whisper ASR", + "version": "0.1.0", + "capability": "asr", + "repo_dir": "wov-node-whisper", + "command": ["python", "-m", "wov_node_whisper"], + "env": { + "WOV_NODE_PORT": "0" + }, + "input_schema": { + "audio_uri": "file" + }, + "output_schema": { + "srt_uri": "file" + }, + "max_concurrency": 1, + "idle_ttl_seconds": 300, + "health_timeout_seconds": 30, + "keep_warm": false +} diff --git a/nodes/__init__.py b/nodes/__init__.py new file mode 100644 index 0000000..5eec7c1 --- /dev/null +++ b/nodes/__init__.py @@ -0,0 +1,9 @@ +"""进程内节点实现包。 + +每个模块对应一个节点，提供统一的 invoke(request) -> InvokeResponse 处理器， +由 wov_app.registry 在启动时静态注册，调度器按 node_type 直接调用。 +""" + +from nodes import ass, echo, ffmpeg, llm, whisper + +__all__ = ["ass", "echo", "ffmpeg", "llm", "whisper"] diff --git a/nodes/adaptive_pool.py b/nodes/adaptive_pool.py new file mode 100644 index 0000000..14a2bb8 --- /dev/null +++ b/nodes/adaptive_pool.py @@ -0,0 +1,177 @@ +"""自适应线程池。 + +用于对耗时的独立子任务（如逐帧 VLM OCR）做弹性并发加速： +- 以滚动时间窗口统计已完成任务的平均响应时间； +- 窗口内平均响应 < fast_threshold（默认 0.3s）→ 增加 1 个工作线程（上限 max_workers）； +- 窗口内平均响应 > slow_threshold（默认 1.0s）→ 减少 1 个工作线程（下限 min_workers）。 + +线程数从 min_workers（默认 1）起步，按实测负载自适应：服务端空闲（响应快） +就加大并发，服务端变慢就退避，避免盲目并发压垮上游（如本地 Ollama）。 + +线程安全说明：worker 会在多个线程中并发调用，调用方需保证 worker 无共享 +可变状态（registry 处理器是纯函数，符合要求）；结果按输入顺序返回。 +""" + +from __future__ import annotations + +import queue +import threading +import time +from typing import Callable + +# 停止哨兵：压入队列让空闲工作线程退出（用于缩容）。 +_POISON = object() + + +def decide( + current: int, + avg: float, + min_workers: int, + max_workers: int, + fast_threshold: float, + slow_threshold: float, +) -> int: + """根据窗口平均响应时间返回调整后的目标线程数（纯决策函数）。 + + 响应快（avg < fast_threshold）且未达上限 → 加 1；响应慢 + （avg > slow_threshold）且未达下限 → 减 1；其余情况保持不变。 + """ + if avg < fast_threshold and current < max_workers: + return current + 1 + if avg > slow_threshold and current > min_workers: + return current - 1 + return current + + +class AdaptiveThreadPool: + """自适应线程池：单次 map 按输入顺序返回全部结果。""" + + def __init__( + self, + worker: Callable, + min_workers: int = 1, + max_workers: int = 16, + window_seconds: float = 10.0, + fast_threshold: float = 0.3, + slow_threshold: float = 1.0, + clock=time.monotonic, + on_progress: Callable[[int, int, float], None] | None = None, + ) -> None: + """初始化；clock 可注入便于测试；on_progress(done,total,rate) 每次完成回调。""" + self._worker = worker + self.min_workers = max(1, min_workers) + self.max_workers = max(self.min_workers, max_workers) + self.window_seconds = window_seconds + self.fast_threshold = fast_threshold + self.slow_threshold = slow_threshold + self._clock = clock + self._queue: queue.Queue = queue.Queue() + # 并发目标线程数：决策/缩容的权威依据（线程退出是异步的，不能用 + # len(_threads) 判断，否则并发缩容会重复放哨兵把全部线程毒死）。 + self._target_workers = 0 + self._threads: list[threading.Thread] = [] + self._results: list = [] + self._lock = threading.Lock() + self._stop = threading.Event() + # 滚动窗口起点与已记录的单次耗时。 + self._window_start = clock() + # 观测到的最大并发线程数（供测试与监控）。 + self.max_concurrency = 0 + # 进度回调与计数：on_progress(已完成数, 总数, 平均速度/秒)。 + self._on_progress = on_progress + self._completed = 0 + self._total = 0 + self._started_at = 0.0 + self._window_times: list[float] = [] + + def _run(self) -> None: + """工作线程主循环：取任务 → 执行 → 记录耗时并自适应评估。""" + try: + while not self._stop.is_set(): + try: + seq, item = self._queue.get(timeout=0.2) + except queue.Empty: + continue + if item is _POISON: + # 缩容哨兵：处理完即可退出（队列计数照常）。 + self._queue.task_done() + break + start = self._clock() + try: + result = self._worker(item) + except Exception as exc: + # 单任务异常不拖垮整体：以异常对象作为结果，由调用方判定。 + result = exc + finally: + elapsed = self._clock() - start + self._results.append((seq, result)) + # 进度回调：已完成数、总数与平均处理速度（条/秒）。 + self._completed += 1 + if self._on_progress is not None: + elapsed_total = max(self._clock() - self._started_at, 1e-9) + self._on_progress( + self._completed, self._total, self._completed / elapsed_total + ) + self._tick(elapsed) + self._queue.task_done() + finally: + # 无论何种退出路径都从线程列表移除，保证线程数统计准确。 + with self._lock: + if threading.current_thread() in self._threads: + self._threads.remove(threading.current_thread()) + + def _tick(self, elapsed: float) -> None: + """记录一次完成耗时；窗口满时按平均响应时间调整线程数。""" + self._window_times.append(elapsed) + if self._clock() - self._window_start < self.window_seconds: + return + avg = sum(self._window_times) / len(self._window_times) + self._window_start = self._clock() + self._window_times.clear() + with self._lock: + current = self._target_workers + self._resize( + decide( + current, avg, self.min_workers, self.max_workers, + self.fast_threshold, self.slow_threshold, + ) + ) + + def _resize(self, target: int) -> None: + """调整并发目标：扩容启动新线程；缩容压入等量停止哨兵（幂等）。 + + 以 _target_workers 为当前值：重复调用同一 target 不会重复放哨兵， + 避免并发缩容把所有线程毒死导致队列任务无人处理而挂起。 + """ + with self._lock: + current = self._target_workers + if target > current: + self.max_concurrency = max(self.max_concurrency, target) + for _ in range(target - current): + thread = threading.Thread(target=self._run, daemon=True) + thread.start() + self._threads.append(thread) + self._target_workers = target + elif target < current: + for _ in range(current - target): + self._queue.put((None, _POISON)) + self._target_workers = target + + def map(self, items) -> list: + """按输入顺序返回每个 item 经 worker 处理后的结果列表。""" + self._results = [] + self._completed = 0 + self._total = len(items) + self._started_at = self._clock() + self._stop.clear() + self._resize(self.min_workers) + for seq, item in enumerate(items): + self._queue.put((seq, item)) + self._queue.join() + self._stop.set() + with self._lock: + threads = list(self._threads) + for thread in threads: + thread.join(1.0) + self._results.sort(key=lambda pair: pair[0]) + return [result for _, result in self._results] diff --git a/nodes/ass.py b/nodes/ass.py new file mode 100755 index 0000000..7417e6a --- /dev/null +++ b/nodes/ass.py @@ -0,0 +1,98 @@ +"""SRT 转 ASS 节点。 + +单体版中作为进程内节点模块，由调度器直接调用。解析标准 SRT 后生成 ASS +文件，其中同一句字幕同时输出 LeftEye 与 RightEye 两个样式，分别落在屏幕 +左右两半，形成 VR 双眼叠加效果。 +""" + +from __future__ import annotations + +from pathlib import Path + +from wov_sdk.models import InvokeRequest, InvokeResponse + +def _ass_header(resolution: str) -> str: + """生成 ASS 文件头：脚本信息、左右眼样式和事件格式。""" + width, height = resolution.lower().split("x", 1) + # 左眼样式占左半边，右眼样式占右半边，各留 50px 内边距。 + left_margin = 50 + right_margin = int(width) - 50 + return f"""[Script Info] +Title: VR Dual-Eye Subtitle +ScriptType: v4.00+ +Collisions: Normal +PlayResX: {width} +PlayResY: {height} +WrapStyle: 1 +ScaledBorderAndShadow: yes + +[V4+ Styles] +Format: Name,Fontname,Fontsize,PrimaryColour,SecondaryColour,OutlineColour,BackColour,Bold,Italic,Underline,StrikeOut,ScaleX,ScaleY,Spacing,Angle,BorderStyle,Outline,Shadow,Alignment,MarginL,MarginR,MarginV,Encoding +Style: LeftEye,Arial,50,&H00FFFFFF,&H000000FF,&H00000000,&H80000000,0,0,0,0,50,100,0,0,1,4,0,2,{left_margin},{int(width) // 2},{int(height) // 2 + 60},1 +Style: RightEye,Arial,50,&H00FFFFFF,&H000000FF,&H00000000,&H80000000,0,0,0,0,50,100,0,0,1,4,0,2,{int(width) // 2},{right_margin},{int(height) // 2 + 60},1 + +[Events] +Format: Layer,Start,End,Style,Name,MarginL,MarginR,MarginV,Effect,Text +""" + + +def parse_srt(text: str) -> list[tuple[str, str, str]]: + """把 SRT 文本解析为 (开始时间, 结束时间, 文本) 条目列表。""" + entries: list[tuple[str, str, str]] = [] + lines = text.splitlines() + index = 0 + while index < len(lines): + # 跳过序号前的空行，兼容文件开头有换行的情况。 + if not lines[index].strip(): + index += 1 + continue + # 跳过序号行，直接读取下一行时间轴。 + index += 1 + if index >= len(lines): + break + time_line = lines[index].strip() + index += 1 + # 时间轴必须包含分隔符，否则按畸形输入跳过。 + if " --> " not in time_line: + continue + # SRT 使用逗号毫秒，ASS 使用点号，需要转换。 + start, end = [part.replace(",", ".") for part in time_line.split(" --> ")] + # 连续读取非空行作为字幕文本，多行用 ASS 换行符 \N 连接。 + text_lines: list[str] = [] + while index < len(lines) and lines[index].strip(): + text_lines.append(lines[index]) + index += 1 + entries.append((start, end, r"\N".join(text_lines))) + index += 1 + return entries + + +def write_ass(entries: list[tuple[str, str, str]], output_path: Path, resolution: str) -> None: + """把解析后的条目写入 ASS 文件，每个条目输出左右眼两行 Dialogue。""" + lines = [_ass_header(resolution)] + for start, end, text in entries: + # an2 对齐到屏幕中央偏下，保证双眼字幕视线自然。 + lines.append(f"Dialogue: 0,{start},{end},LeftEye,,0,0,0,,{{\\an2}}{text}") + lines.append(f"Dialogue: 0,{start},{end},RightEye,,0,0,0,,{{\\an2}}{text}") + output_path.write_text("\n".join(lines) + "\n", encoding="utf-8") + + +def invoke(request: InvokeRequest) -> InvokeResponse: + """把 cn_srt_uri 指向的 SRT 转为 dual_eye.ass 产物。""" + srt_uri = request.inputs.get("cn_srt_uri") + if not srt_uri: + return InvokeResponse(status="failed", error="cn_srt_uri is required") + + srt_path = Path(srt_uri) + if not srt_path.is_file(): + return InvokeResponse(status="failed", error="srt file not found") + + entries = parse_srt(srt_path.read_text(encoding="utf-8")) + output_dir = Path(request.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + output_path = output_dir / "dual_eye.ass" + # 分辨率默认 3840x1920，覆盖常见 VR 视频尺寸。 + resolution = str(request.params.get("resolution", "3840x1920")) + write_ass(entries, output_path, resolution) + return InvokeResponse(status="completed", outputs={"ass_uri": str(output_path)}) + diff --git a/nodes/echo.py b/nodes/echo.py new file mode 100755 index 0000000..898cdfd --- /dev/null +++ b/nodes/echo.py @@ -0,0 +1,50 @@ +"""Echo 节点。 + +单体版中作为进程内节点模块存在，由调度器直接调用 invoke 处理器，不再启动 +独立 HTTP 服务。保留该节点用于验证节点协议与注册表链路。 +""" + +from __future__ import annotations + +from pathlib import Path + +from wov_sdk.models import InvokeRequest, InvokeResponse + +def _resolve_input_text(request: InvokeRequest, node_root: Path) -> str: + """按优先级解析输入文本：直接文本 > 文件 URI > 默认字符串。""" + # 优先使用请求中直接携带的 text 字段。 + text = request.inputs.get("text") + if text is not None: + return str(text) + + # 其次读取 file_uri 指向的文件；相对路径以单体根目录为基准。 + file_uri = request.inputs.get("file_uri") + if file_uri: + path = Path(file_uri) + if not path.is_absolute(): + path = node_root / path + return path.read_text(encoding="utf-8") + + # 都没有时返回固定文本，保证节点总有可演示的输出。 + return "echo" + + +def invoke(request: InvokeRequest) -> InvokeResponse: + """处理节点调用：把解析出的文本写入产物并返回 URI。""" + # 单体根目录用于解析相对文件路径（nodes/ 的上一级）。 + node_root = Path(__file__).resolve().parent.parent + output_dir = Path(request.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + text = _resolve_input_text(request, node_root) + # 产物必须落在请求给定的 output_dir，调度器按 run 与节点组织目录。 + output_path = output_dir / "echo.txt" + output_path.write_text(text, encoding="utf-8") + + return InvokeResponse( + status="completed", + outputs={ + "text": text, + "file_uri": str(output_path), + }, + ) diff --git a/nodes/ffmpeg.py b/nodes/ffmpeg.py new file mode 100755 index 0000000..23db1bc --- /dev/null +++ b/nodes/ffmpeg.py @@ -0,0 +1,79 @@ +"""FFmpeg 提音节点。 + +单体版中作为进程内节点模块，由调度器直接调用。ffmpeg 解析顺序为： +FFMPEG_BIN 环境变量 > PATH 中的 ffmpeg > imageio-ffmpeg 内置二进制。 +""" + +from __future__ import annotations + +import os +import shutil +import subprocess +from pathlib import Path + +from wov_sdk.models import InvokeRequest, InvokeResponse + +def _bundled_ffmpeg() -> str | None: + """尝试获取 imageio-ffmpeg 内置的 ffmpeg 可执行文件路径。""" + try: + import imageio_ffmpeg + + return imageio_ffmpeg.get_ffmpeg_exe() + except Exception: # noqa: BLE001 + # 未安装 imageio-ffmpeg 或获取失败时返回 None，交由上层回退。 + return None + + +def _ffmpeg_bin() -> str: + """按优先级解析 ffmpeg 可执行文件，返回最终命令路径。""" + # 显式配置优先，便于部署环境指定自定义二进制。 + configured = os.getenv("FFMPEG_BIN") + if configured: + return configured + # 其次查找 PATH 中的系统 ffmpeg。 + found = shutil.which("ffmpeg") + if found: + return found + # 最后回退到 imageio-ffmpeg 内置二进制；都没有时保留 "ffmpeg" 交给调用失败处理。 + return _bundled_ffmpeg() or "ffmpeg" + + +def invoke(request: InvokeRequest) -> InvokeResponse: + """提取输入视频/音频的标准化音频，产物为 audio.wav。""" + video_uri = request.inputs.get("video_uri") + if not video_uri: + return InvokeResponse(status="failed", error="video_uri is required") + + # 找不到可用 ffmpeg 时直接返回失败，避免子进程报晦涩错误。 + ffmpeg = _ffmpeg_bin() + if shutil.which(ffmpeg) is None and not Path(ffmpeg).is_file(): + return InvokeResponse(status="failed", error="ffmpeg not found") + + output_dir = Path(request.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + output_path = output_dir / "audio.wav" + # ASR 节点默认期望 16kHz 单声道；参数可覆盖。 + channels = str(request.params.get("channels", 1)) + sample_rate = str(request.params.get("sample_rate", 16000)) + command = [ + ffmpeg, + "-y", # 覆盖可能存在的同名输出文件。 + "-i", + str(video_uri), + "-vn", # 丢弃视频流，只保留音频。 + "-ac", + channels, + "-ar", + sample_rate, + str(output_path), + ] + result = subprocess.run(command, capture_output=True, text=True) + if result.returncode != 0: + # 返回 stderr 尾部，保留最有诊断价值的错误信息。 + return InvokeResponse( + status="failed", + error=result.stderr[-2000:] or "ffmpeg failed", + ) + return InvokeResponse(status="completed", outputs={"audio_uri": str(output_path)}) + + diff --git a/nodes/frame_extract.py b/nodes/frame_extract.py new file mode 100644 index 0000000..c2fdfb6 --- /dev/null +++ b/nodes/frame_extract.py @@ -0,0 +1,178 @@ +"""视频抽帧节点。 + +按**帧间隔**从视频抽取帧：解析视频帧率后，帧间隔 step = round(间隔秒 × fps)， +用 ffmpeg 的 select 过滤器按帧号（n mod step == 0）精确取帧——每帧都是真实 +视频帧，帧时间 = 帧号 / fps，避免按时间 seek（-ss 秒）造成的取整漂移。 +再按 crop 相对比例 [x,y,w,h]（0~1）裁切出字幕区域，供 vlm-ocr 等下游节点 +使用。产物为 frames.json 清单：[{"time": 帧时间秒, "image_uri": 帧图片路径}, ...]。 +""" + +from __future__ import annotations + +import json +import re +import subprocess +from pathlib import Path + +from nodes.ffmpeg import _ffmpeg_bin +from wov_app.logging import get_logger +from wov_sdk.models import InvokeRequest, InvokeResponse + +logger = get_logger("frame-extract") + +# 默认裁切：画面底部 18% 区域（常见字幕位置）。 +DEFAULT_CROP = [0.0, 0.82, 1.0, 0.18] + + +def _parse_crop(raw) -> list[float] | None: + """解析并校验 crop 相对比例 [x,y,w,h]（0~1 且不越出画面）。""" + try: + crop = [float(value) for value in raw] + except (TypeError, ValueError): + return None + if len(crop) != 4: + return None + x, y, w, h = crop + if not (0 <= x <= 1 and 0 <= y <= 1 and 0 <= w <= 1 and 0 <= h <= 1): + return None + if x + w > 1.001 or y + h > 1.001: + return None + return crop + + +def _video_size(video: Path, ffmpeg_bin: str) -> tuple[int, int] | None: + """从 ffmpeg -i 输出解析视频分辨率，避免依赖 ffprobe。""" + result = subprocess.run( + [ffmpeg_bin, "-i", str(video)], + capture_output=True, + text=True, + ) + for line in (result.stderr or "").splitlines(): + if "Video:" not in line: + continue + match = re.search(r"(\d{2,5})x(\d{2,5})", line) + if match: + return int(match.group(1)), int(match.group(2)) + return None + + +def _video_duration(video: Path, ffmpeg_bin: str) -> float | None: + """从 ffmpeg -i 输出解析总时长（秒）。""" + result = subprocess.run( + [ffmpeg_bin, "-i", str(video)], + capture_output=True, + text=True, + ) + match = re.search(r"Duration:\s*(\d+):(\d+):(\d+(?:\.\d+)?)", result.stderr or "") + if match: + hours = int(match.group(1)) + minutes = int(match.group(2)) + seconds = float(match.group(3)) + return hours * 3600 + minutes * 60 + seconds + return None + + +def _video_fps(video: Path, ffmpeg_bin: str) -> float | None: + """从 ffmpeg -i 输出解析帧率，支持小数（29.97 fps）与有理数（30000/1001 fps）。""" + result = subprocess.run( + [ffmpeg_bin, "-i", str(video)], + capture_output=True, + text=True, + ) + for line in (result.stderr or "").splitlines(): + if "Video:" not in line: + continue + match = re.search(r"(\d+)/(\d+)\s*fps", line) + if match: + return int(match.group(1)) / int(match.group(2)) + match = re.search(r"(\d+(?:\.\d+)?)\s*fps", line) + if match: + return float(match.group(1)) + return None + + +def _frame_step(fps: float, interval: float) -> int: + """帧间隔换算：每 step 帧取一帧（step=round(间隔秒×fps)，至少为 1）。""" + return max(1, int(round(interval * fps))) + + +def invoke(request: InvokeRequest) -> InvokeResponse: + """按帧间隔抽取并裁切视频帧，输出 frames.json 清单。""" + video_uri = request.inputs.get("video_uri") + if not video_uri: + return InvokeResponse(status="failed", error="video_uri is required") + video = Path(video_uri) + if not video.is_file(): + return InvokeResponse(status="failed", error="video file not found") + + interval = float(request.params.get("interval_seconds", 0.5)) + crop = _parse_crop(request.params.get("crop", DEFAULT_CROP)) + if crop is None: + return InvokeResponse(status="failed", error="invalid crop") + if interval <= 0: + return InvokeResponse(status="failed", error="invalid interval_seconds") + + ffmpeg_bin = _ffmpeg_bin() + size = _video_size(video, ffmpeg_bin) + if size is None: + return InvokeResponse(status="failed", error="cannot read video size") + duration = _video_duration(video, ffmpeg_bin) + if duration is None or duration <= 0: + return InvokeResponse(status="failed", error="cannot read video duration") + fps = _video_fps(video, ffmpeg_bin) + if fps is None or fps <= 0: + return InvokeResponse(status="failed", error="cannot read video fps") + + # 把"每多少秒一帧"换算为"每多少帧取一帧"，按帧号取帧是帧精确的。 + step = _frame_step(fps, interval) + width, height = size + x_px = int(round(crop[0] * width)) + y_px = int(round(crop[1] * height)) + w_px = max(1, int(round(crop[2] * width))) + h_px = max(1, int(round(crop[3] * height))) + + output_dir = Path(request.output_dir) + frames_dir = output_dir / "frames" + frames_dir.mkdir(parents=True, exist_ok=True) + # 单次解码全片：select 按帧号（n mod step == 0）精确取帧，随后对选中帧 + # 裁切字幕区域并压缩到 720p 内（仅缩小）——过大的输入图会触发 glm-ocr + # 的重复循环 bug（M-RoPE delta），源头规避。 + result = subprocess.run( + [ + ffmpeg_bin, + "-y", + "-hide_banner", + "-loglevel", + "error", + "-i", + str(video), + "-vf", + ( + f"select='not(mod(n\\,{step}))'," + f"crop={w_px}:{h_px}:{x_px}:{y_px}," + "scale=1280:720:force_original_aspect_ratio=decrease:force_divisible_by=2" + ), + # 只写出被选中的帧，避免 CFR 补帧产生重复文件。 + "-vsync", + "vfr", + str(frames_dir / "frame_%04d.png"), + ], + capture_output=True, + text=True, + ) + if result.returncode != 0: + return InvokeResponse(status="failed", error=result.stderr[-500:] or "ffmpeg failed") + + # 第 k 个输出文件对应原始帧号 k×step，时间 = 帧号 / fps（帧精确，无累计偏差）。 + files = sorted(frames_dir.glob("frame_*.png")) + manifest = [ + {"time": round((index * step) / fps, 3), "image_uri": str(path)} + for index, path in enumerate(files) + ] + manifest_path = output_dir / "frames.json" + manifest_path.write_text(json.dumps(manifest, ensure_ascii=False), encoding="utf-8") + logger.info("抽帧完成: %d 帧, 帧间隔 %d, 裁切 %dx%d+%d+%d", len(files), step, w_px, h_px, x_px, y_px) + return InvokeResponse( + status="completed", + outputs={"frames_manifest": str(manifest_path), "frame_count": len(files)}, + ) diff --git a/nodes/llm.py b/nodes/llm.py new file mode 100755 index 0000000..a2126f1 --- /dev/null +++ b/nodes/llm.py @@ -0,0 +1,103 @@ +"""LLM 翻译节点。 + +单体版中作为进程内节点模块，由调度器直接调用。接收 SRT，提取纯文本行 +分批调用 LLM，再把译文回填到原 SRT 结构并输出 cn.srt。 +""" + +from __future__ import annotations + +import json +import os +import urllib.error +import urllib.request +from pathlib import Path + +from wov_sdk.models import InvokeRequest, InvokeResponse +# 单次 LLM 请求携带的字幕行数；过大会超出模型上下文，过小则请求次数过多。 +CHUNK_SIZE = 20 + + +def translate_lines(lines: list[str], params: dict) -> list[str]: + """分批调用 LLM 翻译纯文本行，返回顺序一致的译文列表。""" + # 接口地址、Key 和模型均可通过环境变量配置（.env 自动加载）， + # 默认指向 SiliconFlow 兼容接口，模型为 DeepSeek-V4-Flash。 + api_base = os.getenv( + "LLM_API_BASE", + "https://api.siliconflow.cn/v1/chat/completions", + ) + api_key = os.getenv("LLM_API_KEY", "") + # 单次请求超时可配置，长文本翻译场景下需要放宽。 + request_timeout = float(os.getenv("LLM_TIMEOUT_SECONDS", "600")) + model = str(params.get("model") or os.getenv("LLM_MODEL", "Qwen/Qwen3.6-35B-A3B")) + target_language = str(params.get("target_language", "zh-CN")) + # 系统提示词约束模型只输出译文，保证行数和顺序可回填。 + system_prompt = ( + "你是专业字幕翻译。将用户提供的日文字幕翻译为" + f"{target_language}。只返回译文，保持行数和顺序，不要添加解释。" + ) + translated: list[str] = [] + # 按 CHUNK_SIZE 分批发送，避免单次请求超过模型上下文限制。 + for start in range(0, len(lines), CHUNK_SIZE): + chunk = lines[start : start + CHUNK_SIZE] + body = { + "model": model, + "messages": [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": "\n".join(chunk)}, + ], + # 关闭推理模型的思考模式：Qwen3 等模型默认会把推理过程写入 + # reasoning_content，导致 content 为空或截断译文；关闭后直接输出译文。 + "enable_thinking": False, + # 放宽输出上限，避免长批次翻译被模型默认 max_tokens 截断。 + "max_tokens": 8192, + } + headers = {"Content-Type": "application/json"} + # 配置了 Key 时附带 Bearer 鉴权头。 + if api_key: + headers["Authorization"] = f"Bearer {api_key}" + request = urllib.request.Request( + api_base, + data=json.dumps(body).encode("utf-8"), + headers=headers, + method="POST", + ) + with urllib.request.urlopen(request, timeout=request_timeout) as response: + payload = json.loads(response.read().decode("utf-8")) + # 兼容 OpenAI Chat Completions 响应格式，取第一条消息内容。 + content = payload["choices"][0]["message"]["content"] + # 忽略空行，保证译文列表与输入行一一对应。 + translated.extend( + [line.strip() for line in content.splitlines() if line.strip()] + ) + return translated + + +def invoke(request: InvokeRequest) -> InvokeResponse: + """翻译 SRT 文件中的字幕文本，输出 cn.srt。""" + srt_uri = request.inputs.get("srt_uri") + if not srt_uri: + return InvokeResponse(status="failed", error="srt_uri is required") + + srt_path = Path(srt_uri) + if not srt_path.is_file(): + return InvokeResponse(status="failed", error="srt file not found") + + # 标准 SRT 每 4 行一组：序号、时间轴、文本、空行；文本位于第 3 行。 + lines = srt_path.read_text(encoding="utf-8").splitlines() + text_indices = list(range(2, len(lines), 4)) + source_lines = [lines[index] for index in text_indices] + translated_lines = translate_lines(source_lines, request.params) + # 防止模型返回行数偏差：多出的截断，缺少的用空串补齐。 + translated_lines = translated_lines[: len(source_lines)] + translated_lines += [""] * max(0, len(source_lines) - len(translated_lines)) + # 只替换文本行，序号、时间轴和空行保持不变。 + for index, text_index in enumerate(text_indices): + lines[text_index] = translated_lines[index] + + output_dir = Path(request.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + output_path = output_dir / "cn.srt" + # 末尾补一个换行，让文件满足常见文本工具习惯。 + output_path.write_text("\n".join(lines) + "\n", encoding="utf-8") + return InvokeResponse(status="completed", outputs={"cn_srt_uri": str(output_path)}) + diff --git a/nodes/llm_filter.py b/nodes/llm_filter.py new file mode 100644 index 0000000..03151fb --- /dev/null +++ b/nodes/llm_filter.py @@ -0,0 +1,171 @@ +"""LLM 字幕过滤节点。 + +对 OCR 识别出的 SRT 字幕做二次过滤：为避免字幕上下文过长，把每条字幕连同其 +前后各 context_size 条字幕（纯文本，**不含时间戳**）分批提供给 LLM，模型仅判断 +目标字幕是否属于多余、无意义的字符（如重复、残缺、无实际语义的杂项）； +判定无意义则删除该条（连同其时间戳），其余字幕保持原样并重新编号输出。 +""" + +from __future__ import annotations + +import json +import os +import re +import urllib.request +from pathlib import Path +from nodes.adaptive_pool import AdaptiveThreadPool +from wov_app.logging import get_logger +from wov_sdk.models import InvokeRequest, InvokeResponse + +logger = get_logger("llm-filter") + +# 匹配 SRT 条目：时间轴行 + 文本（文本可多行），到下一个序号行或文末结束。 +_SRT_BLOCK_RE = re.compile( + r"(\d{2}:\d{2}:\d{2},\d{3})\s*-->\s*(\d{2}:\d{2}:\d{2},\d{3})\s*\n(.*?)(?=\n\s*\d+\s*\n|\Z)", + re.DOTALL, +) + +# 目标字幕标记：提示词用该标记指明需要判断的那一条字幕。 +TARGET_MARK = "【目标】" + +# 默认上下文窗口：目标字幕前后各取 10 条。 +DEFAULT_CONTEXT_SIZE = 10 + + +def parse_srt(text: str) -> list[dict]: + """解析 SRT 文本为条目列表：[{"start", "end", "text"}]。""" + entries: list[dict] = [] + for match in _SRT_BLOCK_RE.finditer(text): + entries.append( + { + "start": match.group(1), + "end": match.group(2), + "text": match.group(3).strip(), + } + ) + return entries + + +def serialize_srt(entries: list[dict]) -> str: + """把条目列表序列化为标准 SRT 文本（序号重新从 1 编号）。""" + blocks = [ + f"{index}\n{entry['start']} --> {entry['end']}\n{entry['text']}" + for index, entry in enumerate(entries, start=1) + ] + return "\n\n".join(blocks) + "\n" + + +def _judge_target( + entries: list[dict], index: int, context_size: int, params: dict +) -> bool: + """调用 LLM 判断目标字幕是否多余/无意义；返回 True 表示应删除。 + + 请求体只含目标字幕及其前后各 context_size 条字幕的纯文本（无时间戳）， + 目标字幕用 TARGET_MARK 标记；模型只需回答"保留"或"删除"。 + """ + start = max(0, index - context_size) + end = min(len(entries), index + context_size + 1) + target_pos = index - start + lines = [ + f"{TARGET_MARK}{text}" if pos == target_pos else text + for pos, text in enumerate(entry["text"] for entry in entries[start:end]) + ] + + # LLM 兼容接口配置：地址/Key/模型/超时均可通过环境变量覆盖（默认 SiliconFlow）。 + api_base = os.getenv( + "LLM_API_BASE", + "https://api.siliconflow.cn/v1/chat/completions", + ) + api_key = os.getenv("LLM_API_KEY", "") + request_timeout = float(os.getenv("LLM_TIMEOUT_SECONDS", "60")) + model = str(params.get("model") or os.getenv("LLM_MODEL", "Qwen/Qwen3.6-35B-A3B")) + system_prompt = ( + "你是字幕质量过滤器。用户会提供一段字幕序列（纯文本，不含时间戳），" + f"其中用{TARGET_MARK}标记的字幕是需要判断的目标。请判断该字幕是否属于" + "多余、无意义的字符（如重复、残缺、无实际语义的杂项）。" + "只回答两个字：保留 或 删除，不要输出其他内容。" + ) + body = { + "model": model, + "messages": [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": "\n".join(lines)}, + ], + # 关闭推理模式：Qwen3 等模型默认会把思考过程写入 reasoning_content， + # 导致 content 为空或包含多余内容。 + "enable_thinking": False, + # 只输出"保留/删除"，输出上限给得很小即可。 + "max_tokens": 16, + } + headers = {"Content-Type": "application/json"} + # 配置了 Key 时附带 Bearer 鉴权头。 + if api_key: + headers["Authorization"] = f"Bearer {api_key}" + request = urllib.request.Request( + api_base, + data=json.dumps(body).encode("utf-8"), + headers=headers, + method="POST", + ) + with urllib.request.urlopen(request, timeout=request_timeout) as response: + payload = json.loads(response.read().decode("utf-8")) + content = str(payload["choices"][0]["message"]["content"]) + # 模型回答含"删除"即视为该条无意义；其余情况（保留/异常）一律保留，宁多勿删。 + return "删除" in content + + +def invoke(request: InvokeRequest) -> InvokeResponse: + """过滤 SRT 中多余/无意义的字幕，产物为 filtered.srt。""" + srt_uri = request.inputs.get("srt_uri") + if not srt_uri: + return InvokeResponse(status="failed", error="srt_uri is required") + srt_path = Path(srt_uri) + if not srt_path.is_file(): + return InvokeResponse(status="failed", error="srt file not found") + + entries = parse_srt(srt_path.read_text(encoding="utf-8")) + context_size = int(request.params.get("context_size", DEFAULT_CONTEXT_SIZE)) + # 单条判断的工作函数：返回 True 表示该条应删除。 + def judge_one(index) -> bool: + return _judge_target(entries, index, context_size, request.params) + + # 进度日志：打印已判定条数、总数与平均处理速度（条/s）。 + def log_progress(done: int, total: int, rate: float) -> None: + logger.info("字幕判定进度 %d/%d 条 (%.1f 条/s)", done, total, rate) + + # 自适应并发调用 LLM：10s 窗口内平均响应 < 0.3s 则加 1 线程（上限 + # pool_max_workers），> pool_slow_threshold 则减 1 线程（下限 1）， + # 按实测负载弹性伸缩，避免盲目并发压垮 LLM 接口。 + pool = AdaptiveThreadPool( + worker=judge_one, + on_progress=log_progress, + min_workers=int(request.params.get("pool_min_workers", 1)), + max_workers=int(request.params.get("pool_max_workers", 16)), + window_seconds=float(request.params.get("pool_window_seconds", 10.0)), + fast_threshold=float(request.params.get("pool_fast_threshold", 0.3)), + slow_threshold=float(request.params.get("pool_slow_threshold", 1.0)), + ) + verdicts = pool.map(range(len(entries))) + + kept: list[dict] = [] + removed = 0 + for index, (entry, verdict) in enumerate(zip(entries, verdicts)): + # 并行下 LLM 异常被线程池隔离为异常结果：任一条失败即整体失败， + # 避免静默输出未过滤结果。 + if isinstance(verdict, Exception): + return InvokeResponse(status="failed", error=str(verdict)) + if verdict: + removed += 1 + logger.info("删除无意义字幕 %d: %r", index + 1, entry["text"][:40]) + else: + kept.append(entry) + + output_dir = Path(request.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + output_path = output_dir / "filtered.srt" + output_path.write_text(serialize_srt(kept), encoding="utf-8") + logger.info("字幕过滤完成: 保留 %d 条, 删除 %d 条", len(kept), removed) + return InvokeResponse( + status="completed", + outputs={"srt_uri": str(output_path), "kept": len(kept), "removed": removed}, + ) diff --git a/nodes/subtitle_ocr.py b/nodes/subtitle_ocr.py new file mode 100644 index 0000000..9b8c46b --- /dev/null +++ b/nodes/subtitle_ocr.py @@ -0,0 +1,162 @@ +"""字幕 OCR 汇总节点。 + +读取 frame-extract 产出的 frames.json，逐帧调用 vlm-ocr 节点识别字幕文字； +过滤无文字帧的垃圾输出（glm-ocr 在空帧上会输出无用文字），折叠模型重复 +循环输出，合并连续相同的字幕（记录最后可见帧时间），最终组装为带时间轴的 +SRT 基准数据：每条字幕消失时间 = 最后可见帧时间 + 采样间隔（间隔从帧清单 +时间轴推导），与视频烧录时间对齐。 +""" + +from __future__ import annotations + +import json +from pathlib import Path + +from nodes.adaptive_pool import AdaptiveThreadPool +from nodes.whisper import format_timestamp +from wov_app import registry +from wov_app.logging import get_logger +from wov_sdk.models import InvokeRequest, InvokeResponse + +logger = get_logger("subtitle-ocr") + +# 默认垃圾词：无文字帧的模型输出可能反复出现这些词。 +def _sampling_interval(manifest: list[dict], default: float) -> float: + """从帧清单时间轴推导采样间隔（相邻帧时间差的中位数）。 + + 帧时间由 frame-extract 按固定间隔生成，取相邻差的中位数即可还原真实 + 采样间隔，避免结束时间与抽取参数不一致。清单不足两帧时回退默认值。 + """ + diffs = [ + float(manifest[i + 1]["time"]) - float(manifest[i]["time"]) + for i in range(len(manifest) - 1) + if float(manifest[i + 1]["time"]) > float(manifest[i]["time"]) + ] + if not diffs: + return default + diffs.sort() + # 取 3 位小数：与 frame-extract 的 round(秒,3) 时间戳精度一致，避免浮点漂移。 + return round(diffs[len(diffs) // 2], 3) + + +def _assemble_srt( + kept: list[tuple[float, float, str]], + interval_seconds: float, +) -> list[str]: + """把 (起始帧时间, 最后可见帧时间, 文本) 序列组装为 SRT 行列表。 + + 每条字幕的结束时间 = 最后可见帧时间 + 采样间隔：字幕在最后一个被识别 + 到的帧之后的一个采样间隔内消失，与视频烧录时间对齐；两段字幕之间的 + 空白段（无字幕帧）不再被并入前一条字幕。 + """ + lines: list[str] = [] + for index, (start, last_seen, text) in enumerate(kept): + end = last_seen + interval_seconds + lines.extend( + [ + str(index + 1), + f"{format_timestamp(start)} --> {format_timestamp(end)}", + text, + "", + ] + ) + return lines + + +def invoke(request: InvokeRequest) -> InvokeResponse: + """逐帧 OCR 并汇总字幕，产物为 subtitle.srt。""" + manifest_uri = request.inputs.get("frames_manifest") + if not manifest_uri: + return InvokeResponse(status="failed", error="frames_manifest is required") + manifest_path = Path(manifest_uri) + if not manifest_path.is_file(): + return InvokeResponse(status="failed", error="frames manifest not found") + manifest = json.loads(manifest_path.read_text(encoding="utf-8")) + + # 结果长度上限：超过即视为模型异常（重复循环等），该帧直接报错跳过。 + max_result_chars = int(request.params.get("max_result_chars", 200)) + # 采样间隔优先从帧清单时间轴推导（与 frame-extract 实际抽取间隔一致）， + # 参数仅作清单退化时的兜底。 + interval = _sampling_interval( + manifest, float(request.params.get("interval_seconds", 2.0)) + ) + # 透传给 vlm-ocr 的参数（仅传已提供的，避免覆盖其默认值）。 + vlm_params = { + key: request.params.get(key) + for key in ( + "model", "ollama_host", "prompt", "timeout_seconds", "keep_alive", + "temperature", "repeat_penalty", "num_predict", + ) + if request.params.get(key) is not None + } + + # 单帧 OCR：并行池的工作函数，返回该帧识别文本（失败/空/超长均返回空串）。 + def ocr_frame(payload) -> str: + index, item = payload + response = registry.invoke( + "vlm-ocr", + InvokeRequest( + run_id=request.run_id, + node_instance_id="", + inputs={"image_uri": str(item["image_uri"])}, + params=vlm_params, + output_dir=str(Path(request.output_dir) / "ocr_frames" / f"{index:04d}"), + ), + ) + if response.status != "completed": + # 单帧失败不中断整体，跳过该帧继续汇总。 + logger.warning("帧 %d OCR 失败，跳过: %s", index, response.error) + return "" + logger.info("帧 %d/%d OCR 完成: %r", index + 1, len(manifest), response.outputs.get("text")) + text = str(response.outputs.get("text", "")).strip() + if not text: + return "" + # 超长输出视为模型异常（重复循环等），直接报错并跳过该帧。 + if len(text) > max_result_chars: + logger.warning( + "帧 %d OCR 输出超长(%d > %d)，跳过: %r", + index, len(text), max_result_chars, text[:60], + ) + return "" + return text + + # 进度日志：打印已识别帧数、总数与平均处理速度（帧/s）。 + def log_progress(done: int, total: int, rate: float) -> None: + logger.info("OCR 进度 %d/%d 帧 (%.1f 帧/s)", done, total, rate) + + # 自适应并发调用 vlm-ocr：10s 窗口内平均响应 < 0.3s 则加 1 线程（上限 + # pool_max_workers），> pool_slow_threshold 则减 1 线程（下限 1）， + # 按实测负载弹性伸缩，避免盲目并发压垮本地 Ollama。 + pool = AdaptiveThreadPool( + worker=ocr_frame, + on_progress=log_progress, + min_workers=int(request.params.get("pool_min_workers", 1)), + max_workers=int(request.params.get("pool_max_workers", 16)), + window_seconds=float(request.params.get("pool_window_seconds", 10.0)), + fast_threshold=float(request.params.get("pool_fast_threshold", 0.3)), + slow_threshold=float(request.params.get("pool_slow_threshold", 1.0)), + ) + texts = pool.map(list(enumerate(manifest))) + + # kept 元素为 (起始帧时间, 最后可见帧时间, 文本)；按帧顺序合并连续相同字幕。 + kept: list[tuple[float, float, str]] = [] + for index, text in enumerate(texts): + if not text: + continue + time = float(manifest[index]["time"]) + # 连续帧相同字幕合并为一条（字幕停留多帧属正常现象）： + # 仅更新最后可见帧时间，起始时间保持首次出现。 + if kept and kept[-1][2] == text: + kept[-1] = (kept[-1][0], time, text) + continue + kept.append((time, time, text)) + + output_dir = Path(request.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + output_path = output_dir / "subtitle.srt" + output_path.write_text("\n".join(_assemble_srt(kept, interval)), encoding="utf-8") + logger.info("字幕汇总完成: %d 条", len(kept)) + return InvokeResponse( + status="completed", + outputs={"srt_uri": str(output_path), "count": len(kept)}, + ) diff --git a/nodes/vlm.py b/nodes/vlm.py new file mode 100644 index 0000000..f8cd75d --- /dev/null +++ b/nodes/vlm.py @@ -0,0 +1,193 @@ +"""VLM OCR 节点。 + +调用本地 Ollama 服务的多模态模型（默认 glm-ocr:latest）对图片做文字识别 +（OCR），用于从视频帧中提取字幕文字，构建"真实音频 + 正确字幕"的测试数据。 + +与 llm-translate 节点相互独立：本节点只做视觉 OCR，不做翻译，避免把两类 +职责混在一起。调用协议见 Ollama 官方文档：POST /api/chat。 + +请求约定（2026-08 调整，直接请求 API 版本）： +- 使用流式传输（stream=True），逐行接收生成内容，命中终止序列立即停止接收， + 避免模型重复循环时无限拉取输出；响应体整体受 5 秒截止时间约束。 +- 采样 temperature 默认 0.3（可参数覆盖），并携带终止序列列表 + `["\n", "\n答", "答"]`（输出首个换行即停 + 阻止“答：”式重复循环）； + 模型生成遇到任一标记即停止。 +- 每次调用整体超时 5 秒（timeout_seconds 参数 / VLM_TIMEOUT_SECONDS 环境变量）， + 超过即终止，不再继续等待后续流式块。 +""" + +from __future__ import annotations + +import base64 +import json +import os +import re +import time +import urllib.error +import urllib.request +from pathlib import Path + +from wov_sdk.models import InvokeRequest, InvokeResponse + +# 终止序列列表：模型输出一旦出现任一标记（如换行 "\n"、"答"）立即停止生成 +# （Ollama 的 stop 参数，同时作为流式读取时的本地截断标记）。 +# 语义：输出第一个换行即停（单行字幕），"答" 系列阻止"答："式重复循环。 +STOP_SEQUENCE = ["\n","\n答","答"] + + +def _default_host() -> str: + """默认 Ollama 服务地址，可通过环境变量覆盖。""" + return os.getenv("OLLAMA_HOST", "http://192.168.123.70:11434") + + +def _clean_ocr_text(content: str) -> str: + """清洗模型输出：去掉 markdown 围栏与空行，只保留识别到的文字。 + + glm-ocr 的自定义解析器会在识别文本后追加大量 ``` 围栏，必须剔除， + 否则会污染字幕文本。 + """ + lines: list[str] = [] + for line in content.splitlines(): + stripped = line.strip() + # 整行只有反引号（可带语言标记，如 ```markdown）的围栏行丢弃。 + if re.fullmatch(r"`+[A-Za-z0-9]*", stripped): + continue + if stripped: + lines.append(stripped) + return "\n".join(lines) +# 标签提取正则：匹配 ...（DOTALL 让 . 也能匹配换行）。 +_GETTEXT_RE = re.compile(r"(.*?)", re.DOTALL) + + +def _extract_gettext(raw: str) -> str: + """从模型输出中提取 标签内的内容。 + + 提示词要求模型用该标签包裹识别结果；模型未按格式输出（找不到标签） + 时回退返回原始文本，保持旧行为。多个标签只取第一个（防重复循环）。 + """ + match = _GETTEXT_RE.search(raw) + if match is None: + return raw + return match.group(1) + + +def _truncate_at_stop(raw: str) -> str: + """在最先命中的终止序列处截断文本。 + + STOP_SEQUENCE 为终止序列列表（如 ["\n", "\n答", "答"]）：取最早出现的位置 + 截断，返回截断后的文本；未命中任何序列时原样返回。 + """ + positions = [pos for seq in STOP_SEQUENCE if (pos := raw.find(seq)) != -1] + if not positions: + return raw + return raw[: min(positions)] + + +def _consume_stream(response, deadline: float) -> str: + """逐行读取流式响应，直到命中终止序列 / 流结束 / 超过截止时间。 + + response 为 urllib 打开的响应对象，readline() 返回 bytes（每行一个 + Ollama 流式 JSON 块）。返回拼接后的原始文本（未清洗、未截断终止序列）。 + """ + parts: list[str] = [] + while True: + # 每次调用整体 5 秒上限：超过立即终止，不再等待下一个流式块。 + if time.monotonic() >= deadline: + raise TimeoutError("timed out") + line = response.readline() + if not line: + # 流式输出正常结束（模型未发出终止序列，直接收完）。 + break + chunk = json.loads(line.decode("utf-8")) + message = chunk.get("message") + if message is None: + # done 行是流结束标记，可能不带 message；其余缺失视为格式错误。 + if chunk.get("done"): + break + raise KeyError("missing message in stream chunk") + parts.append(str(message.get("content", ""))) + # 命中任一终止序列即停止接收后续内容（模型应已结束生成）。 + if any(seq in "".join(parts) for seq in STOP_SEQUENCE): + break + if chunk.get("done"): + # 模型侧完成（未命中终止序列也自然结束）。 + break + return "".join(parts) +def invoke(request: InvokeRequest) -> InvokeResponse: + """识别 image_uri 指向的图片中的文字，产物为 ocr.txt。 + + 参数：model（默认 glm-ocr:latest）、ollama_host、prompt、 + timeout_seconds（默认 5，每次调用整体上限）；均可通过环境变量 + VLM_MODEL / OLLAMA_HOST / VLM_TIMEOUT_SECONDS 覆盖。 + """ + image_uri = request.inputs.get("image_uri") + if not image_uri: + return InvokeResponse(status="failed", error="image_uri is required") + + image_path = Path(image_uri) + # 文件不存在时提前失败，避免无谓的网络请求。 + if not image_path.is_file(): + return InvokeResponse(status="failed", error="image file not found") + + host = str(request.params.get("ollama_host") or _default_host()) + model = str(request.params.get("model") or os.getenv("VLM_MODEL", "glm-ocr:latest")) + prompt = str( + request.params.get("prompt") + or os.getenv("VLM_PROMPT", "提取图像中的文字，不要描述图片中的内容") + ) + timeout = float( + request.params.get("timeout_seconds") + or os.getenv("VLM_TIMEOUT_SECONDS", "5") + ) + + # 图片按 base64 随请求体发送（Ollama 多模态标准格式）。 + image_b64 = base64.b64encode(image_path.read_bytes()).decode("ascii") + body = { + "model": model, + # 流式传输：逐行接收生成内容，命中终止序列或超时即停止。 + "stream": True, + # keep_alive 让模型在服务端常驻，避免逐帧调用反复加载模型。 + "keep_alive": str(request.params.get("keep_alive", "5m")), + # 采样选项：temperature 默认 0.3（可参数覆盖）；glm-ocr 在大图上有已知 + # 重复循环 bug，repeat_penalty 惩罚重复 token、num_predict 限制输出上限。 + "options": { + "temperature": float(request.params.get("temperature", 0.3)), + "repeat_penalty": float(request.params.get("repeat_penalty", 1)), + "num_predict": int(request.params.get("num_predict", 256)), + }, + # /api/chat 的输入结构：识别指令放系统提示词，用户消息只携带图片 + # （content 为空、images 传 base64，与 glm-ocr 期望结构一致）。 + "messages": [ + {"role": "system", "content": prompt}, + {"role": "user", "content": "", "images": [image_b64]}, + ], + # 模型遇到任一终止序列即停止生成，遏制重复循环；同时作为流式读取截断点。 + "stop": STOP_SEQUENCE, + } + request_url = f"{host.rstrip('/')}/api/chat" + http_request = urllib.request.Request( + request_url, + data=json.dumps(body).encode("utf-8"), + headers={"Content-Type": "application/json"}, + method="POST", + ) + try: + # timeout 同时作用于连接与每次 socket 读取；deadline 保证整体 5 秒上限。 + deadline = time.monotonic() + timeout + with urllib.request.urlopen(http_request, timeout=timeout) as response: + raw = _consume_stream(response, deadline) + # 终止序列可能随最后一个流式块一起返回，在最先命中的序列处截断再清洗。 + raw = _truncate_at_stop(raw) + # 提取提示词约定的 标签内容；模型未按格式输出时回退原始文本。 + text = _clean_ocr_text(_extract_gettext(raw)) + output_dir = Path(request.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + output_path = output_dir / "ocr.txt" + output_path.write_text(text + "\n", encoding="utf-8") + return InvokeResponse( + status="completed", + outputs={"text": text, "text_uri": str(output_path)}, + ) + except (urllib.error.URLError, KeyError, ValueError, OSError) as exc: + # 网络失败、响应格式异常、超时等统一转换为 failed 响应。 + return InvokeResponse(status="failed", error=str(exc)) diff --git a/nodes/whisper.py b/nodes/whisper.py new file mode 100755 index 0000000..fbf3cff --- /dev/null +++ b/nodes/whisper.py @@ -0,0 +1,274 @@ +"""faster-whisper ASR 节点。 + +单体版中作为进程内节点模块，由调度器直接调用。模型权重默认优先从本地 +目录加载，避免从远端下载，仅在本地找不到模型时才回退到远端 large-v3。 +CUDA 动态库通过 ctypes 在进程内预加载，替代分布式版的 LD_LIBRARY_PATH 注入。 +""" + +from __future__ import annotations + +import ctypes +import os +import subprocess +import sysconfig +import time +import wave +from pathlib import Path + +from nodes.ffmpeg import _ffmpeg_bin +from wov_app.logging import get_logger +from wov_sdk.models import InvokeRequest, InvokeResponse + +# 转写进度日志：输出到主进程控制台，长音频分块时可见每块进度。 +logger = get_logger("whisper") + + +def _is_windows() -> bool: + """判断当前是否为 Windows，供测试单独注入覆盖。""" + return os.name == "nt" + + +def _load_cuda_libraries() -> None: + """在进程内预加载 pip 安装的 NVIDIA 动态库。 + + 分布式版通过给节点子进程注入 LD_LIBRARY_PATH（Windows 为 PATH）解决； + 单体版没有进程边界，必须在导入 faster-whisper 前加载 nvidia 轮子自带 + 的 .so/.dll，否则 ctranslate2 初始化 CUDA 时找不到 libcublas.so.12。 + """ + # site-packages 目录，nvidia 各包的动态库位于其下。 + site_packages = Path(sysconfig.get_paths()["purelib"]) + for vendor in ("cublas", "cudnn", "cuda_nvrtc"): + # Windows 轮子把 dll 放在 bin/，Linux 放在 lib/。 + for subdir in ("bin", "lib"): + lib_dir = site_packages / "nvidia" / vendor / subdir + if not lib_dir.is_dir(): + continue + if _is_windows(): + # Windows 通过 DLL 搜索目录注册，等价于进程内 PATH 注入。 + os.add_dll_directory(str(lib_dir)) + else: + for so_file in sorted(lib_dir.glob("*.so*")): + try: + ctypes.CDLL(str(so_file)) + except OSError: + # 个别依赖缺失（如 libcudart）时跳过，交由 ctranslate2 报错。 + continue + +def _local_model_candidates() -> list[Path]: + """返回本地模型候选目录：单体根目录 model/ 优先，其次 nodes/ 同级 model/。 + + 单体根目录 model/ 对应仓库根下的 model/faster-whisper-large-v3， + nodes/ 同级 model/ 允许部署时把权重随代码目录一起携带。 + """ + monolith_root = Path(__file__).resolve().parent.parent + return [ + monolith_root / "model" / "faster-whisper-large-v3", + monolith_root / "nodes" / "model" / "faster-whisper-large-v3", + ] + + +def resolve_model_path( + params: dict, + env: dict | None = None, + candidates: list[Path] | None = None, +) -> str: + """按 参数 > 环境变量 > 本地候选目录 > 远端 large-v3 的顺序解析模型路径。 + + 本地优先是默认行为：只要候选目录存在且包含 model.bin 就使用本地权重， + 避免从 Hugging Face 下载；远端下载仅在全部本地候选缺失时作为兜底。 + 参数/环境变量传入的是裸模型名（不含路径分隔符）时，会先在本地模型 + 目录（model/）下按名解析，方便工作流直接引用下载好的模型。 + candidates 参数供测试注入临时目录，默认使用 _local_model_candidates()。 + """ + env = env if env is not None else os.environ + candidates = candidates if candidates is not None else _local_model_candidates() + explicit = params.get("model_path") or env.get("WHISPER_MODEL_PATH") + if explicit: + explicit_str = str(explicit) + # 裸模型名按 <模型目录>/<名称> 在本地解析，例如 + # "whisper-large-v3-translate-zh-v0.1-lt-ct2"。 + if "/" not in explicit_str and "\\" not in explicit_str: + named = candidates[0].parent / explicit_str + if (named / "model.bin").is_file(): + return str(named) + return explicit_str + for candidate in candidates: + # model.bin 是 CTranslate2 权重的必需文件，存在才认为模型完整。 + if candidate.is_dir() and (candidate / "model.bin").is_file(): + return str(candidate) + return "large-v3" + +def format_timestamp(seconds: float) -> str: + """把秒数格式化为 SRT 时间戳，例如 01:00:00,500。""" + # 先换算成毫秒再逐级拆分为时/分/秒/毫秒，避免浮点误差。 + total_ms = int(seconds * 1000) + hours, remainder = divmod(total_ms, 3600000) + minutes, remainder = divmod(remainder, 60000) + secs, millis = divmod(remainder, 1000) + return f"{hours:02d}:{minutes:02d}:{secs:02d},{millis:03d}" + + +def _split_audio( + audio_path: Path, + output_dir: Path, + chunk_seconds: int, + ffmpeg_bin: str | None, +) -> list[Path]: + """用 ffmpeg 把音频切成 chunk_seconds 秒一块的 wav，返回块路径列表。 + + 分块是应用层工程策略（内存有界、失败粒度小），与模型 30s 窗口无关； + whisper 训练与推理都按 30s 窗口解码，任意块大小都适用。以下情况回退 + 为整段单次转写：chunk_seconds <= 0、找不到 ffmpeg、切块失败、音频本身 + 不足一块（ffmpeg 产出单块）。 + """ + if chunk_seconds <= 0 or not ffmpeg_bin: + return [audio_path] + chunk_dir = output_dir / "chunks" + chunk_dir.mkdir(parents=True, exist_ok=True) + pattern = str(chunk_dir / "chunk_%03d.wav") + # 音频已是 16kHz 单声道 WAV，流拷贝切块即可，无需重编码。 + result = subprocess.run( + [ + ffmpeg_bin, + "-y", + "-i", + str(audio_path), + "-f", + "segment", + "-segment_time", + str(chunk_seconds), + "-c", + "copy", + pattern, + ], + capture_output=True, + text=True, + ) + if result.returncode != 0: + # 切块失败（输入损坏等）回退整段，不让转写流程中断。 + return [audio_path] + chunks = sorted(chunk_dir.glob("chunk_*.wav")) + return chunks or [audio_path] + + +def _wav_duration_seconds(path: Path, fallback: float) -> float: + """从 WAV 头精确读取时长；文件非法/非 WAV 时用 fallback 兜底。 + + 分块偏移必须用每块的实际时长累积，而不是 块序号×块长 的假设值—— + ffmpeg 切出的块实际时长并不精确等于块长（如 60.05s），假设值会随 + 块数累积漂移，造成字幕时间轴逐渐错位。 + """ + try: + with wave.open(str(path), "rb") as wav: + rate = wav.getframerate() + return wav.getnframes() / rate if rate else fallback + except (wave.Error, EOFError, OSError): + # 文件损坏/非 WAV（如 mp4 直传）时用 fallback 兜底。 + return fallback + + +def _append_srt_lines(lines: list[str], segments, offset: float, start_index: int) -> int: + """把一段转写结果按 SRT 格式追加到 lines，时间加上 offset 偏移。 + + 分块合并时每块 offset 为前面所有块的实际时长累积；单次调用 offset=0。 + 每写出一条字幕就打印其编号与完整视频角度的时间范围，便于对照对齐。 + 返回本段新增的条数，用于全局序号递增。 + """ + count = 0 + for segment in segments: + # 完整视频角度的时间 = 模型预测时间 + 累积偏移。 + start_time = segment.start + offset + end_time = segment.end + offset + lines.extend( + [ + str(start_index + count), + f"{format_timestamp(start_time)} --> {format_timestamp(end_time)}", + segment.text.strip(), + "", + ] + ) + logger.info( + "分段 #%d: %s --> %s", + start_index + count, + format_timestamp(start_time), + format_timestamp(end_time), + ) + count += 1 + return count + +def invoke(request: InvokeRequest) -> InvokeResponse: + """转写音频并生成 SRT 字幕，产物为 transcript.srt。""" + audio_uri = request.inputs.get("audio_uri") + if not audio_uri: + return InvokeResponse(status="failed", error="audio_uri is required") + + # 文件不存在时提前失败，避免进入耗时的模型加载流程。 + audio_path = Path(audio_uri) + if not audio_path.is_file(): + return InvokeResponse(status="failed", error="audio file not found") + + try: + # 延迟导入 faster-whisper，保证节点注册与调度等轻量路径不依赖重型依赖； + # 导入前先预加载 NVIDIA 动态库，否则 ctranslate2 找不到 libcublas。 + _load_cuda_libraries() + from faster_whisper import WhisperModel + # 模型路径默认本地优先：参数 > 环境变量 > 工作区本地目录 > 远端兜底。 + model_path = resolve_model_path(request.params) + device = str(request.params.get("device") or os.getenv("WHISPER_DEVICE", "auto")) + # auto 让 faster-whisper 根据硬件自动选择 float16/int8 等计算类型。 + compute_type = str(request.params.get("compute_type") or "auto") + model = WhisperModel( + model_path, + device=device, + compute_type=compute_type, + ) + # language 默认日语；vad_filter 默认开启（用户 2026-08 决定）：过滤静音 + # 段以提速并减少无语音处幻觉；长静音时 VAD 压缩时间轴可能轻微错位， + # 如需极致对齐可在工作流参数中显式关闭。 + # task 默认 transcribe，中文直出模型可传 translate 直接翻译为目标语言。 + # condition_on_previous_text 默认 False：长音频下开启会导致重复/漂移， + # 关闭后每个 30s 窗口独立解码，是 faster-whisper 官方建议的长音频方案。 + output_dir = Path(request.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + # 分块转写：默认每 1 分钟一块（chunk_seconds=60），切块失败自动回退整段。 + chunk_seconds = int(request.params.get("chunk_seconds", 60)) + chunks = _split_audio(audio_path, output_dir, chunk_seconds, _ffmpeg_bin()) + # 逐块转写并合并：offset 用每块实际时长累积（WAV 头精确），SRT 序号连续。 + logger.info("转写开始: %d 个分块", len(chunks)) + lines: list[str] = [] + offset = 0.0 + # SRT 序号从 1 开始，跨块连续递增。 + srt_number = 1 + transcribe_started = time.monotonic() + for chunk_index, chunk in enumerate(chunks, start=1): + chunk_started = time.monotonic() + segments, _info = model.transcribe( + str(chunk), + language=str(request.params.get("language", "ja")), + task=str(request.params.get("task", "transcribe")), + beam_size=int(request.params.get("beam_size", 1)), + vad_filter=bool(request.params.get("vad_filter", True)), + condition_on_previous_text=bool( + request.params.get("condition_on_previous_text", False) + ), + ) + # 进度日志：块序号/总数、单块耗时、实时倍率（块音频时长/墙钟耗时） + # 与转写累计耗时，直观反映数据处理速度。 + chunk_elapsed = time.monotonic() - chunk_started + srt_number += _append_srt_lines(lines, segments, offset, srt_number) + # 偏移按本块实际时长推进，避免假设块长导致的累积漂移。 + offset += _wav_duration_seconds(chunk, chunk_seconds) + logger.info( + "分块 %d/%d 完成 offset=%.2fs 耗时 %.1fs (%.2fx 实时, 累计 %.1fs)", + chunk_index, len(chunks), offset, chunk_elapsed, + chunk_seconds / chunk_elapsed if chunk_elapsed > 0 else 0.0, + time.monotonic() - transcribe_started, + ) + output_path = output_dir / "transcript.srt" + output_path.write_text("\n".join(lines), encoding="utf-8") + return InvokeResponse(status="completed", outputs={"srt_uri": str(output_path)}) + except Exception as exc: # noqa: BLE001 + # 模型加载或转写异常统一转换为 failed 响应，不让调度线程崩溃。 + return InvokeResponse(status="failed", error=str(exc)) + + diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..fc4cff3 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,41 @@ +[project] +name = "vrsub" +version = "0.1.0" +description = "VRSub：为视频生成 VR 双眼字幕的单体应用（API、调度器与全部节点在单进程内运行）" +requires-python = ">=3.11" +dependencies = [ + # Web 框架：FastAPI 负责 API 与静态前端挂载。 + "fastapi", + "uvicorn", + "python-multipart", + # 转写节点：faster-whisper 及其 CUDA 动态库（同进程直接加载，无需路径注入）。 + "faster-whisper>=1.2.1", + "nvidia-cublas-cu12>=12.9.2.10", + "nvidia-cudnn-cu12>=9.24.0.43", + # 提音节点：imageio-ffmpeg 提供内置 ffmpeg 兜底。 + "imageio-ffmpeg>=0.6", + "python-dotenv>=1.2.2", +] + +[dependency-groups] +dev = [ + "pytest", + "pytest-cov", + "httpx", +] + +[build-system] +requires = ["setuptools>=68"] +build-backend = "setuptools.build_meta" + +# wov_sdk / wov_app 位于 src/，nodes 位于仓库根，一并作为可安装包。 +[tool.setuptools.packages.find] +where = ["src", "."] +include = ["wov_sdk*", "wov_app*", "nodes*"] + +# pytest 配置：扫描 tests 目录并强制 100% 行覆盖率；同时把仓库根加入 +# sys.path，保证 nodes 包在未重新安装时也能被导入。 +[tool.pytest.ini_options] +testpaths = ["tests"] +pythonpath = ["."] +addopts = "--cov=src --cov=nodes --cov-fail-under=100 -p no:cacheprovider" diff --git a/src/wov_app/__init__.py b/src/wov_app/__init__.py new file mode 100644 index 0000000..8d23025 --- /dev/null +++ b/src/wov_app/__init__.py @@ -0,0 +1,5 @@ +"""WOV 单体应用包。 + +包含 FastAPI 应用入口、SQLite 数据访问、进程内节点注册表、工作流调度器 +以及管理端/用户端路由。所有节点在同一进程内直接调用，无子进程边界。 +""" diff --git a/src/wov_app/config.py b/src/wov_app/config.py new file mode 100644 index 0000000..80cda51 --- /dev/null +++ b/src/wov_app/config.py @@ -0,0 +1,28 @@ +"""应用配置中心。 + +集中读取环境变量并推导路径常量，避免业务代码散落魔法值。路径统一使用 +pathlib，Windows 与 Linux 开发环境均可用。 +""" + +from __future__ import annotations + +import os +from pathlib import Path + +# WOV 单体根目录：本文件位于 src/wov_app/config.py，向上三级即仓库根。 +WORKSPACE_ROOT = Path(__file__).resolve().parent.parent.parent + +# 数据目录、SQLite 文件与产物存储目录均可通过环境变量覆盖，便于测试隔离。 +DATA_DIR = Path(os.getenv("WOV_DATA_DIR", str(WORKSPACE_ROOT / "data"))) +DB_PATH = Path(os.getenv("WOV_DB_PATH", str(DATA_DIR / "wov.db"))) +STORAGE_DIR = Path(os.getenv("WOV_STORAGE_DIR", str(DATA_DIR / "storage"))) + +# 调度器轮询排队任务的间隔（秒）。 +SCHEDULER_INTERVAL_SECONDS = float(os.getenv("WOV_SCHEDULER_INTERVAL_SECONDS", "1.0")) + +# 孤儿数据清理器配置：定时扫描并清理无对应文件/记录的死数据。 +CLEANUP_ENABLED = os.getenv("WOV_CLEANUP_ENABLED", "1") == "1" +# 清理扫描周期（秒），默认每小时一次。 +CLEANUP_INTERVAL_SECONDS = float(os.getenv("WOV_CLEANUP_INTERVAL_SECONDS", "3600")) +# 宽限期（秒）：任务最后更新距今超过该时长且满足孤儿条件才清理。 +CLEANUP_GRACE_SECONDS = float(os.getenv("WOV_CLEANUP_GRACE_SECONDS", "3600")) diff --git a/src/wov_app/db.py b/src/wov_app/db.py new file mode 100644 index 0000000..8285fa6 --- /dev/null +++ b/src/wov_app/db.py @@ -0,0 +1,398 @@ +"""SQLite 数据访问层。 + +所有持久化逻辑集中在本模块，业务代码只依赖 Database 提供的方法。后续切换 +PostgreSQL 时只需替换本层实现，不修改调度器与路由的业务逻辑。 +""" + +from __future__ import annotations + +import json +import sqlite3 +from contextlib import contextmanager +from pathlib import Path +from typing import Any, Iterator + + +class Database: + """SQLite 数据库封装：负责建表以及工作流/任务/产物的 CRUD。""" + + def __init__(self, path: Path) -> None: + """打开数据库并确保父目录存在、表结构已初始化。""" + self.path = path + self.path.parent.mkdir(parents=True, exist_ok=True) + self._init_schema() + + @contextmanager + def _connect(self) -> Iterator[sqlite3.Connection]: + """提供带事务提交的数据库连接上下文。""" + conn = sqlite3.connect(self.path) + # 按列名读取结果，返回 dict 更直观。 + conn.row_factory = sqlite3.Row + # 开启外键约束，保证子表记录引用有效。 + conn.execute("PRAGMA foreign_keys = ON") + try: + yield conn + conn.commit() + finally: + conn.close() + + def _init_schema(self) -> None: + """创建全部业务表；已存在的表保持不变。""" + with self._connect() as conn: + conn.executescript( + """ + -- 工作流表：只保存概要信息，完整定义存版本表。 + CREATE TABLE IF NOT EXISTS workflows ( + id TEXT PRIMARY KEY, + name TEXT NOT NULL, + description TEXT NOT NULL DEFAULT '', + published INTEGER NOT NULL DEFAULT 0, + latest_version INTEGER NOT NULL DEFAULT 0, + created_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP + ); + + -- 工作流版本表：每个版本保存一份 DAG 定义 JSON。 + CREATE TABLE IF NOT EXISTS workflow_versions ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + workflow_id TEXT NOT NULL, + version INTEGER NOT NULL, + definition_json TEXT NOT NULL, + created_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP, + UNIQUE(workflow_id, version), + FOREIGN KEY(workflow_id) REFERENCES workflows(id) + ); + + -- 工作流运行表：记录任务从排队到完成/失败的状态机。 + CREATE TABLE IF NOT EXISTS workflow_runs ( + id TEXT PRIMARY KEY, + workflow_id TEXT NOT NULL, + workflow_version INTEGER NOT NULL, + status TEXT NOT NULL, + current_node_id TEXT, + progress REAL NOT NULL DEFAULT 0, + error TEXT, + input_uri TEXT, + param_overrides TEXT, + created_at TEXT NOT NULL, + updated_at TEXT NOT NULL, + FOREIGN KEY(workflow_id) REFERENCES workflows(id) + ); + + -- 产物表：记录每个任务各节点的输出 URI，按名称唯一。 + CREATE TABLE IF NOT EXISTS artifacts ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + run_id TEXT NOT NULL, + node_id TEXT NOT NULL, + name TEXT NOT NULL, + uri TEXT NOT NULL, + mime_type TEXT NOT NULL DEFAULT '', + size INTEGER NOT NULL DEFAULT 0, + created_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP, + UNIQUE(run_id, name), + FOREIGN KEY(run_id) REFERENCES workflow_runs(id) + ); + """ + ) + + # 旧库迁移：workflow_runs 补充 param_overrides 列（前端框选覆盖）。 + columns = [ + row["name"] + for row in conn.execute("PRAGMA table_info(workflow_runs)").fetchall() + ] + if "param_overrides" not in columns: + conn.execute("ALTER TABLE workflow_runs ADD COLUMN param_overrides TEXT") + + def upsert_workflow(self, workflow: dict[str, Any]) -> None: + """插入或更新工作流概要信息。""" + with self._connect() as conn: + conn.execute( + """ + INSERT INTO workflows (id, name, description, published, latest_version) + VALUES (?, ?, ?, ?, ?) + ON CONFLICT(id) DO UPDATE SET + name = excluded.name, + description = excluded.description, + published = excluded.published, + latest_version = excluded.latest_version + """, + ( + workflow["id"], + workflow["name"], + workflow.get("description", ""), + int(workflow.get("published", 0)), + int(workflow.get("latest_version", 0)), + ), + ) + + def get_workflow(self, workflow_id: str) -> dict[str, Any] | None: + """按 ID 读取工作流概要。""" + with self._connect() as conn: + row = conn.execute("SELECT * FROM workflows WHERE id = ?", (workflow_id,)).fetchone() + return dict(row) if row else None + + def list_workflows(self) -> list[dict[str, Any]]: + """按创建时间倒序返回全部工作流。""" + with self._connect() as conn: + rows = conn.execute("SELECT * FROM workflows ORDER BY created_at DESC").fetchall() + return [dict(row) for row in rows] + + def delete_workflow(self, workflow_id: str) -> None: + """级联删除工作流相关的产物、任务、版本和概要记录。""" + with self._connect() as conn: + # 外键没有级联删除配置，手动按依赖顺序清理。 + conn.execute("DELETE FROM artifacts WHERE run_id IN (SELECT id FROM workflow_runs WHERE workflow_id = ?)", (workflow_id,)) + conn.execute("DELETE FROM workflow_runs WHERE workflow_id = ?", (workflow_id,)) + conn.execute("DELETE FROM workflow_versions WHERE workflow_id = ?", (workflow_id,)) + conn.execute("DELETE FROM workflows WHERE id = ?", (workflow_id,)) + + def create_workflow_version(self, workflow_id: str, version: int, definition: dict[str, Any]) -> None: + """为工作流新增一个版本，definition 以 JSON 保存。""" + with self._connect() as conn: + conn.execute( + """ + INSERT INTO workflow_versions (workflow_id, version, definition_json) + VALUES (?, ?, ?) + """, + (workflow_id, version, json.dumps(definition, ensure_ascii=False)), + ) + + def get_latest_workflow_version(self, workflow_id: str) -> dict[str, Any] | None: + """返回工作流最新版本，并把 definition_json 反序列化为 definition。""" + with self._connect() as conn: + row = conn.execute( + """ + SELECT * FROM workflow_versions + WHERE workflow_id = ? + ORDER BY version DESC + LIMIT 1 + """, + (workflow_id,), + ).fetchone() + if row is None: + return None + result = dict(row) + # 对外统一暴露 definition 字典，隐藏 JSON 存储细节。 + result["definition"] = json.loads(result.pop("definition_json")) + return result + + def get_workflow_version(self, workflow_id: str, version: int) -> dict[str, Any] | None: + """按版本号读取指定工作流版本。""" + with self._connect() as conn: + row = conn.execute( + """ + SELECT * FROM workflow_versions + WHERE workflow_id = ? AND version = ? + """, + (workflow_id, version), + ).fetchone() + if row is None: + return None + result = dict(row) + result["definition"] = json.loads(result.pop("definition_json")) + return result + + def list_workflow_versions(self, workflow_id: str) -> list[dict[str, Any]]: + """按版本倒序返回工作流全部版本。""" + with self._connect() as conn: + rows = conn.execute( + """ + SELECT * FROM workflow_versions + WHERE workflow_id = ? + ORDER BY version DESC + """, + (workflow_id,), + ).fetchall() + versions = [] + for row in rows: + item = dict(row) + item["definition"] = json.loads(item.pop("definition_json")) + versions.append(item) + return versions + + def create_run(self, run: dict[str, Any]) -> None: + """创建一条排队中的工作流运行记录。""" + with self._connect() as conn: + conn.execute( + """ + INSERT INTO workflow_runs ( + id, workflow_id, workflow_version, status, current_node_id, + progress, error, input_uri, param_overrides, created_at, updated_at + ) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + run["id"], + run["workflow_id"], + run["workflow_version"], + run["status"], + run.get("current_node_id"), + float(run.get("progress", 0)), + run.get("error"), + run.get("input_uri"), + json.dumps(run["param_overrides"], ensure_ascii=False) + if run.get("param_overrides") + else None, + run["created_at"], + run["updated_at"], + ), + ) + + def get_run(self, run_id: str) -> dict[str, Any] | None: + """按 ID 读取任务运行记录。""" + with self._connect() as conn: + row = conn.execute("SELECT * FROM workflow_runs WHERE id = ?", (run_id,)).fetchone() + return self._parse_overrides(row) if row else None + + + @staticmethod + def _parse_overrides(row) -> dict: + """把查询行中的 param_overrides JSON 字符串解析为字典。""" + result = dict(row) + raw = result.get("param_overrides") + result["param_overrides"] = json.loads(raw) if raw else None + return result + + def list_runs(self, limit: int = 20) -> list[dict[str, Any]]: + """按创建时间倒序返回最近的运行记录。""" + with self._connect() as conn: + rows = conn.execute( + "SELECT * FROM workflow_runs ORDER BY created_at DESC LIMIT ?", + (limit,), + ).fetchall() + return [self._parse_overrides(row) for row in rows] + + def list_run_ids(self) -> list[str]: + """返回全部任务 ID，供孤儿数据清理对照磁盘目录使用。""" + with self._connect() as conn: + rows = conn.execute("SELECT id FROM workflow_runs").fetchall() + return [row["id"] for row in rows] + + def update_run(self, run_id: str, **fields: Any) -> None: + """更新运行状态字段，同时刷新 updated_at；未知字段会被忽略。""" + # 只允许更新状态机相关字段，防止任意列被改写。 + allowed = { + "status", + "current_node_id", + "progress", + "error", + } + updates = {key: value for key, value in fields.items() if key in allowed} + if not updates: + return + updates["updated_at"] = fields.get("updated_at") + # 动态拼接 SET 子句，键来自白名单，不存在 SQL 注入风险。 + assignments = ", ".join(f"{key} = ?" for key in updates) + values = list(updates.values()) + [run_id] + with self._connect() as conn: + conn.execute(f"UPDATE workflow_runs SET {assignments} WHERE id = ?", values) + + def reset_run(self, run_id: str, updated_at: str) -> None: + """把失败任务重置为 QUEUED，并清空进度与旧产物，供重试使用。""" + with self._connect() as conn: + # 清空错误和进度，恢复到首次排队时的状态。 + conn.execute( + """ + UPDATE workflow_runs + SET status = 'QUEUED', current_node_id = NULL, progress = 0, + error = NULL, updated_at = ? + WHERE id = ? + """, + (updated_at, run_id), + ) + # 删除旧产物，避免重试后残留过期下载链接。 + conn.execute("DELETE FROM artifacts WHERE run_id = ?", (run_id,)) + + def next_queued_run(self) -> dict[str, Any] | None: + """按创建时间返回最早一条可执行任务（排队或已暂停待续跑）。""" + with self._connect() as conn: + row = conn.execute( + """ + SELECT * FROM workflow_runs + WHERE status IN ('QUEUED', 'PAUSED') + ORDER BY created_at ASC + LIMIT 1 + """ + ).fetchone() + return self._parse_overrides(row) if row else None + + def pause_run(self, run_id: str, updated_at: str) -> None: + """暂停任务：置为 PAUSED；调度器会在节点边界检查并停止推进。""" + with self._connect() as conn: + conn.execute( + "UPDATE workflow_runs SET status = 'PAUSED', updated_at = ? WHERE id = ?", + (updated_at, run_id), + ) + + def resume_run(self, run_id: str, updated_at: str) -> None: + """继续任务：PAUSED 恢复为 QUEUED，等待调度器从断点续跑。""" + with self._connect() as conn: + conn.execute( + "UPDATE workflow_runs SET status = 'QUEUED', updated_at = ? WHERE id = ?", + (updated_at, run_id), + ) + + def restore_run_outputs(self, run_id: str) -> dict[str, dict[str, str]]: + """从已登记的产物重建各节点输出，供暂停后断点续跑使用。 + + 返回 {节点ID: {输出名: URI}}；已完成节点的产物可直接作为后续节点的输入。 + """ + outputs: dict[str, dict[str, str]] = {} + for artifact in self.list_artifacts(run_id): + name = artifact["name"] + # 产物名形如 "节点ID.输出名"（如 a.data_uri），还原为 {输出名: URI}。 + prefix = artifact["node_id"] + "." + if name.startswith(prefix): + name = name[len(prefix):] + outputs.setdefault(artifact["node_id"], {})[name] = artifact["uri"] + return outputs + def create_artifact(self, artifact: dict[str, Any]) -> None: + """记录任务产物；同 run 与 name 冲突时覆盖。""" + with self._connect() as conn: + conn.execute( + """ + INSERT OR REPLACE INTO artifacts ( + run_id, node_id, name, uri, mime_type, size + ) + VALUES (?, ?, ?, ?, ?, ?) + """, + ( + artifact["run_id"], + artifact["node_id"], + artifact["name"], + artifact["uri"], + artifact.get("mime_type", ""), + int(artifact.get("size", 0)), + ), + ) + + def list_artifacts(self, run_id: str) -> list[dict[str, Any]]: + """按创建时间返回任务的全部产物。""" + with self._connect() as conn: + rows = conn.execute( + "SELECT * FROM artifacts WHERE run_id = ? ORDER BY created_at", + (run_id,), + ).fetchall() + return [dict(row) for row in rows] + + def get_artifact(self, run_id: str, name: str) -> dict[str, Any] | None: + """按任务与产物名读取单个产物记录。""" + with self._connect() as conn: + row = conn.execute( + "SELECT * FROM artifacts WHERE run_id = ? AND name = ?", + (run_id, name), + ).fetchone() + return dict(row) if row else None + + def delete_run_artifacts(self, run_id: str) -> None: + """删除任务的全部产物记录。""" + with self._connect() as conn: + conn.execute("DELETE FROM artifacts WHERE run_id = ?", (run_id,)) + + def delete_run(self, run_id: str) -> None: + """删除任务记录本身及其产物记录。 + + 产物表外键引用任务表，必须先删产物再删任务，否则违反外键约束。 + """ + with self._connect() as conn: + conn.execute("DELETE FROM artifacts WHERE run_id = ?", (run_id,)) + conn.execute("DELETE FROM workflow_runs WHERE id = ?", (run_id,)) diff --git a/src/wov_app/logging.py b/src/wov_app/logging.py new file mode 100644 index 0000000..3f72d74 --- /dev/null +++ b/src/wov_app/logging.py @@ -0,0 +1,31 @@ +"""轻量日志配置。 + +单体版所有节点在 API 主进程内运行，这里把节点运行日志直接输出到主进程 +控制台（uvicorn 的 stderr），便于观察各节点的执行过程与耗时。 +""" + +from __future__ import annotations + +import logging + +# 应用日志统一前缀，便于与其他库日志区分。 +_APP_LOGGER_NAME = "vrsub" + + +def _ensure_console_handler(logger: logging.Logger) -> None: + """为日志器附加控制台输出；已配置过则跳过，避免重复打印。""" + if any(isinstance(handler, logging.StreamHandler) for handler in logger.handlers): + return + handler = logging.StreamHandler() + handler.setFormatter(logging.Formatter("%(asctime)s [%(levelname)s] %(message)s")) + logger.addHandler(handler) + logger.setLevel(logging.INFO) + # 不向 uvicorn 根日志传播，防止消息重复输出。 + logger.propagate = False + + +def get_logger(name: str) -> logging.Logger: + """获取并确保输出到主进程控制台的日志器。""" + logger = logging.getLogger(f"{_APP_LOGGER_NAME}.{name}") + _ensure_console_handler(logger) + return logger diff --git a/src/wov_app/main.py b/src/wov_app/main.py new file mode 100644 index 0000000..1887397 --- /dev/null +++ b/src/wov_app/main.py @@ -0,0 +1,82 @@ +"""FastAPI 应用入口。 + +负责组装数据库、进程内节点注册表、调度器与静态前端，并在应用生命周期内 +管理后台线程的启动与清理。单体版所有节点在同一进程内直接调用。 +""" + +from __future__ import annotations + +# 先加载 .env（含 LLM API Key 等本地配置），再导入读取环境变量的 config。 +from dotenv import load_dotenv + +load_dotenv() + +import os # noqa: E402 +from contextlib import asynccontextmanager # noqa: E402 +from pathlib import Path # noqa: E402 + +from fastapi import FastAPI # noqa: E402 +from fastapi.middleware.cors import CORSMiddleware # noqa: E402 +from fastapi.staticfiles import StaticFiles # noqa: E402 + +from wov_app import registry +from wov_app.config import DB_PATH, STORAGE_DIR, WORKSPACE_ROOT +from wov_app.db import Database +from wov_app.maintenance import OrphanCleaner +from wov_app.routers import apps, workflows +from wov_app.scheduler import WorkflowScheduler +from wov_app.seed import seed_default_workflows + +@asynccontextmanager +async def lifespan(app: FastAPI): + """应用生命周期：启动时初始化数据、注册节点和后台服务，退出时回收资源。""" + # 确保数据与存储目录存在，避免首次启动写文件失败。 + db = Database(DB_PATH) + STORAGE_DIR.mkdir(parents=True, exist_ok=True) + # 静态注册全部内置节点到进程内注册表（内存态，不落库）。 + registry.register_all() + # 默认创建演示工作流，可关闭便于测试。 + if os.getenv("WOV_AUTO_SEED", "1") == "1": + seed_default_workflows(db) + + scheduler = WorkflowScheduler(db, STORAGE_DIR) + # 调度器默认开启，处理排队中的任务；测试可关闭后手动执行。 + if os.getenv("WOV_SCHEDULER_ENABLED", "1") == "1": + scheduler.start() + cleaner = OrphanCleaner(db, STORAGE_DIR) + # 孤儿数据清理默认开启，定时清除死数据；测试可关闭。 + if os.getenv("WOV_CLEANUP_ENABLED", "1") == "1": + cleaner.start() + # 共享对象挂到 app.state，路由通过 Depends 延迟获取。 + app.state.db = db + app.state.scheduler = scheduler + app.state.cleaner = cleaner + yield + # 退出时先停调度器与清理器，避免残留后台线程。 + cleaner.stop() + scheduler.stop() + + +app = FastAPI(title="VRSub API（单体版）", version="0.1.0", lifespan=lifespan) + +# MVP 阶段不做鉴权，允许跨域便于本地调试与静态页面访问。 +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_methods=["*"], + allow_headers=["*"], +) + +app.include_router(workflows.router) +app.include_router(apps.router) + + +@app.get("/health") +def health() -> dict: + """进程存活探针，供部署环境与前端检测后端可用性。""" + return {"status": "ok", "service": "wov-api", "mode": "monolith"} + + +# 静态前端目录位于仓库根下的 web，由 FastAPI 直接挂载。 +FRONTEND_DIR = WORKSPACE_ROOT / "web" +app.mount("/", StaticFiles(directory=FRONTEND_DIR, html=True), name="frontend") diff --git a/src/wov_app/maintenance.py b/src/wov_app/maintenance.py new file mode 100644 index 0000000..d4cc00c --- /dev/null +++ b/src/wov_app/maintenance.py @@ -0,0 +1,129 @@ +"""孤儿数据清理器。 + +定时扫描存储目录与数据库，清理不再有意义的死数据： + +1. 磁盘上存在但没有对应任务记录的上传/步骤目录（删除任务中断等残留）。 +2. 状态为 COMPLETED 但产物文件已全部丢失、且超过宽限期的任务记录 + （这类任务在任务页会显示"完成"但下载全部 404，属于孤儿数据）。 + +出于安全考虑，以下数据**不会**被自动清理： + +- FAILED 任务（用户可能重试，且失败任务本就可能没有文件）。 +- 状态非终态（QUEUED/RUNNING）的任务。 +- 最近宽限期内的任务，避免误删刚完成的运行。 + +手动删除任务仍走删除接口，本模块只做保守的孤儿兜底。 +""" + +from __future__ import annotations + +import shutil +import threading +import time +from datetime import datetime, timezone +from pathlib import Path + +from wov_app.config import CLEANUP_GRACE_SECONDS, CLEANUP_INTERVAL_SECONDS +from wov_app.db import Database + + +class OrphanCleaner: + """后台孤儿清理器：周期扫描并清理孤儿数据，只保留明确的死数据。""" + + def __init__( + self, + db: Database, + storage_dir: Path, + interval_seconds: float | None = None, + grace_seconds: float | None = None, + ) -> None: + """保存依赖并初始化轮询线程控制字段。""" + self.db = db + self.storage_dir = storage_dir + self.interval_seconds = interval_seconds or CLEANUP_INTERVAL_SECONDS + self.grace_seconds = grace_seconds or CLEANUP_GRACE_SECONDS + self._thread: threading.Thread | None = None + self._stopping = False + + def start(self) -> None: + """启动清理线程；重复调用无副作用。""" + if self._thread is not None: + return + self._stopping = False + self._thread = threading.Thread( + target=self._loop, + name="wov-orphan-cleaner", + daemon=True, + ) + self._thread.start() + + def stop(self) -> None: + """请求停止并等待清理线程退出。""" + self._stopping = True + if self._thread is not None: + self._thread.join(timeout=5) + self._thread = None + + def _loop(self) -> None: + """周期循环：每隔一个间隔执行一次清理。""" + while not self._stopping: + time.sleep(self.interval_seconds) + self.clean_once() + + def clean_once(self) -> int: + """执行一次孤儿清理，返回清理的数据条目数。""" + run_ids = set(self.db.list_run_ids()) + removed = 0 + # 1) 无对应任务记录的上传/步骤目录视为残留，直接删除。 + removed += self._clean_dangling(self.storage_dir / "uploads", run_ids) + removed += self._clean_dangling(self.storage_dir / "runs", run_ids) + # 2) COMPLETED 且产物文件全失、超过宽限期的任务记录删除。 + for run_id in run_ids: + run = self.db.get_run(run_id) + if run is None: + continue + if run["status"] != "COMPLETED": + continue + if not self._expired(run.get("updated_at")): + continue + if self._has_files(self.storage_dir / "runs" / run_id): + continue + removed += self._remove_run(run_id, run) + return removed + + def _clean_dangling(self, root: Path, run_ids: set[str]) -> int: + """删除 root 下没有对应任务记录的残留子目录，返回删除数。""" + if not root.is_dir(): + return 0 + removed = 0 + for child in root.iterdir(): + if child.is_dir() and child.name not in run_ids: + shutil.rmtree(child, ignore_errors=True) + removed += 1 + return removed + + def _expired(self, updated_at: str | None) -> bool: + """判断任务最后更新时间是否已超过宽限期；无法解析时保守视为未过期。""" + if not updated_at: + return False + try: + updated = datetime.fromisoformat(updated_at) + return (datetime.now(timezone.utc) - updated).total_seconds() > self.grace_seconds + except ValueError: + # 时间格式损坏时保守保留，避免误删。 + return False + + def _has_files(self, run_dir: Path) -> bool: + """判断任务目录下是否仍存在产物文件。""" + if not run_dir.is_dir(): + return False + return any(path.is_file() for path in run_dir.rglob("*")) + + def _remove_run(self, run_id: str, run: dict) -> int: + """删除孤儿任务：数据库记录（含产物）、上传目录与步骤目录。""" + self.db.delete_run(run_id) + input_uri = run.get("input_uri") + if input_uri: + shutil.rmtree(Path(input_uri).parent, ignore_errors=True) + shutil.rmtree(self.storage_dir / "runs" / run_id, ignore_errors=True) + return 1 diff --git a/src/wov_app/registry.py b/src/wov_app/registry.py new file mode 100644 index 0000000..811147a --- /dev/null +++ b/src/wov_app/registry.py @@ -0,0 +1,110 @@ +"""进程内节点注册表。 + +单体版不再启动子进程：节点清单与 invoke 处理器在启动时静态注册到本模块， +调度器通过 invoke(node_id, request) 在同一个进程内直接调用处理器。 +协议数据模型（NodeManifest / InvokeRequest / InvokeResponse）保持不变， +为将来回退分布式保留兼容桥梁。 +""" + +from __future__ import annotations + +import time +from dataclasses import dataclass +from pathlib import Path +from typing import Callable + +from wov_app.logging import get_logger +from wov_sdk.models import InvokeRequest, InvokeResponse, NodeManifest + +# 节点运行日志：输出到主进程控制台。 +logger = get_logger("node") + +# 节点调用处理器签名：接收调用请求，返回调用结果。 +NodeHandler = Callable[[InvokeRequest], InvokeResponse] + + +@dataclass +class NodeEntry: + """注册表条目：节点清单与其进程内处理器。""" + + manifest: NodeManifest + handler: NodeHandler + + +# 进程内注册表：key 为节点 ID（即工作流中的 node_type），value 为注册条目。 +_registry: dict[str, NodeEntry] = {} + + +def _load_manifest(name: str) -> NodeManifest: + """从 manifests/ 目录加载节点清单文件并校验。""" + path = Path(__file__).resolve().parent.parent.parent / "manifests" / f"{name}.json" + return NodeManifest.load(str(path)) + + +def register(manifest: NodeManifest, handler: NodeHandler) -> None: + """注册单个节点；manifest 校验失败时抛出 ValueError。""" + manifest.validate() + _registry[manifest.id] = NodeEntry(manifest=manifest, handler=handler) + + +def register_all() -> None: + """注册全部内置节点，启动时调用一次；重复调用按 ID 覆盖，幂等。""" + from nodes import ass, echo, ffmpeg, frame_extract, llm, llm_filter, subtitle_ocr, vlm, whisper + register(_load_manifest("echo"), echo.invoke) + register(_load_manifest("ffmpeg"), ffmpeg.invoke) + register(_load_manifest("whisper"), whisper.invoke) + register(_load_manifest("llm"), llm.invoke) + register(_load_manifest("vlm"), vlm.invoke) + register(_load_manifest("frame-extract"), frame_extract.invoke) + register(_load_manifest("subtitle-ocr"), subtitle_ocr.invoke) + register(_load_manifest("llm-filter"), llm_filter.invoke) + register(_load_manifest("ass"), ass.invoke) + + +def list_nodes() -> list[NodeManifest]: + """按节点 ID 顺序返回全部已注册节点清单。""" + return [entry.manifest for _, entry in sorted(_registry.items())] + + +def get_node(node_id: str) -> NodeManifest | None: + """按节点 ID 返回清单；未注册时返回 None。""" + entry = _registry.get(node_id) + return entry.manifest if entry else None + + +def invoke(node_id: str, request: InvokeRequest) -> InvokeResponse: + """调用指定节点的进程内处理器；节点未注册时抛出 ValueError。 + + 统一在这里记录节点的开始/结束/耗时/产物日志，所有节点自动获得 + 主进程可见的运行日志，无需在各节点实现内重复埋点。 + """ + entry = _registry.get(node_id) + if entry is None: + raise ValueError(f"node not registered: {node_id}") + logger.info( + "节点 %s 开始 run=%s inputs=%s params=%s", + node_id, + request.run_id, + request.inputs, + request.params, + ) + start = time.perf_counter() + response = entry.handler(request) + elapsed = time.perf_counter() - start + if response.status == "completed": + logger.info( + "节点 %s 完成 run=%s 耗时=%.2fs outputs=%s", + node_id, + request.run_id, + elapsed, + response.outputs, + ) + else: + logger.warning( + "节点 %s 失败 run=%s 耗时=%.2fs error=%s", + node_id, + request.run_id, + elapsed, + response.error, + ) + return response diff --git a/src/wov_app/routers/__init__.py b/src/wov_app/routers/__init__.py new file mode 100644 index 0000000..9b46c03 --- /dev/null +++ b/src/wov_app/routers/__init__.py @@ -0,0 +1,5 @@ +"""WOV 单体 API 路由包。 + +按职责拆分为用户应用与工作流管理两组路由，统一由 wov_app.main 挂载。 +节点注册/实例管理路由已随单体化移除。 +""" diff --git a/src/wov_app/routers/apps.py b/src/wov_app/routers/apps.py new file mode 100755 index 0000000..3b2fb93 --- /dev/null +++ b/src/wov_app/routers/apps.py @@ -0,0 +1,213 @@ +"""用户端应用路由。 + +面向普通用户暴露“应用中心”能力：列出已发布工作流、上传输入创建任务、 +查询进度、重试失败任务以及下载产物。用户只看到输入 -> 进度 -> 结果。 +""" + +from __future__ import annotations + +import json +import shutil +import uuid +from datetime import datetime, timezone +from pathlib import Path + +from fastapi import APIRouter, Depends, File, Form, HTTPException, UploadFile +from fastapi.responses import FileResponse + +from wov_app.db import Database + +router = APIRouter(tags=["apps"]) + + +def _now_iso() -> str: + """返回当前 UTC 时间的 ISO 格式字符串。""" + return datetime.now(timezone.utc).isoformat() + + +def _get_db() -> Database: + """从 FastAPI 应用状态中延迟获取数据库实例。""" + from wov_app.main import app + + return app.state.db + + +@router.get("/api/apps") +def list_apps(db: Database = Depends(_get_db)) -> list[dict]: + """返回全部已发布工作流及其最新版本定义。""" + apps = [] + for workflow in db.list_workflows(): + # 草稿工作流不对用户端可见。 + if not workflow["published"]: + continue + latest = db.get_latest_workflow_version(workflow["id"]) + apps.append( + { + "id": workflow["id"], + "name": workflow["name"], + "description": workflow["description"], + "version": workflow["latest_version"], + "definition": latest["definition"] if latest else None, + } + ) + return apps + + +@router.post("/api/apps/{workflow_id}/runs") +async def create_run( + workflow_id: str, + file: UploadFile = File(...), + params: str = Form(default=""), + db: Database = Depends(_get_db), +) -> dict: + """接收用户上传文件，创建排队中的工作流任务。""" + workflow = db.get_workflow(workflow_id) + # 只允许对已发布且存在版本的工作流发起任务。 + if workflow is None or not workflow["published"]: + raise HTTPException(status_code=404, detail="published workflow not found") + + latest = db.get_latest_workflow_version(workflow_id) + if latest is None: + raise HTTPException(status_code=422, detail="workflow has no version") + + run_id = f"run_{uuid.uuid4().hex[:12]}" + # 使用安全文件名，避免路径穿越。 + filename = Path(file.filename or "upload.bin").name + from wov_app.config import STORAGE_DIR + + # 上传文件按 run 隔离存放，调度器通过 input_uri 引用。 + input_dir = STORAGE_DIR / "uploads" / run_id + input_dir.mkdir(parents=True, exist_ok=True) + input_uri = input_dir / filename + content = await file.read() + input_uri.write_bytes(content) + + # 可选参数覆盖（如前端框选的 crop）：{节点ID: {参数: 值}}，随任务持久化。 + param_overrides = None + if params.strip(): + try: + parsed = json.loads(params) + except json.JSONDecodeError as exc: + raise HTTPException(status_code=422, detail="params must be valid JSON") from exc + if not isinstance(parsed, dict): + raise HTTPException(status_code=422, detail="params must be a JSON object") + param_overrides = parsed + + now = _now_iso() + db.create_run( + { + "id": run_id, + "workflow_id": workflow_id, + "workflow_version": latest["version"], + "status": "QUEUED", + "progress": 0, + "input_uri": str(input_uri), + "param_overrides": param_overrides, + "created_at": now, + "updated_at": now, + } + ) + return { + "id": run_id, + "status": "QUEUED", + "progress": 0, + "artifacts": [], + } + + +@router.get("/api/runs") +def list_runs(db: Database = Depends(_get_db)) -> list[dict]: + """返回最近的运行记录，供任务管理页展示。""" + return db.list_runs() + + +@router.get("/api/runs/{run_id}") +def get_run(run_id: str, db: Database = Depends(_get_db)) -> dict: + """返回任务详情，并附带当前产物列表。""" + run = db.get_run(run_id) + if run is None: + raise HTTPException(status_code=404, detail="run not found") + run["artifacts"] = db.list_artifacts(run_id) + return run + + +@router.post("/api/runs/{run_id}/retry") +def retry_run(run_id: str, db: Database = Depends(_get_db)) -> dict: + """重置失败任务为排队状态，清空旧产物后重新执行。""" + run = db.get_run(run_id) + if run is None: + raise HTTPException(status_code=404, detail="run not found") + if run["status"] != "FAILED": + raise HTTPException(status_code=422, detail="only failed runs can be retried") + # reset_run 会清空进度、错误和旧产物，确保从头开始。 + db.reset_run(run_id, _now_iso()) + return {"id": run_id, "status": "QUEUED"} + + +@router.post("/api/runs/{run_id}/pause") +def pause_run(run_id: str, db: Database = Depends(_get_db)) -> dict: + """暂停任务：排队中或运行中的任务可暂停，运行中的任务在节点边界停下。""" + run = db.get_run(run_id) + if run is None: + raise HTTPException(status_code=404, detail="run not found") + if run["status"] not in ("QUEUED", "RUNNING"): + raise HTTPException(status_code=422, detail="only queued or running runs can be paused") + db.pause_run(run_id, _now_iso()) + return {"id": run_id, "status": "PAUSED"} + + +@router.post("/api/runs/{run_id}/resume") +def resume_run(run_id: str, db: Database = Depends(_get_db)) -> dict: + """继续任务：暂停的任务恢复排队，由调度器从断点继续执行。""" + run = db.get_run(run_id) + if run is None: + raise HTTPException(status_code=404, detail="run not found") + if run["status"] != "PAUSED": + raise HTTPException(status_code=422, detail="only paused runs can be resumed") + db.resume_run(run_id, _now_iso()) + return {"id": run_id, "status": "QUEUED"} + +@router.delete("/api/runs/{run_id}") +def delete_run(run_id: str, db: Database = Depends(_get_db)) -> dict: + """删除任务：清理产物记录、上传文件与步骤产物目录。""" + run = db.get_run(run_id) + if run is None: + raise HTTPException(status_code=404, detail="run not found") + from wov_app.config import STORAGE_DIR + + # 先删数据库记录（含产物表），再清理磁盘上的上传与中间产物。 + db.delete_run(run_id) + input_uri = run.get("input_uri") + if input_uri: + # 上传文件位于 /uploads//，整目录一并删除。 + shutil.rmtree(Path(input_uri).parent, ignore_errors=True) + # 步骤产物位于 /runs//，整目录一并删除。 + shutil.rmtree(STORAGE_DIR / "runs" / run_id, ignore_errors=True) + return {"deleted": run_id} + +@router.get("/api/runs/{run_id}/artifacts") +def list_run_artifacts(run_id: str, db: Database = Depends(_get_db)) -> list[dict]: + """返回任务全部产物记录。""" + if db.get_run(run_id) is None: + raise HTTPException(status_code=404, detail="run not found") + return db.list_artifacts(run_id) + + +@router.get("/api/runs/{run_id}/artifacts/{artifact_name}") +def download_artifact( + run_id: str, + artifact_name: str, + db: Database = Depends(_get_db), +) -> FileResponse: + """按任务与产物名下载文件，文件缺失时返回 404。""" + artifact = db.get_artifact(run_id, artifact_name) + if artifact is None: + raise HTTPException(status_code=404, detail="artifact not found") + path = Path(artifact["uri"]) + if not path.is_file(): + raise HTTPException(status_code=404, detail="artifact file missing") + return FileResponse( + path, + media_type=artifact["mime_type"], + filename=path.name, + ) diff --git a/src/wov_app/routers/workflows.py b/src/wov_app/routers/workflows.py new file mode 100755 index 0000000..2c2160f --- /dev/null +++ b/src/wov_app/routers/workflows.py @@ -0,0 +1,140 @@ +"""工作流管理路由。 + +提供工作流的创建、查询、校验、发布和删除能力。工作流以版本化 DAG 数据保存， +不写死在业务代码中。 +""" + +from __future__ import annotations + +import re +import uuid + +from fastapi import APIRouter, Depends, HTTPException + +from wov_app.db import Database +from wov_app.schemas import WorkflowCreate +from wov_sdk.models import WorkflowDefinition + +router = APIRouter(prefix="/api/admin/workflows", tags=["workflows"]) + + +def _get_db() -> Database: + """从应用状态延迟获取数据库实例。""" + from wov_app.main import app + + return app.state.db + + +def _slugify(value: str) -> str: + """把工作流名称转换为小写连字符 ID；无有效字符时生成随机 ID。""" + slug = re.sub(r"[^a-z0-9]+", "-", value.lower()).strip("-") + return slug or uuid.uuid4().hex[:8] + + +def _validate_definition(raw: dict) -> WorkflowDefinition: + """解析并校验 DAG 定义，非法时转换为 422 HTTP 异常。""" + try: + definition = WorkflowDefinition.from_dict(raw) + definition.validate() + return definition + except (KeyError, TypeError, ValueError) as exc: + raise HTTPException(status_code=422, detail=str(exc)) from exc + + +@router.get("") +def list_workflows(db: Database = Depends(_get_db)) -> list[dict]: + """返回全部工作流概要。""" + return db.list_workflows() + + +@router.post("") +def create_workflow( + payload: WorkflowCreate, + db: Database = Depends(_get_db), +) -> dict: + """创建新工作流或为已有工作流追加一个版本。""" + definition = _validate_definition(payload.definition) + # 未显式指定 ID 时由名称生成；已有工作流则版本号递增。 + workflow_id = payload.id or _slugify(payload.name) + existing = db.get_workflow(workflow_id) + version = (existing or {}).get("latest_version", 0) + 1 + # 每次创建都保存新版本，发布操作只切换 published 标记。 + db.upsert_workflow( + { + "id": workflow_id, + "name": payload.name, + "description": payload.description, + "published": 0, + "latest_version": version, + } + ) + db.create_workflow_version(workflow_id, version, definition.to_dict()) + return { + "id": workflow_id, + "name": payload.name, + "description": payload.description, + "published": False, + "latest_version": version, + } + + +@router.get("/{workflow_id}") +def get_workflow(workflow_id: str, db: Database = Depends(_get_db)) -> dict: + """返回工作流概要及最新版本定义。""" + workflow = db.get_workflow(workflow_id) + if workflow is None: + raise HTTPException(status_code=404, detail="workflow not found") + latest = db.get_latest_workflow_version(workflow_id) + workflow["latest_version_data"] = latest + return workflow + + +@router.delete("/{workflow_id}") +def delete_workflow(workflow_id: str, db: Database = Depends(_get_db)) -> dict: + """删除工作流及其版本、任务和产物记录。""" + if db.get_workflow(workflow_id) is None: + raise HTTPException(status_code=404, detail="workflow not found") + db.delete_workflow(workflow_id) + return {"deleted": workflow_id} + + +@router.post("/{workflow_id}/validate") +def validate_workflow( + workflow_id: str, + definition: dict, + db: Database = Depends(_get_db), +) -> dict: + """在不保存的情况下校验一份 DAG 定义。""" + if db.get_workflow(workflow_id) is None: + raise HTTPException(status_code=404, detail="workflow not found") + parsed = _validate_definition(definition) + return {"valid": True, "node_ids": [node.id for node in parsed.nodes]} + + +@router.post("/{workflow_id}/publish") +def publish_workflow(workflow_id: str, db: Database = Depends(_get_db)) -> dict: + """把工作流标记为已发布，使其出现在用户应用中心。""" + workflow = db.get_workflow(workflow_id) + if workflow is None: + raise HTTPException(status_code=404, detail="workflow not found") + if workflow["latest_version"] == 0: + raise HTTPException(status_code=422, detail="workflow has no version") + # 发布只是状态切换，不修改已保存的版本数据。 + db.upsert_workflow( + { + "id": workflow_id, + "name": workflow["name"], + "description": workflow["description"], + "published": 1, + "latest_version": workflow["latest_version"], + } + ) + return {"published": workflow_id} + + +@router.get("/{workflow_id}/versions") +def list_versions(workflow_id: str, db: Database = Depends(_get_db)) -> list[dict]: + """返回工作流全部版本定义。""" + if db.get_workflow(workflow_id) is None: + raise HTTPException(status_code=404, detail="workflow not found") + return db.list_workflow_versions(workflow_id) diff --git a/src/wov_app/scheduler.py b/src/wov_app/scheduler.py new file mode 100755 index 0000000..c4c41e1 --- /dev/null +++ b/src/wov_app/scheduler.py @@ -0,0 +1,308 @@ +"""工作流调度器。 + +轮询 SQLite 中的排队任务，按工作流 DAG 的拓扑顺序依次调用进程内节点 +处理器，并把节点产物登记为任务产物。单体版使用单线程顺序执行，节点在 +同一进程内直接调用，不再经过子进程与 HTTP 协议。 +""" + +from __future__ import annotations + +import threading +import time +from datetime import datetime, timezone +from pathlib import Path + +from wov_sdk.models import InvokeRequest, WorkflowDefinition + +from wov_app import registry +from wov_app.config import SCHEDULER_INTERVAL_SECONDS +from wov_app.db import Database +from wov_app.logging import get_logger + +# 调度器运行日志：节点进度、暂停/续跑等状态变化。 +logger = get_logger("scheduler") + +def _now_iso() -> str: + """返回当前 UTC 时间的 ISO 格式字符串。""" + return datetime.now(timezone.utc).isoformat() + + +def topological_sort(definition: WorkflowDefinition) -> list[str]: + """对工作流 DAG 做拓扑排序，返回可执行的节点 ID 顺序。""" + nodes = {node.id: node for node in definition.nodes} + # 统计每个节点的入度，并记录依赖关系。 + indegree = {node_id: 0 for node_id in nodes} + dependents: dict[str, list[str]] = {node_id: [] for node_id in nodes} + + for edge in definition.edges: + # 边引用了不存在的节点时直接报错。 + if edge.from_node not in nodes or edge.to_node not in nodes: + raise ValueError(f"unknown edge: {edge.from_node} -> {edge.to_node}") + indegree[edge.to_node] += 1 + dependents[edge.from_node].append(edge.to_node) + + # Kahn 算法：从入度为 0 的节点开始逐层取出。 + queue = [node_id for node_id, degree in indegree.items() if degree == 0] + ordered: list[str] = [] + while queue: + current = queue.pop(0) + ordered.append(current) + for dependent in dependents[current]: + indegree[dependent] -= 1 + if indegree[dependent] == 0: + queue.append(dependent) + + # 排序结果数量不足说明存在环，无法确定执行顺序。 + if len(ordered) != len(nodes): + raise ValueError("workflow contains a cycle") + return ordered + + +class WorkflowScheduler: + """后台任务调度器：单线程轮询并执行排队中的工作流运行。""" + + def __init__( + self, + db: Database, + storage_dir: Path, + interval_seconds: float | None = None, + ) -> None: + """保存依赖并初始化轮询线程控制字段。""" + self.db = db + self.storage_dir = storage_dir + self.interval_seconds = interval_seconds or SCHEDULER_INTERVAL_SECONDS + self._thread: threading.Thread | None = None + self._stopping = False + + def start(self) -> None: + """启动调度线程；重复调用无副作用。""" + if self._thread is not None: + return + self._stopping = False + self._thread = threading.Thread( + target=self._loop, + name="wov-workflow-scheduler", + daemon=True, + ) + self._thread.start() + + def stop(self) -> None: + """请求停止并等待轮询线程退出。""" + self._stopping = True + if self._thread is not None: + self._thread.join(timeout=5) + self._thread = None + + def _loop(self) -> None: + """轮询循环：有排队任务就立即执行，否则休眠一个间隔。""" + while not self._stopping: + run = self.db.next_queued_run() + if run is not None: + self.execute_run(run["id"]) + else: + time.sleep(self.interval_seconds) + + def _resolve_ref( + self, + ref: str, + run_input_uri: str | None, + outputs_by_node: dict[str, dict[str, str]], + ) -> str | None: + """解析输入引用：input.xxx 取任务入口，node.key 取前序节点产物。""" + # 入口引用以 input. 为前缀。 + if ref.startswith("input."): + return run_input_uri + # 其余引用必须形如 "节点ID.输出名"。 + node_id, separator, key = ref.partition(".") + if not separator: + return None + return outputs_by_node.get(node_id, {}).get(key) + + def execute_run(self, run_id: str) -> None: + """执行单个任务：加载 DAG、按拓扑顺序调用节点并登记产物。""" + run = self.db.get_run(run_id) + # 任务不存在或不在可执行状态（排队/暂停）时直接返回，避免重复执行。 + if run is None or run["status"] not in ("QUEUED", "PAUSED"): + return + + # 工作流或版本记录丢失时把任务标记为失败。 + workflow = self.db.get_workflow(run["workflow_id"]) + if workflow is None: + self.db.update_run(run_id, status="FAILED", error="workflow not found", updated_at=_now_iso()) + return + + version = self.db.get_workflow_version(run["workflow_id"], run["workflow_version"]) + if version is None: + self.db.update_run(run_id, status="FAILED", error="workflow version not found", updated_at=_now_iso()) + return + + # 解析并校验 DAG，随后计算拓扑执行顺序。 + definition = WorkflowDefinition.from_dict(version["definition"]) + definition.validate() + ordered = topological_sort(definition) + # 从已登记产物重建已完成节点的输出，支持暂停后断点续跑。 + outputs_by_node = self.db.restore_run_outputs(run_id) + run_started = time.monotonic() + self.db.update_run(run_id, status="RUNNING", progress=0, updated_at=_now_iso()) + try: + for index, node_id in enumerate(ordered): + # 暂停检查：用户暂停后调度器在节点边界停下，保持 PAUSED 等待续跑。 + current = self.db.get_run(run_id) + if current is None or current["status"] == "PAUSED": + logger.info("任务 %s 已暂停，停止在节点 %s 之前", run_id, node_id) + return + # 断点续跑：跳过已产出结果的节点（其产物已作为输入可用）。 + if node_id in outputs_by_node: + continue + # 当前节点进度 = 已完成节点数 / 总节点数。 + node_spec = next(item for item in definition.nodes if item.id == node_id) + self.db.update_run( + run_id, + current_node_id=node_id, + progress=index / len(ordered), + updated_at=_now_iso(), + ) + # 解析节点声明的每个输入引用，缺任一输入即失败。 + invoke_inputs: dict[str, str] = {} + for input_name, ref in node_spec.inputs.items(): + value = self._resolve_ref(ref, run.get("input_uri"), outputs_by_node) + if value is None: + raise ValueError(f"missing input {input_name} for node {node_id}") + invoke_inputs[input_name] = value + + # 前端框选的 crop 等参数覆盖：按节点 ID 合并进节点参数。 + node_params = dict(node_spec.params) + overrides = run.get("param_overrides") or {} + node_params.update(overrides.get(node_id, {})) + + # 每个任务的每个节点使用独立产物目录，避免并发冲突。 + node_started = time.monotonic() + output_dir = ( + self.storage_dir + / "runs" + / run_id + / "steps" + / node_id + ) + response = registry.invoke( + node_spec.node_type, + InvokeRequest( + run_id=run_id, + node_instance_id="", + inputs=invoke_inputs, + params=node_params, + output_dir=str(output_dir), + ), + ) + # 节点返回非 completed 即视为步骤失败。 + if response.status != "completed": + raise RuntimeError(response.error or f"node {node_id} failed") + + # 记录节点输出，供后续节点引用和最终产物映射使用。 + outputs_by_node[node_id] = { + str(key): str(value) for key, value in response.outputs.items() + } + for key, uri in outputs_by_node[node_id].items(): + # 产物名带节点前缀，例如 asr.srt_uri，避免跨节点重名。 + artifact = { + "run_id": run_id, + "node_id": node_id, + "name": f"{node_id}.{key}", + "uri": uri, + "mime_type": self._mime_type(uri), + "size": self._file_size(uri), + } + self.db.create_artifact(artifact) + # 进度日志：节点序号/总数、耗时与任务累计运行时间（数据速度可观测）。 + logger.info( + "任务 %s 进度 %d/%d 节点: %s 耗时 %.1fs, 运行累计 %.1fs", + run_id, index + 1, len(ordered), node_id, + time.monotonic() - node_started, + time.monotonic() - run_started, + ) + # 处理 final_outputs，为用户端提供简洁的下载别名。 + for alias, ref in definition.final_outputs.items(): + resolved = self._resolve_ref(ref, run.get("input_uri"), outputs_by_node) + if resolved is not None: + # 最终产物按 上传文件名.标识.时间戳 重命名，区分语言与版本。 + resolved = self._final_artifact_uri(resolved, run, definition, alias, ref) + self.db.create_artifact( + { + "run_id": run_id, + "node_id": ref.partition(".")[0], + "name": alias, + "uri": resolved, + "mime_type": self._mime_type(resolved), + "size": self._file_size(resolved), + } + ) + + # 全部节点成功后标记完成；期间被暂停则保持 PAUSED，等待续跑补做收尾。 + if self.db.get_run(run_id)["status"] == "PAUSED": + logger.info("任务 %s 节点全部完成但已暂停，保持 PAUSED", run_id) + return + self.db.update_run( + run_id, + status="COMPLETED", + current_node_id=None, + progress=1.0, + updated_at=_now_iso(), + ) + except Exception as exc: # noqa: BLE001 + # 任一步骤异常都结束任务并记录错误，等待用户重试。 + self.db.update_run( + run_id, + status="FAILED", + error=str(exc), + updated_at=_now_iso(), + ) + + def _final_artifact_uri( + self, + resolved: str, + run: dict, + definition: WorkflowDefinition, + alias: str, + ref: str, + ) -> str: + """把最终产物重命名为 上传文件名.标识.时间戳 并返回新 URI。 + + 标识优先取产出节点的 target_language 参数（如 zh-CN），否则回退为 + 产物别名；时间戳取当前时刻，用于区分同一上传文件的多次运行版本。 + 重命名在原地进行（同目录），不复制文件。 + """ + source = Path(resolved) + # 续跑等场景下源文件可能已被上次收尾重命名过：不再重命名，原样返回。 + if not source.is_file(): + return resolved + # 基础名来自上传文件名；无上传文件时退回通用名称 subtitle。 + base = Path(run["input_uri"]).stem if run.get("input_uri") else "subtitle" + # 通过最终输出引用定位产出节点，取其语言参数作为标识。 + node_id = ref.partition(".")[0] + node = next((item for item in definition.nodes if item.id == node_id), None) + tag = (node.params.get("target_language") if node else None) or alias + timestamp = datetime.now(timezone.utc).strftime("%Y%m%d%H%M%S") + new_path = source.with_name(f"{base}.{tag}.{timestamp}{source.suffix}") + source.rename(new_path) + return str(new_path) + + @staticmethod + def _mime_type(uri: str) -> str: + """按扩展名推断产物 MIME 类型，未知类型使用通用二进制类型。""" + path = Path(uri) + suffix = path.suffix.lower() + return { + ".srt": "application/x-subrip", + ".ass": "text/plain", + ".wav": "audio/wav", + ".mp4": "video/mp4", + ".txt": "text/plain", + }.get(suffix, "application/octet-stream") + + @staticmethod + def _file_size(uri: str) -> int: + """读取产物文件大小；文件缺失时按 0 处理。""" + try: + return Path(uri).stat().st_size + except OSError: + return 0 diff --git a/src/wov_app/schemas.py b/src/wov_app/schemas.py new file mode 100644 index 0000000..b15bc86 --- /dev/null +++ b/src/wov_app/schemas.py @@ -0,0 +1,22 @@ +"""FastAPI 请求/响应 schema。 + +使用 Pydantic 模型校验管理 API 的 JSON 请求体。节点管理功能已移除，仅保留 +工作流相关请求模型。 +""" + +from __future__ import annotations + +from typing import Any + +from pydantic import BaseModel, Field + + +class WorkflowCreate(BaseModel): + """创建工作流或新增版本的请求体。""" + + # 缺省时由后端根据名称生成 slug ID。 + id: str | None = None + name: str = Field(min_length=1) + description: str = "" + # DAG 原始字典，后端会解析并校验为 WorkflowDefinition。 + definition: dict[str, Any] diff --git a/src/wov_app/seed.py b/src/wov_app/seed.py new file mode 100644 index 0000000..38072e1 --- /dev/null +++ b/src/wov_app/seed.py @@ -0,0 +1,48 @@ +"""种子数据模块。 + +从 workflows/*.json 数据文件加载默认工作流并写入数据库（幂等）。 +工作流定义是**数据**（JSON）：切换模型、调整链路只改数据文件，不涉及代码， +满足"工作流即数据"与"切换模型不改代码"的设计约束。 +""" + +from __future__ import annotations + +import json +from pathlib import Path + +from wov_sdk.models import WorkflowDefinition + +from wov_app.config import WORKSPACE_ROOT +from wov_app.db import Database + + +def seed_default_workflows(db: Database, workflows_dir: Path | None = None) -> int: + """从数据目录加载默认工作流，已存在的工作流跳过，返回创建数量。 + + 每个 JSON 文件结构： + {"id", "name", "description", "version", "definition"}， + definition 为 WorkflowDefinition 的标准 DAG 字典。 + """ + workflows_dir = workflows_dir or (WORKSPACE_ROOT / "workflows") + created = 0 + for path in sorted(workflows_dir.glob("*.json")): + payload = json.loads(path.read_text(encoding="utf-8")) + workflow_id = str(payload["id"]) + # 已存在的工作流不覆盖，避免启动时反复改写用户数据。 + if db.get_workflow(workflow_id) is not None: + continue + definition = WorkflowDefinition.from_dict(payload["definition"]) + definition.validate() + version = int(payload.get("version", 1)) + db.upsert_workflow( + { + "id": workflow_id, + "name": str(payload["name"]), + "description": str(payload.get("description", "")), + "published": 1, + "latest_version": version, + } + ) + db.create_workflow_version(workflow_id, version, definition.to_dict()) + created += 1 + return created diff --git a/src/wov_sdk/__init__.py b/src/wov_sdk/__init__.py new file mode 100644 index 0000000..3911f0c --- /dev/null +++ b/src/wov_sdk/__init__.py @@ -0,0 +1,28 @@ +"""WOV SDK 公共导出入口。 + +单体版中调度器、节点与 API 统一从 wov_sdk 导入协议模型，而无需关心具体 +模块路径。协议数据模型保持与分布式版一致，为将来回退保留兼容桥梁。 +""" + +from wov_sdk.models import ( + HealthResponse, + InvokeRequest, + InvokeResponse, + NodeManifest, + ProgressEvent, + WorkflowDefinition, + WorkflowEdge, + WorkflowNode, +) + +# 对外稳定的公共 API 清单；新增模型时必须同步追加到这里。 +__all__ = [ + "HealthResponse", + "InvokeRequest", + "InvokeResponse", + "NodeManifest", + "ProgressEvent", + "WorkflowDefinition", + "WorkflowEdge", + "WorkflowNode", +] diff --git a/src/wov_sdk/models.py b/src/wov_sdk/models.py new file mode 100755 index 0000000..eab17b1 --- /dev/null +++ b/src/wov_sdk/models.py @@ -0,0 +1,321 @@ +"""WOV 节点协议核心数据模型。 + +本模块定义节点 Manifest、调用请求/响应、健康检查、进度事件以及工作流 DAG 的 +通用数据结构。单体版中调度器、节点与 API 共用这些类，字段语义必须长期保持 +稳定，新增能力时只能向后兼容地扩展字段。 +""" + +from __future__ import annotations + +import json +from dataclasses import dataclass, field +from typing import Any + + +def _require_non_empty(value: str, name: str) -> None: + """校验必填字符串字段，空字符串或纯空白字符串都会被拒绝。""" + if not value or not value.strip(): + raise ValueError(f"{name} must not be empty") + + +@dataclass +class NodeManifest: + """节点注册清单：描述节点能力、输入输出与资源参数。 + + 该清单由 manifests/ 目录下的 JSON 文件提供，单体启动时注册到进程内 + 节点注册表，调度器据此把 node_type 解析到对应的 invoke 处理器。 + """ + + # 节点稳定唯一 ID，例如 faster-whisper；注册后不可随意更改。 + id: str + # 展示名称，仅用于管理后台等界面。 + name: str + # 节点版本号，与节点仓库 Git tag 保持一致。 + version: str + # 能力标识，工作流通过 node_type 引用能力，而不是直接绑定具体仓库。 + capability: str + # 启动命令；单体版不再启动子进程，字段保留仅为协议兼容。 + command: list[str] + # 节点代码所在目录；单体版保留仅为协议兼容。 + repo_dir: str = "." + # 节点环境变量；单体版保留仅为协议兼容。 + env: dict[str, str] = field(default_factory=dict) + # 输入字段 schema，当前主要用于文档展示，后续可用于运行时校验。 + input_schema: dict[str, Any] = field(default_factory=dict) + # 输出字段 schema，用于描述节点产物的名称与类型。 + output_schema: dict[str, Any] = field(default_factory=dict) + # 单进程内无并发槽位概念，字段保留仅为协议兼容。 + max_concurrency: int = 1 + # 单体内模型常驻不回收，字段保留仅为协议兼容。 + idle_ttl_seconds: int = 300 + # 无进程启动等待，字段保留仅为协议兼容。 + health_timeout_seconds: int = 10 + # 单体内模型常驻不回收，字段保留仅为协议兼容。 + keep_warm: bool = False + + def validate(self) -> None: + """校验 manifest 必填字段与数值边界，非法配置抛出 ValueError。""" + _require_non_empty(self.id, "id") + _require_non_empty(self.name, "name") + _require_non_empty(self.version, "version") + _require_non_empty(self.capability, "capability") + _require_non_empty(self.repo_dir, "repo_dir") + # 命令不能为空，否则节点进程无法启动。 + if not self.command: + raise ValueError("command must not be empty") + if self.max_concurrency < 1: + raise ValueError("max_concurrency must be >= 1") + if self.idle_ttl_seconds < 0: + raise ValueError("idle_ttl_seconds must be >= 0") + if self.health_timeout_seconds < 1: + raise ValueError("health_timeout_seconds must be >= 1") + + def to_dict(self) -> dict[str, Any]: + """转换为可 JSON 序列化的普通字典。""" + return { + "id": self.id, + "name": self.name, + "version": self.version, + "capability": self.capability, + "command": self.command, + "repo_dir": self.repo_dir, + "env": self.env, + "input_schema": self.input_schema, + "output_schema": self.output_schema, + "max_concurrency": self.max_concurrency, + "idle_ttl_seconds": self.idle_ttl_seconds, + "health_timeout_seconds": self.health_timeout_seconds, + "keep_warm": self.keep_warm, + } + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> "NodeManifest": + """从注册 API 或 JSON 文件解析出的字典恢复 manifest。""" + return cls( + id=str(data["id"]), + name=str(data["name"]), + version=str(data["version"]), + capability=str(data["capability"]), + # command 可能缺省，解析时提供空列表兜底。 + command=[str(item) for item in data.get("command", [])], + repo_dir=str(data.get("repo_dir", ".")), + env={str(k): str(v) for k, v in data.get("env", {}).items()}, + input_schema=dict(data.get("input_schema", {})), + output_schema=dict(data.get("output_schema", {})), + # 数值字段缺省时使用与 dataclass 一致的默认值。 + max_concurrency=int(data.get("max_concurrency", 1)), + idle_ttl_seconds=int(data.get("idle_ttl_seconds", 300)), + health_timeout_seconds=int(data.get("health_timeout_seconds", 10)), + keep_warm=bool(data.get("keep_warm", False)), + ) + + @classmethod + def load(cls, path: str) -> "NodeManifest": + """从磁盘上的 node.manifest.json 加载并校验 manifest。""" + with open(path, "r", encoding="utf-8") as f: + manifest = cls.from_dict(json.load(f)) + manifest.validate() + return manifest + + +@dataclass +class InvokeRequest: + """节点调用请求：由调度器或管理后台发送给节点 HTTP 服务。""" + + # 工作流运行 ID，用于追踪一次完整执行。 + run_id: str + # 实际承载本次调用的节点实例 ID，由 NodeManager 回填。 + node_instance_id: str + # 输入产物映射，key 为输入名，value 为产物 URI 或直接文本。 + inputs: dict[str, Any] = field(default_factory=dict) + # 节点运行参数，例如采样率、语言、模型路径等。 + params: dict[str, Any] = field(default_factory=dict) + # 节点产物输出目录。 + output_dir: str = "." + + def to_dict(self) -> dict[str, Any]: + """转换为可 JSON 序列化的普通字典。""" + return { + "run_id": self.run_id, + "node_instance_id": self.node_instance_id, + "inputs": self.inputs, + "params": self.params, + "output_dir": self.output_dir, + } + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> "InvokeRequest": + """从 HTTP 请求 JSON 解析调用请求。""" + return cls( + run_id=str(data["run_id"]), + node_instance_id=str(data["node_instance_id"]), + inputs=dict(data.get("inputs", {})), + params=dict(data.get("params", {})), + output_dir=str(data.get("output_dir", ".")), + ) + + +@dataclass +class InvokeResponse: + """节点调用响应：completed 表示成功，failed 表示执行失败。""" + + # 执行状态，固定为 completed / failed。 + status: str + # 输出产物映射，key 为输出名，value 为产物 URI。 + outputs: dict[str, Any] = field(default_factory=dict) + # 失败原因，仅在 failed 时有意义。 + error: str | None = None + + def to_dict(self) -> dict[str, Any]: + """转换为可 JSON 序列化的普通字典。""" + return { + "status": self.status, + "outputs": self.outputs, + "error": self.error, + } + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> "InvokeResponse": + """从 HTTP 响应 JSON 解析调用结果。""" + return cls( + # 缺省按失败处理，避免未知状态被误判为成功。 + status=str(data.get("status", "failed")), + outputs=dict(data.get("outputs", {})), + error=data.get("error"), + ) + + +@dataclass +class HealthResponse: + """节点健康检查响应：节点进程就绪后返回 ok 与自身标识。""" + + status: str + node_id: str + version: str + + def to_dict(self) -> dict[str, Any]: + """转换为可 JSON 序列化的普通字典。""" + return { + "status": self.status, + "node_id": self.node_id, + "version": self.version, + } + + +@dataclass +class ProgressEvent: + """进度事件：预留用于节点向调度器上报执行进度。""" + + run_id: str + node_id: str + progress: float + message: str | None = None + + def to_dict(self) -> dict[str, Any]: + """转换为可 JSON 序列化的普通字典。""" + return { + "run_id": self.run_id, + "node_id": self.node_id, + "progress": self.progress, + "message": self.message, + } + + +@dataclass +class WorkflowNode: + """工作流中的一个节点：声明节点类型、参数和输入引用。""" + + # 节点在 DAG 内的唯一 ID，例如 extract、asr。 + id: str + # 引用的节点能力，例如 ffmpeg-extract、faster-whisper。 + node_type: str + # 传递给节点 invoke 的 params。 + params: dict[str, Any] = field(default_factory=dict) + # 输入引用，value 形如 "前序节点ID.输出名" 或 "input.入口字段"。 + inputs: dict[str, str] = field(default_factory=dict) + + def to_dict(self) -> dict[str, Any]: + """转换为可 JSON 序列化的普通字典。""" + return { + "id": self.id, + "node_type": self.node_type, + "params": self.params, + "inputs": self.inputs, + } + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> "WorkflowNode": + """从工作流定义 JSON 解析节点。""" + return cls( + id=str(data["id"]), + node_type=str(data["node_type"]), + params=dict(data.get("params", {})), + inputs={str(k): str(v) for k, v in data.get("inputs", {}).items()}, + ) + + +@dataclass +class WorkflowEdge: + """工作流有向边：from_node 的输出流向 to_node 的输入。""" + + from_node: str + to_node: str + + def to_dict(self) -> dict[str, Any]: + """转换为 JSON 时使用 from/to 短字段名。""" + return {"from": self.from_node, "to": self.to_node} + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> "WorkflowEdge": + """从工作流定义 JSON 解析边。""" + return cls(from_node=str(data["from"]), to_node=str(data["to"])) + + +@dataclass +class WorkflowDefinition: + """工作流 DAG 定义：包含节点列表、依赖边和输入输出映射。""" + + name: str + version: int + nodes: list[WorkflowNode] = field(default_factory=list) + edges: list[WorkflowEdge] = field(default_factory=list) + # 用户上传入口与入口字段名的映射，例如 {"video_uri": "file"}。 + entry_inputs: dict[str, Any] = field(default_factory=dict) + # 最终对外暴露的产物别名映射，例如 {"ass": "ass.ass_uri"}。 + final_outputs: dict[str, Any] = field(default_factory=dict) + + def validate(self) -> None: + """校验 DAG 基本约束：名称、版本、节点 ID 唯一、边引用有效。""" + _require_non_empty(self.name, "name") + if self.version < 1: + raise ValueError("version must be >= 1") + # 节点 ID 集合用于检查重复和边引用。 + node_ids = {node.id for node in self.nodes} + if len(node_ids) != len(self.nodes): + raise ValueError("workflow node ids must be unique") + for edge in self.edges: + if edge.from_node not in node_ids or edge.to_node not in node_ids: + raise ValueError(f"edge references unknown node: {edge}") + + def to_dict(self) -> dict[str, Any]: + """转换为可 JSON 序列化的普通字典。""" + return { + "name": self.name, + "version": self.version, + "nodes": [node.to_dict() for node in self.nodes], + "edges": [edge.to_dict() for edge in self.edges], + "entry_inputs": self.entry_inputs, + "final_outputs": self.final_outputs, + } + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> "WorkflowDefinition": + """从工作流定义 JSON 解析 DAG。""" + return cls( + name=str(data["name"]), + version=int(data.get("version", 1)), + nodes=[WorkflowNode.from_dict(item) for item in data.get("nodes", [])], + edges=[WorkflowEdge.from_dict(item) for item in data.get("edges", [])], + entry_inputs=dict(data.get("entry_inputs", {})), + final_outputs=dict(data.get("final_outputs", {})), + ) diff --git a/testdata/ocr_notext.png b/testdata/ocr_notext.png new file mode 100644 index 0000000000000000000000000000000000000000..0086edb04f605ec3c2a54389afe84c707dea184f GIT binary patch literal 1532 zcmeAS@N?(olHy`uVBq!ia0y~yU~XYxVASDY0*Z+Id36j(aTa()76WNUF!onHa*ct3 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z#_(E;mC*f!dl}c$ZWZ)E7bgj>>8HpQ`4=2!>7iOdPnk;j9Ao7BmFmwKewk z_OQlC^-rIxI1UFAW}o)o2l0qTWiRt)BcSO6#8d=2DWuf3okVoBF~=_6XMT%XTrL5cF3WD?RrC7zh`7qe26VGa|gr{n4I$viMq zGEar6yeK6U>P&ikd6`d=MxW^+<I ze96Cx_h+6}Di;N8>hlr}kNZ$Cm*qJm-uXIX$I_7d29NlEq0GMi-s`$RiYkI+@Kha8 za>}Jhn6&2c%zY=t{UR=vEBZrwoaD)3UCIevzpo%Rz3Vd#)Z_mKqJA>2;UncT00000 LNkvXXu0mjfX>zNT literal 0 HcmV?d00001 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..10653bd --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,43 @@ +"""pytest 全局配置。 + +在测试进程启动时创建独立临时目录，并通过环境变量把应用的数据目录、数据库、 +存储和后台服务全部指向测试环境，避免污染本地开发数据；同时隔离进程内节点 +注册表，防止测试之间互相泄漏注册条目。 +""" + +import atexit +import os +import shutil +import tempfile +from pathlib import Path + +import pytest + +# 每个测试进程使用独立临时根目录，保证测试之间互不干扰。 +TEST_ROOT = Path(tempfile.mkdtemp(prefix="vrsub-test-")) +os.environ["WOV_DATA_DIR"] = str(TEST_ROOT / "data") +os.environ["WOV_DB_PATH"] = str(TEST_ROOT / "data" / "wov.db") +os.environ["WOV_STORAGE_DIR"] = str(TEST_ROOT / "storage") +# 默认关闭自动种子和后台调度，测试显式控制执行时机。 +os.environ["WOV_AUTO_SEED"] = "0" +os.environ["WOV_SCHEDULER_ENABLED"] = "0" +os.environ["WOV_CLEANUP_ENABLED"] = "0" + + +def _cleanup() -> None: + """进程退出时清理临时测试目录。""" + shutil.rmtree(TEST_ROOT, ignore_errors=True) + + +atexit.register(_cleanup) + + +@pytest.fixture(autouse=True) +def _isolate_registry(): + """快照并恢复进程内节点注册表，避免测试之间互相污染。""" + from wov_app import registry + + snapshot = dict(registry._registry) + yield + registry._registry.clear() + registry._registry.update(snapshot) diff --git a/tests/test_adaptive_pool.py b/tests/test_adaptive_pool.py new file mode 100644 index 0000000..a13af83 --- /dev/null +++ b/tests/test_adaptive_pool.py @@ -0,0 +1,148 @@ +"""自适应线程池测试。 + +覆盖决策函数（增/减/保持/边界）、map 顺序返回、worker 异常隔离， +以及"10s 窗口内平均响应 < 0.3s 加线程 / > 1.0s 减线程"的弹性行为 +（通过注入假时钟做确定性验证）。 +""" + +import time + +from nodes.adaptive_pool import AdaptiveThreadPool, decide + + +class FakeClock: + """可手动拨动的假时钟，用于确定性验证弹性窗口逻辑。""" + + def __init__(self, now: float = 0.0) -> None: + self.now = now + + def __call__(self) -> float: + return self.now + + def advance(self, seconds: float) -> None: + self.now += seconds + + +def test_decide_increase_when_fast() -> None: + """平均响应低于 fast_threshold 且未达上限：线程数 +1。""" + assert decide(1, 0.1, 1, 16, 0.3, 1.0) == 2 + + +def test_decide_decrease_when_slow() -> None: + """平均响应高于 slow_threshold 且高于下限：线程数 -1。""" + assert decide(3, 2.0, 1, 16, 0.3, 1.0) == 2 + + +def test_decide_keep_when_mid() -> None: + """平均响应介于两阈值之间：保持不变。""" + assert decide(2, 0.5, 1, 16, 0.3, 1.0) == 2 + + +def test_decide_bounds() -> None: + """已达上限不再增、已达下限不再减。""" + assert decide(16, 0.1, 1, 16, 0.3, 1.0) == 16 + assert decide(1, 2.0, 1, 16, 0.3, 1.0) == 1 + + +def test_pool_map_ordered_results() -> None: + """map 按输入顺序返回结果，worker 简单映射。""" + pool = AdaptiveThreadPool(worker=lambda item: item * 2) + assert pool.map([1, 2, 3, 4]) == [2, 4, 6, 8] + + +def test_pool_on_progress_callback() -> None: + """进度回调：每次完成触发一次，携带已完成数/总数/速度。""" + progress: list[tuple[int, int, float]] = [] + pool = AdaptiveThreadPool( + worker=lambda item: item, + on_progress=lambda done, total, rate: progress.append((done, total, rate)), + ) + pool.map([10, 20, 30]) + assert [item[0] for item in progress] == [1, 2, 3] # 已完成数递增。 + assert all(item[1] == 3 for item in progress) # 总数固定。 + assert all(item[2] > 0 for item in progress) # 速度为正值。 + +def test_pool_map_empty() -> None: + """空输入：不启动任务，直接返回空列表。""" + pool = AdaptiveThreadPool(worker=lambda item: item) + assert pool.map([]) == [] + + +def test_pool_worker_exception_isolated() -> None: + """worker 抛异常时以异常对象作为结果，不拖垮整体。""" + def boom(item): + raise RuntimeError("boom") + + pool = AdaptiveThreadPool(worker=boom) + results = pool.map([1, 2]) + assert len(results) == 2 + assert all(isinstance(result, RuntimeError) for result in results) + + +def test_pool_grows_when_fast() -> None: + """10s 窗口内平均响应 < 0.3s：线程数从 1 增至 2（弹性扩容）。""" + clock = FakeClock() + pool = AdaptiveThreadPool( + worker=lambda item: item, + min_workers=1, max_workers=16, + window_seconds=10.0, fast_threshold=0.3, + clock=clock, + ) + # 拨快时钟越过窗口：首个任务完成即触发评估 → 平均响应≈0 < 0.3 → +1 线程。 + clock.advance(11) + pool.map(list(range(4))) + assert pool.max_concurrency == 2 + + +def test_pool_shrink_when_slow() -> None: + """窗口平均响应 > 1.0s：线程数从 2 减至 1（弹性退避）。""" + clock = FakeClock() + pool = AdaptiveThreadPool( + worker=lambda item: item, + min_workers=1, max_workers=16, + window_seconds=10.0, fast_threshold=0.3, slow_threshold=1.0, + clock=clock, + ) + pool._resize(2) # 先扩到 2 个线程。 + clock.advance(11) + pool._tick(2.0) # 窗口内平均 2.0 > 1.0 → 缩回 1。 + deadline = time.monotonic() + 2 + while len(pool._threads) > 1 and time.monotonic() < deadline: + time.sleep(0.01) + assert len(pool._threads) == 1 + pool._stop.set() + + +def test_resize_shrink_idempotent() -> None: + """回归：重复缩容到同一目标不会重复放哨兵（曾因并发缩容毒死全部线程而死锁）。""" + pool = AdaptiveThreadPool(worker=lambda item: item, min_workers=1, max_workers=8) + pool._resize(3) + assert pool._target_workers == 3 + pool._resize(2) + pool._resize(2) # 目标已是 2：幂等，不再放哨兵。 + assert pool._target_workers == 2 + # 只有 1 个线程被哨兵退出，最终存活 2 个。 + deadline = time.monotonic() + 2 + while len(pool._threads) > 2 and time.monotonic() < deadline: + time.sleep(0.01) + assert len(pool._threads) == 2 + pool._stop.set() + + +def test_pool_survives_mixed_grow_shrink() -> None: + """回归：扩容+缩容混合场景 map 必须完成且保序（修复前会死锁挂起）。""" + clock = FakeClock() + state = {"count": 0} + + def worker(item): + state["count"] += 1 + clock.advance(0.06 if state["count"] <= 20 else 0.6) + return item + + pool = AdaptiveThreadPool( + worker=worker, min_workers=1, max_workers=4, + window_seconds=0.5, fast_threshold=0.2, slow_threshold=0.4, + clock=clock, + ) + out = pool.map(list(range(60))) + assert out == list(range(60)) diff --git a/tests/test_api.py b/tests/test_api.py new file mode 100644 index 0000000..e29c247 --- /dev/null +++ b/tests/test_api.py @@ -0,0 +1,34 @@ +"""应用级 API 冒烟测试。 + +使用 FastAPI TestClient 验证健康检查、静态页面与 OpenAPI 文档可访问。 +节点注册/实例管理 API 已随单体化移除，不再有对应路由。 +""" + +from fastapi.testclient import TestClient + +from wov_app.main import app + + +def test_health_and_static() -> None: + """验证静态首页、OpenAPI 文档与健康探针均可访问。""" + with TestClient(app) as client: + root = client.get("/", follow_redirects=False) + assert root.status_code == 200 + assert "VRSub 字幕生成" in root.text + + docs = client.get("/docs") + assert docs.status_code == 200 + + response = client.get("/health") + assert response.status_code == 200 + assert response.json()["service"] == "wov-api" + assert response.json()["mode"] == "monolith" + + +def test_node_admin_routes_removed() -> None: + """验证节点注册与实例管理路由在单体版中已移除（404/405）。""" + with TestClient(app) as client: + # GET 落到静态文件挂载后返回 404；POST 对静态挂载返回 405。 + assert client.post("/api/admin/nodes", json={}).status_code == 405 + assert client.get("/api/admin/nodes").status_code == 404 + assert client.get("/api/admin/node-instances").status_code == 404 diff --git a/tests/test_apps_api.py b/tests/test_apps_api.py new file mode 100644 index 0000000..4567f3e --- /dev/null +++ b/tests/test_apps_api.py @@ -0,0 +1,313 @@ +"""用户应用 API 测试。 + +覆盖已发布应用的上传建任务、进度查询、产物下载、失败重试以及 +未发布/无版本工作流的拒绝逻辑。节点为内置注册，无需再手动注册。 +""" + +from pathlib import Path + +from fastapi.testclient import TestClient + +from wov_app.main import app + + +def _create_published_echo_workflow(client) -> str: + """创建一个已发布的单节点 Echo 工作流（echo 为内置节点）。""" + definition = { + "name": "echo-flow", + "version": 1, + "nodes": [ + { + "id": "step", + "node_type": "echo", + "inputs": {"file_uri": "input.video_uri"}, + } + ], + "edges": [], + "entry_inputs": {"video_uri": "file"}, + "final_outputs": {"result": "step.file_uri"}, + } + client.post( + "/api/admin/workflows", + json={ + "id": "echo-app", + "name": "Echo App", + "description": "upload a file", + "definition": definition, + }, + ) + client.post("/api/admin/workflows/echo-app/publish") + return "echo-app" + + +def test_upload_run_progress_and_download() -> None: + """验证上传文件建任务、手动执行、查询产物与下载的完整流程。""" + with TestClient(app) as client: + workflow_id = _create_published_echo_workflow(client) + apps = client.get("/api/apps") + assert apps.status_code == 200 + assert any(item["id"] == workflow_id for item in apps.json()) + + uploaded = client.post( + f"/api/apps/{workflow_id}/runs", + files={"file": ("sample.txt", b"hello from upload", "text/plain")}, + ) + assert uploaded.status_code == 200 + run_id = uploaded.json()["id"] + assert uploaded.json()["status"] == "QUEUED" + + run = client.get(f"/api/runs/{run_id}") + assert run.status_code == 200 + assert run.json()["input_uri"].endswith("sample.txt") + assert run.json()["artifacts"] == [] + + scheduler = app.state.scheduler + scheduler.execute_run(run_id) + + completed = client.get(f"/api/runs/{run_id}") + assert completed.status_code == 200 + assert completed.json()["status"] == "COMPLETED" + artifact_names = [item["name"] for item in completed.json()["artifacts"]] + assert "result" in artifact_names + + artifacts = client.get(f"/api/runs/{run_id}/artifacts") + assert artifacts.status_code == 200 + assert len(artifacts.json()) >= 1 + + downloaded = client.get(f"/api/runs/{run_id}/artifacts/result") + assert downloaded.status_code == 200 + assert b"hello from upload" in downloaded.content + + assert client.get(f"/api/runs/{run_id}/artifacts/missing").status_code == 404 + assert client.get("/api/runs/missing").status_code == 404 + assert client.get("/api/runs/missing/artifacts").status_code == 404 + + db = app.state.db + db.create_artifact( + { + "run_id": run_id, + "node_id": "step", + "name": "missing-file", + "uri": str(Path(__file__).resolve().parent / "not-exists.bin"), + "mime_type": "text/plain", + "size": 0, + } + ) + assert client.get(f"/api/runs/{run_id}/artifacts/missing-file").status_code == 404 + + runs = client.get("/api/runs") + assert runs.status_code == 200 + assert any(item["id"] == run_id for item in runs.json()) + + +def test_upload_rejects_unpublished_workflow() -> None: + """验证草稿或不存在的工作流不能被用户发起任务。""" + with TestClient(app) as client: + client.post( + "/api/admin/workflows", + json={ + "id": "draft", + "name": "Draft", + "definition": { + "name": "Draft", + "version": 1, + "nodes": [], + "edges": [], + }, + }, + ) + response = client.post( + "/api/apps/draft/runs", + files={"file": ("x.txt", b"x", "text/plain")}, + ) + assert response.status_code == 404 + + response = client.post( + "/api/apps/missing/runs", + files={"file": ("x.txt", b"x", "text/plain")}, + ) + assert response.status_code == 404 + + +def test_upload_rejects_workflow_without_version() -> None: + """验证已发布但没有任何版本的工作流返回 422。""" + with TestClient(app) as client: + db = app.state.db + db.upsert_workflow( + {"id": "empty", "name": "Empty", "published": 1, "latest_version": 0} + ) + response = client.post( + "/api/apps/empty/runs", + files={"file": ("x.txt", b"x", "text/plain")}, + ) + assert response.status_code == 422 + + +def test_retry_failed_run_requeues_and_reruns() -> None: + """验证失败任务重试会清空旧产物并重新执行成功。""" + with TestClient(app) as client: + workflow_id = _create_published_echo_workflow(client) + uploaded = client.post( + f"/api/apps/{workflow_id}/runs", + files={"file": ("sample.txt", b"hello retry", "text/plain")}, + ) + run_id = uploaded.json()["id"] + db = app.state.db + db.update_run( + run_id, + status="FAILED", + error="boom", + updated_at="2026-01-01T00:00:00+00:00", + ) + db.create_artifact( + { + "run_id": run_id, + "node_id": "step", + "name": "stale", + "uri": "stale.txt", + "mime_type": "text/plain", + "size": 1, + } + ) + + response = client.post(f"/api/runs/{run_id}/retry") + + assert response.status_code == 200 + assert response.json() == {"id": run_id, "status": "QUEUED"} + run = client.get(f"/api/runs/{run_id}").json() + assert run["status"] == "QUEUED" + assert run["error"] is None + assert run["artifacts"] == [] + + app.state.scheduler.execute_run(run_id) + completed = client.get(f"/api/runs/{run_id}").json() + assert completed["status"] == "COMPLETED" + assert any(item["name"] == "result" for item in completed["artifacts"]) + + +def test_pause_resume_run_api() -> None: + """验证暂停/继续接口：QUEUED→PAUSED→QUEUED，状态非法时报 422。""" + with TestClient(app) as client: + workflow_id = _create_published_echo_workflow(client) + uploaded = client.post( + f"/api/apps/{workflow_id}/runs", + files={"file": ("sample.txt", b"hello pause", "text/plain")}, + ) + run_id = uploaded.json()["id"] + assert uploaded.json()["status"] == "QUEUED" + + paused = client.post(f"/api/runs/{run_id}/pause") + assert paused.status_code == 200 + assert paused.json() == {"id": run_id, "status": "PAUSED"} + assert client.get(f"/api/runs/{run_id}").json()["status"] == "PAUSED" + + resumed = client.post(f"/api/runs/{run_id}/resume") + assert resumed.status_code == 200 + assert resumed.json() == {"id": run_id, "status": "QUEUED"} + assert client.get(f"/api/runs/{run_id}").json()["status"] == "QUEUED" + + # 非 PAUSED 任务不可继续。 + assert client.post(f"/api/runs/{run_id}/resume").status_code == 422 + # 不存在的任务 404。 + assert client.post("/api/runs/missing/pause").status_code == 404 + assert client.post("/api/runs/missing/resume").status_code == 404 + + +def test_pause_rejects_terminal_states() -> None: + """验证已完成任务不可暂停。""" + with TestClient(app) as client: + workflow_id = _create_published_echo_workflow(client) + uploaded = client.post( + f"/api/apps/{workflow_id}/runs", + files={"file": ("sample.txt", b"hello done", "text/plain")}, + ) + run_id = uploaded.json()["id"] + db = app.state.db + db.update_run(run_id, status="COMPLETED", progress=1.0, updated_at="2026-01-01T00:00:00+00:00") + assert client.post(f"/api/runs/{run_id}/pause").status_code == 422 + + +def test_retry_rejects_non_failed_and_missing_runs() -> None: + """验证只有 FAILED 状态且存在的任务才能重试。""" + with TestClient(app) as client: + workflow_id = _create_published_echo_workflow(client) + uploaded = client.post( + f"/api/apps/{workflow_id}/runs", + files={"file": ("sample.txt", b"x", "text/plain")}, + ) + run_id = uploaded.json()["id"] + + assert client.post(f"/api/runs/{run_id}/retry").status_code == 422 + assert client.post("/api/runs/missing/retry").status_code == 404 + + +def test_delete_run_removes_record_and_files() -> None: + """验证删除任务会清理数据库记录与磁盘上的上传/步骤文件。""" + import shutil + + from wov_app.config import STORAGE_DIR + + with TestClient(app) as client: + workflow_id = _create_published_echo_workflow(client) + uploaded = client.post( + f"/api/apps/{workflow_id}/runs", + files={"file": ("sample.txt", b"hello delete", "text/plain")}, + ) + run_id = uploaded.json()["id"] + + # 执行任务以生成步骤产物目录。 + app.state.scheduler.execute_run(run_id) + run = client.get(f"/api/runs/{run_id}").json() + steps_dir = STORAGE_DIR / "runs" / run_id + assert steps_dir.is_dir() + # 上传文件目录也应存在。 + upload_dir = Path(run["input_uri"]).parent + assert upload_dir.is_dir() + + deleted = client.delete(f"/api/runs/{run_id}") + assert deleted.status_code == 200 + assert deleted.json() == {"deleted": run_id} + + assert client.get(f"/api/runs/{run_id}").status_code == 404 + assert not steps_dir.exists() + assert not upload_dir.exists() + + # 删除不存在的任务返回 404。 + assert client.delete(f"/api/runs/missing").status_code == 404 + + # 清理测试遗留的 runs 目录，避免跨用例残留。 + shutil.rmtree(STORAGE_DIR / "runs", ignore_errors=True) + + +def test_create_run_with_param_overrides() -> None: + """验证创建任务时可携带 params 覆盖（如前端框选的 crop），并持久化。""" + with TestClient(app) as client: + workflow_id = _create_published_echo_workflow(client) + uploaded = client.post( + f"/api/apps/{workflow_id}/runs", + files={"file": ("sample.txt", b"x", "text/plain")}, + data={"params": '{"step": {"crop": [0, 0.82, 1, 0.18]}}'}, + ) + assert uploaded.status_code == 200 + run_id = uploaded.json()["id"] + run = client.get(f"/api/runs/{run_id}").json() + assert run["param_overrides"] == {"step": {"crop": [0, 0.82, 1, 0.18]}} + # 非法 JSON 返回 422。 + bad = client.post( + f"/api/apps/{workflow_id}/runs", + files={"file": ("sample.txt", b"x", "text/plain")}, + data={"params": "not-json"}, + ) + assert bad.status_code == 422 + + +def test_create_run_params_non_object_rejected() -> None: + """params 为 JSON 数组时返回 422。""" + with TestClient(app) as client: + workflow_id = _create_published_echo_workflow(client) + response = client.post( + f"/api/apps/{workflow_id}/runs", + files={"file": ("sample.txt", b"x", "text/plain")}, + data={"params": "[1,2,3]"}, + ) + assert response.status_code == 422 diff --git a/tests/test_crop_js.js b/tests/test_crop_js.js new file mode 100644 index 0000000..b86e668 --- /dev/null +++ b/tests/test_crop_js.js @@ -0,0 +1,32 @@ +// crop 归一化纯函数单测：由 pytest 通过 node 执行（TDD 红阶段先失败）。 +"use strict"; +const assert = require("assert"); +const { videoDisplayRect, rectToCrop, cropToRect } = require("../web/assets/crop.js"); + +// 1) 无留边（容器比例与视频一致）：底部 20% 矩形 → crop [0, 0.8, 1, 0.2] +let d = videoDisplayRect(1280, 720, 1280, 720); +assert.deepStrictEqual(d, { x: 0, y: 0, w: 1280, h: 720 }); +assert.deepStrictEqual( + rectToCrop({ x: 0, y: 576, w: 1280, h: 144 }, 1280, 720, 1280, 720), + [0, 0.8, 1, 0.2] +); + +// 2) letterbox（容器比视频宽）：视频显示在中间，矩形映射要考虑左右留边 +d = videoDisplayRect(1280, 720, 1600, 720); +assert.deepStrictEqual(d, { x: 160, y: 0, w: 1280, h: 720 }); +// 在渲染视频内框选右下 25% 区域 +let crop = rectToCrop({ x: 160 + 640, y: 360, w: 640, h: 360 }, 1280, 720, 1600, 720); +assert.deepStrictEqual(crop, [0.5, 0.5, 0.5, 0.5]); + +// 3) 回显一致性：crop → rect → crop 应还原（含 letterbox） +let back = cropToRect(crop, 1280, 720, 1600, 720); +assert.deepStrictEqual( + rectToCrop(back, 1280, 720, 1600, 720), + crop +); + +// 4) 越界钳制：矩形超出画面时 crop 值被限制在 0~1 +crop = rectToCrop({ x: -100, y: -50, w: 2000, h: 900 }, 1280, 720, 1280, 720); +assert.ok(crop.every((v) => v >= 0 && v <= 1)); + +console.log("crop.js 全部断言通过"); diff --git a/tests/test_db.py b/tests/test_db.py new file mode 100644 index 0000000..18c3e47 --- /dev/null +++ b/tests/test_db.py @@ -0,0 +1,294 @@ +"""数据库层单元测试。 + +直接对 Database 方法调用真实 SQLite 路径，覆盖工作流、版本、任务与产物的 +增删改查。节点注册表已改为进程内内存态，不再落库。 +""" + +from pathlib import Path + +from wov_app.db import Database + + +def test_workflow_crud(tmp_path) -> None: + """验证工作流概要的插入、发布标记更新与删除。""" + db = Database(tmp_path / "wov.db") + workflow = { + "id": "demo", + "name": "Demo", + "description": "desc", + "published": 0, + "latest_version": 0, + } + db.upsert_workflow(workflow) + assert db.get_workflow("demo")["name"] == "Demo" + assert [item["id"] for item in db.list_workflows()] == ["demo"] + + db.upsert_workflow({**workflow, "published": 1, "latest_version": 1}) + assert db.get_workflow("demo")["published"] == 1 + + db.delete_workflow("demo") + assert db.get_workflow("demo") is None + + +def test_workflow_versions(tmp_path) -> None: + """验证工作流版本的写入、最新版本查询与列表。""" + db = Database(tmp_path / "wov.db") + db.upsert_workflow( + {"id": "demo", "name": "Demo", "published": 1, "latest_version": 2} + ) + definition = {"name": "Demo", "version": 1, "nodes": [], "edges": []} + db.create_workflow_version("demo", 1, definition) + db.create_workflow_version("demo", 2, {**definition, "version": 2}) + + latest = db.get_latest_workflow_version("demo") + assert latest["version"] == 2 + assert latest["definition"]["version"] == 2 + + version = db.get_workflow_version("demo", 1) + assert version["version"] == 1 + assert db.get_workflow_version("demo", 99) is None + assert len(db.list_workflow_versions("demo")) == 2 + + empty_db = Database(tmp_path / "empty.db") + assert empty_db.get_latest_workflow_version("missing") is None + + +def test_run_and_artifact_crud(tmp_path) -> None: + """验证任务与产物的创建、查询、更新与删除。""" + db = Database(tmp_path / "wov.db") + db.upsert_workflow({"id": "demo", "name": "Demo", "published": 1, "latest_version": 1}) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_1", + "workflow_id": "demo", + "workflow_version": 1, + "status": "QUEUED", + "progress": 0, + "input_uri": "in.txt", + "created_at": now, + "updated_at": now, + } + ) + assert db.get_run("run_1")["status"] == "QUEUED" + assert db.next_queued_run()["id"] == "run_1" + + db.update_run("run_1", status="RUNNING", progress=0.5, updated_at=now) + db.update_run("run_1") + assert db.get_run("run_1")["status"] == "RUNNING" + assert db.get_run("run_1")["progress"] == 0.5 + assert db.next_queued_run() is None + assert len(db.list_runs()) == 1 + + db.create_artifact( + { + "run_id": "run_1", + "node_id": "echo", + "name": "result", + "uri": "out.txt", + "mime_type": "text/plain", + "size": 3, + } + ) + assert db.get_artifact("run_1", "result")["uri"] == "out.txt" + assert db.get_artifact("run_1", "missing") is None + assert len(db.list_artifacts("run_1")) == 1 + + db.delete_run_artifacts("run_1") + assert db.list_artifacts("run_1") == [] + + +def test_reset_run_clears_error_and_artifacts(tmp_path) -> None: + """验证 reset_run 会把失败任务恢复到排队状态并清空旧产物。""" + db = Database(tmp_path / "wov.db") + db.upsert_workflow({"id": "demo", "name": "Demo", "published": 1, "latest_version": 1}) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_1", + "workflow_id": "demo", + "workflow_version": 1, + "status": "FAILED", + "progress": 0.75, + "current_node_id": "translate", + "error": "timed out", + "input_uri": "in.txt", + "created_at": now, + "updated_at": now, + } + ) + db.create_artifact( + { + "run_id": "run_1", + "node_id": "asr", + "name": "asr.srt_uri", + "uri": "out.srt", + "mime_type": "application/x-subrip", + "size": 3, + } + ) + + db.reset_run("run_1", "2026-01-02T00:00:00+00:00") + + run = db.get_run("run_1") + assert run["status"] == "QUEUED" + assert run["progress"] == 0 + assert run["current_node_id"] is None + assert run["error"] is None + assert run["updated_at"] == "2026-01-02T00:00:00+00:00" + assert run["created_at"] == now + assert db.list_artifacts("run_1") == [] + + +def test_delete_run(tmp_path) -> None: + """验证 delete_run 会删除任务记录及其产物记录。""" + db = Database(tmp_path / "wov.db") + db.upsert_workflow({"id": "demo", "name": "Demo", "published": 1, "latest_version": 1}) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_1", + "workflow_id": "demo", + "workflow_version": 1, + "status": "COMPLETED", + "progress": 1, + "input_uri": "in.txt", + "created_at": now, + "updated_at": now, + } + ) + db.create_artifact( + { + "run_id": "run_1", + "node_id": "asr", + "name": "asr.srt_uri", + "uri": "out.srt", + "mime_type": "application/x-subrip", + "size": 3, + } + ) + db.delete_run("run_1") + assert db.get_run("run_1") is None + assert db.list_artifacts("run_1") == [] + + +def test_list_run_ids(tmp_path) -> None: + """验证 list_run_ids 返回全部任务 ID，供孤儿清理对照使用。""" + db = Database(tmp_path / "wov.db") + db.upsert_workflow({"id": "demo", "name": "Demo", "published": 1, "latest_version": 1}) + now = "2026-01-01T00:00:00+00:00" + assert db.list_run_ids() == [] + for run_id in ("run_a", "run_b"): + db.create_run( + { + "id": run_id, + "workflow_id": "demo", + "workflow_version": 1, + "status": "QUEUED", + "progress": 0, + "created_at": now, + "updated_at": now, + } + ) + assert set(db.list_run_ids()) == {"run_a", "run_b"} + + +def test_run_param_overrides_persist(tmp_path) -> None: + """验证 param_overrides 随任务持久化并可读回。""" + db = Database(tmp_path / "wov.db") + db.upsert_workflow({"id": "demo", "name": "D", "published": 1, "latest_version": 1}) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_ov", + "workflow_id": "demo", + "workflow_version": 1, + "status": "QUEUED", + "progress": 0, + "param_overrides": {"extract": {"crop": [0, 0.5, 1, 0.5]}}, + "created_at": now, + "updated_at": now, + } + ) + run = db.get_run("run_ov") + assert run["param_overrides"] == {"extract": {"crop": [0, 0.5, 1, 0.5]}} + assert db.next_queued_run()["param_overrides"] == {"extract": {"crop": [0, 0.5, 1, 0.5]}} + + +def test_db_migration_adds_param_overrides(tmp_path) -> None: + """旧库迁移：缺少 param_overrides 列的库打开后自动补列。""" + import sqlite3 + + db_path = tmp_path / "old.db" + conn = sqlite3.connect(db_path) + conn.execute( + "CREATE TABLE workflow_runs (id TEXT PRIMARY KEY, workflow_id TEXT NOT NULL," + " workflow_version INTEGER NOT NULL, status TEXT NOT NULL, current_node_id TEXT," + " progress REAL NOT NULL DEFAULT 0, error TEXT, input_uri TEXT," + " created_at TEXT NOT NULL, updated_at TEXT NOT NULL)" + ) + conn.commit() + conn.close() + + Database(db_path) + conn = sqlite3.connect(db_path) + columns = [row[1] for row in conn.execute("PRAGMA table_info(workflow_runs)")] + conn.close() + assert "param_overrides" in columns + + +def test_pause_resume_run(tmp_path) -> None: + """验证 pause_run/resume_run 的状态流转与 PAUSED 任务可被调度器取到。""" + db = Database(tmp_path / "wov.db") + db.upsert_workflow({"id": "demo", "name": "Demo", "published": 1, "latest_version": 1}) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_p", + "workflow_id": "demo", + "workflow_version": 1, + "status": "QUEUED", + "progress": 0, + "created_at": now, + "updated_at": now, + } + ) + db.pause_run("run_p", now) + assert db.get_run("run_p")["status"] == "PAUSED" + # PAUSED 任务会被 next_queued_run 取到（等待续跑）。 + assert db.next_queued_run()["id"] == "run_p" + db.resume_run("run_p", now) + assert db.get_run("run_p")["status"] == "QUEUED" + assert db.next_queued_run()["id"] == "run_p" + + +def test_restore_run_outputs(tmp_path) -> None: + """验证从产物重建节点输出（断点续跑的依据）。""" + db = Database(tmp_path / "wov.db") + db.upsert_workflow({"id": "demo", "name": "Demo", "published": 1, "latest_version": 1}) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_r", + "workflow_id": "demo", + "workflow_version": 1, + "status": "PAUSED", + "progress": 0, + "created_at": now, + "updated_at": now, + } + ) + db.create_artifact( + { + "run_id": "run_r", + "node_id": "extract", + "name": "frames_manifest", + "uri": "frames.json", + "mime_type": "application/json", + "size": 1, + } + ) + assert db.restore_run_outputs("run_r") == { + "extract": {"frames_manifest": "frames.json"} + } + assert db.restore_run_outputs("run_none") == {} diff --git a/tests/test_frontend_crop.py b/tests/test_frontend_crop.py new file mode 100644 index 0000000..7fefaa0 --- /dev/null +++ b/tests/test_frontend_crop.py @@ -0,0 +1,28 @@ +"""前端 crop 归一化纯函数测试。 + +通过 node 执行 tests/test_crop_js.js（真实 JS 断言），验证框选矩形与 +crop 比例的互转（含 letterbox 与越界钳制）。 +""" + +import shutil +import subprocess +from pathlib import Path + +import pytest + +# 单体根目录：tests/ 的上一级。 +WORKSPACE = Path(__file__).resolve().parent.parent +JS_TEST = WORKSPACE / "tests" / "test_crop_js.js" + + +def test_crop_js_normalization() -> None: + """node 执行 crop 纯函数断言（TDD 红阶段先失败）。""" + if shutil.which("node") is None: + pytest.skip("环境无 node，跳过前端 crop 单测") + result = subprocess.run( + ["node", str(JS_TEST)], + capture_output=True, + text=True, + cwd=str(WORKSPACE), + ) + assert result.returncode == 0, result.stderr diff --git a/tests/test_integration_subtitle_ocr.py b/tests/test_integration_subtitle_ocr.py new file mode 100644 index 0000000..6106ac2 --- /dev/null +++ b/tests/test_integration_subtitle_ocr.py @@ -0,0 +1,90 @@ +"""字幕 OCR 整链真实集成测试。 + +使用 testdata/subtitle_10s.mp4（烧录 SUB 001@1-4s、SUB 002@6-9s）与真实 +glm-ocr 模型：抽帧（frame-extract）→ 逐帧 OCR（subtitle-ocr）→ 汇总 SRT， +断言烧录文字与时间轴对齐。Ollama 服务或资产缺失时自动跳过。 +""" + +import json +import urllib.request +from pathlib import Path + +import pytest + +from nodes.frame_extract import invoke as frame_invoke +from nodes.subtitle_ocr import invoke as ocr_invoke +from wov_sdk.models import InvokeRequest + +OLLAMA_HOST = "http://192.168.123.70:11434" +MODEL = "glm-ocr:latest" + +# 单体根目录：tests/ 的上一级。 +WORKSPACE = Path(__file__).resolve().parent.parent +VIDEO = WORKSPACE / "testdata" / "subtitle_10s.mp4" + + +def _register_nodes() -> None: + """注册全部内置节点，供 subtitle-ocr 内部调 vlm-ocr 使用。""" + from wov_app import registry + + registry.register_all() + + +def _ollama_reachable() -> bool: + """探测 Ollama 服务与目标模型是否可用。""" + try: + req = urllib.request.Request( + f"{OLLAMA_HOST}/api/show", + data=b'{"model": "%s"}' % MODEL.encode(), + headers={"Content-Type": "application/json"}, + method="POST", + ) + with urllib.request.urlopen(req, timeout=5) as resp: + return resp.status == 200 + except (urllib.error.URLError, OSError): + return False + + +@pytest.mark.integration +def test_subtitle_ocr_full_chain(tmp_path) -> None: + """抽帧→OCR→汇总：SRT 应含 SUB 001/SUB 002 且时间轴落在各自区间。""" + if not _ollama_reachable(): + pytest.skip("Ollama 服务或 glm-ocr 模型不可用，跳过真实模型集成测试") + if not VIDEO.is_file(): + pytest.skip("缺少 testdata/subtitle_10s.mp4 测试资产，跳过集成测试") + _register_nodes() + + # 抽帧：1s 间隔，字幕在底部，裁切下半 30% 区域（y=0.7,h=0.3）。 + frames_resp = frame_invoke( + InvokeRequest( + run_id="chain_fx", + node_instance_id="", + inputs={"video_uri": str(VIDEO)}, + params={"interval_seconds": 1, "crop": [0, 0.7, 1, 0.3]}, + output_dir=str(tmp_path / "frames"), + ) + ) + assert frames_resp.status == "completed", frames_resp.error + manifest = json.loads(Path(frames_resp.outputs["frames_manifest"]).read_text(encoding="utf-8")) + assert len(manifest) >= 8 + + # OCR 汇总：真实 glm-ocr 逐帧识别。 + ocr_resp = ocr_invoke( + InvokeRequest( + run_id="chain_ocr", + node_instance_id="", + inputs={"frames_manifest": str(frames_resp.outputs["frames_manifest"])}, + params={"model": MODEL, "ollama_host": OLLAMA_HOST, "min_chars": 2}, + output_dir=str(tmp_path / "out"), + ) + ) + assert ocr_resp.status == "completed", ocr_resp.error + srt = Path(ocr_resp.outputs["srt_uri"]).read_text(encoding="utf-8") + # 两条烧录字幕都应被识别（文字可能带噪声，但至少含关键片段）。 + assert "SUB" in srt + # 时间轴：SUB 001 应在 1-4s，SUB 002 应在 6-9s（允许模型/抽帧容差）。 + first_line = next(line for line in srt.splitlines() if "-->" in line) + start = first_line.split(" --> ")[0].replace(",", ".") + hours, minutes, seconds = start.split(":") + total = int(hours) * 3600 + int(minutes) * 60 + float(seconds) + assert total < 5 diff --git a/tests/test_integration_vlm.py b/tests/test_integration_vlm.py new file mode 100644 index 0000000..f444c2a --- /dev/null +++ b/tests/test_integration_vlm.py @@ -0,0 +1,65 @@ +"""VLM OCR 节点真实集成测试。 + +复用 testdata/test_real_hav_sub.png（真实视频字幕截图，一次性入库，避免 +每次测试生成）。调用本地 Ollama 服务（192.168.123.70:11434）的真实 +glm-ocr 模型做 OCR。Ollama 服务或测试资产缺失时自动跳过；可用时必须执行。 +""" +import urllib.request +from pathlib import Path + +import pytest + +from nodes.vlm import invoke +from wov_sdk.models import InvokeRequest + +OLLAMA_HOST = "http://192.168.123.70:11434" +MODEL = "glm-ocr:latest" +# 测试图片（真实视频字幕帧）上应识别出的字幕文本。 +EXPECTED_TEXT = "还有没有什么困扰 或者奇怪的地方吗" + +# 单体根目录：tests/ 的上一级。 +WORKSPACE = Path(__file__).resolve().parent.parent +TEST_IMAGE = WORKSPACE / "testdata" / "test_real_hav_sub.png" + + +def _ollama_reachable() -> bool: + """探测 Ollama 服务与目标模型是否可用。""" + try: + req = urllib.request.Request( + f"{OLLAMA_HOST}/api/show", + data=b'{"model": "%s"}' % MODEL.encode(), + headers={"Content-Type": "application/json"}, + method="POST", + ) + with urllib.request.urlopen(req, timeout=5) as resp: + return resp.status == 200 + except (urllib.error.URLError, OSError): + return False + + +@pytest.mark.integration +def test_vlm_ocr_real_model(tmp_path) -> None: + """复用真实字幕截图 + 真实 glm-ocr：应识别出关键字幕文本并清洗围栏垃圾。""" + if not _ollama_reachable(): + pytest.skip("Ollama 服务或 glm-ocr 模型不可用，跳过真实模型集成测试") + if not TEST_IMAGE.is_file(): + pytest.skip("缺少 testdata/test_real_hav_sub.png 测试资产，跳过集成测试") + + response = invoke( + InvokeRequest( + run_id="vlm_integration", + node_instance_id="", + inputs={"image_uri": str(TEST_IMAGE)}, + params={"model": MODEL, "ollama_host": OLLAMA_HOST}, + output_dir=str(tmp_path / "out"), + ) + ) + assert response.status == "completed", response.error + text = response.outputs["text"] + print(text) + # 关键字幕文本应被识别出来。注意 glm-ocr 在此图上存在已知重复循环 bug： + # 识别出正确文本后可能继续循环输出，因此用"包含"断言而非全等， + # 下游 subtitle-ocr 的 max_result_chars 守卫会拦截超长输出。 + assert EXPECTED_TEXT in text + # 围栏垃圾（```）不应出现在输出里。 + assert "```" not in text diff --git a/tests/test_integration_whisper.py b/tests/test_integration_whisper.py new file mode 100644 index 0000000..f3df6f8 --- /dev/null +++ b/tests/test_integration_whisper.py @@ -0,0 +1,52 @@ +"""真实模型集成测试。 + +复用 testdata/speech_60s.wav（真实语音 WAV，一次性生成、入库，避免每次 +测试从视频提取）。使用真实 faster-whisper 模型端到端验证 whisper 节点的 +分块转写与 SRT 生成。本地缺少模型或测试资产时自动跳过；具备条件时必须 +执行，作为对假模型单元测试的校准。 + +约定（见 AGENTS.md「测试与覆盖率」）：单元测试允许在模型推理这一 I/O +边界使用返回真实结构的薄桩，但必须配套本集成测试验证真实行为。 +""" + +from pathlib import Path + +import pytest + +from nodes.whisper import invoke +from wov_sdk.models import InvokeRequest + +# 单体根目录：tests/ 的上一级。 +WORKSPACE = Path(__file__).resolve().parent.parent +MODEL_DIR = WORKSPACE / "model" / "faster-whisper-large-v3" +TEST_AUDIO = WORKSPACE / "testdata" / "speech_60s.wav" + + +@pytest.mark.integration +def test_whisper_real_model_chunked_transcription(tmp_path) -> None: + """复用 testdata 语音 + 真实模型：分块转写产出真实 SRT，时间不越出素材范围。""" + if not (MODEL_DIR / "model.bin").is_file(): + pytest.skip("本地无 faster-whisper-large-v3 模型，跳过真实模型集成测试") + if not TEST_AUDIO.is_file(): + pytest.skip("缺少 testdata/speech_60s.wav 测试资产，跳过真实模型集成测试") + + response = invoke( + InvokeRequest( + run_id="integration_1", + node_instance_id="", + inputs={"audio_uri": str(TEST_AUDIO)}, + params={"language": "ja", "chunk_seconds": 60, "vad_filter": False}, + output_dir=str(tmp_path / "out"), + ) + ) + assert response.status == "completed", response.error + srt_path = Path(response.outputs["srt_uri"]) + assert srt_path.is_file() + srt = srt_path.read_text(encoding="utf-8") + time_lines = [line for line in srt.splitlines() if "-->" in line] + # 60s 语音若含可识别内容，则应有字幕，且时间轴不越出素材时长（允许少量超窗）。 + if time_lines: + last_end = time_lines[-1].split(" --> ")[1].replace(",", ".") + hours, minutes, seconds = last_end.split(":") + total = int(hours) * 3600 + int(minutes) * 60 + float(seconds) + assert total < 90 diff --git a/tests/test_llm_filter.py b/tests/test_llm_filter.py new file mode 100644 index 0000000..b464152 --- /dev/null +++ b/tests/test_llm_filter.py @@ -0,0 +1,257 @@ +"""LLM 字幕过滤节点测试。 + +覆盖 SRT 解析/序列化、±N 上下文窗口组装（纯文本无时间戳、目标标记）、 +LLM 调用（按 I/O 边界 mock urlopen）与删除判定、invoke 全链路与异常路径。 +""" + +import json +import urllib.error +from pathlib import Path + +from nodes.llm_filter import invoke, parse_srt, serialize_srt +from wov_sdk.models import InvokeRequest + +# 4 条字幕的 SRT：第 3 条为"答："开头的无意义杂项，模拟 OCR 噪声。 +_SRT = ( + "1\n00:00:01,000 --> 00:00:04,000\n还有没有什么困扰\n\n" + "2\n00:00:05,000 --> 00:00:08,000\n或者奇怪的地方吗\n\n" + "3\n00:00:09,000 --> 00:00:12,000\n答：无意义杂项\n\n" + "4\n00:00:13,000 --> 00:00:16,000\n第二句正常字幕\n" +) + + +class FakeResponse: + """模拟 urllib 响应：read() 返回 LLM 兼容接口的 JSON 载荷。""" + + def __init__(self, payload: bytes) -> None: + self._payload = payload + + def read(self) -> bytes: + return self._payload + + def __enter__(self): + return self + + def __exit__(self, *args) -> bool: + return False + + +class FakeLLM: + """模拟 LLM 兼容接口：记录请求体，按策略返回"保留/删除"。 + + 支持两种策略：contents（按队列顺序，用于单次直调 _judge_target 的 + 确定性测试）或 decision_fn（按请求体内容决策，用于并发 invoke 测试， + 保证任何线程执行顺序下判定结果都确定）。 + """ + + def __init__(self, contents: list[str] | None = None, decision_fn=None) -> None: + self._contents = list(contents) if contents is not None else None + self._decision_fn = decision_fn + self.bodies: list[dict] = [] + self.headers: list[dict] = [] + + def __call__(self, request, timeout=None): + body = json.loads(request.data.decode("utf-8")) + self.bodies.append(body) + self.headers.append(dict(request.headers)) + if self._decision_fn is not None: + content = self._decision_fn(body) + else: + content = self._contents.pop(0) + payload = json.dumps({"choices": [{"message": {"content": content}}]}).encode() + return FakeResponse(payload) + + +def _patch_llm(monkeypatch, contents: list[str] | None = None, decision_fn=None) -> FakeLLM: + """替换 nodes.llm_filter 的 urlopen 为 FakeLLM 并返回实例。""" + fake = FakeLLM(contents=contents, decision_fn=decision_fn) + monkeypatch.setattr("nodes.llm_filter.urllib.request.urlopen", fake) + return fake + + +def _decision_by_target(body) -> str: + """按目标字幕内容决策：含"答："判为删除，其余保留（与 _SRT 的噪声对应）。""" + target = next( + line for line in body["messages"][1]["content"].splitlines() + if line.startswith("【目标】") + ) + return "删除" if "答：" in target else "保留" + + +def test_parse_srt_multiline_and_last_block() -> None: + """解析 SRT：多行文本与末条无空行结尾均能正确解析。""" + text = ( + "1\n00:00:01,000 --> 00:00:04,000\n第一行\n第二行\n\n" + "2\n00:00:05,000 --> 00:00:08,000\n末条无空行结尾\n" + ) + entries = parse_srt(text) + assert len(entries) == 2 + assert entries[0]["start"] == "00:00:01,000" + assert entries[0]["end"] == "00:00:04,000" + assert entries[0]["text"] == "第一行\n第二行" + assert entries[1]["text"] == "末条无空行结尾" + + +def test_serialize_srt_renumbers() -> None: + """序列化：序号从 1 重新编号，保留原始时间轴。""" + entries = [ + {"start": "00:00:09,000", "end": "00:00:12,000", "text": "答：无意义杂项"}, + {"start": "00:00:13,000", "end": "00:00:16,000", "text": "第二句正常字幕"}, + ] + out = serialize_srt(entries) + assert out == ( + "1\n00:00:09,000 --> 00:00:12,000\n答：无意义杂项\n\n" + "2\n00:00:13,000 --> 00:00:16,000\n第二句正常字幕\n" + ) + + +def test_judge_target_window_and_keep(monkeypatch) -> None: + """窗口只含纯文本（无时间戳）、目标带标记；模型答"保留"则返回 False。""" + from nodes.llm_filter import _judge_target + + entries = parse_srt(_SRT) + fake = _patch_llm(monkeypatch, ["保留"]) + # context_size=1，目标为第 2 条（index=1）：窗口 0..2 共 3 行，目标在中间。 + assert _judge_target(entries, 1, context_size=1, params={}) is False + body = fake.bodies[0] + lines = body["messages"][1]["content"].splitlines() + assert len(lines) == 3 + assert lines[0] == "还有没有什么困扰" + assert lines[1] == "【目标】或者奇怪的地方吗" + assert lines[2] == "答：无意义杂项" + # 不含时间戳。 + assert "00:00" not in body["messages"][1]["content"] + assert body["enable_thinking"] is False + assert body["max_tokens"] == 16 + + +def test_judge_target_delete(monkeypatch) -> None: + """模型答"删除"时返回 True（判定该条无意义）。""" + from nodes.llm_filter import _judge_target + + entries = parse_srt(_SRT) + _patch_llm(monkeypatch, ["删除"]) + assert _judge_target(entries, 2, context_size=10, params={}) is True + + +def test_judge_target_model_and_auth(monkeypatch) -> None: + """模型名从参数取；配置 API Key 时附带 Bearer 鉴权头。""" + from nodes.llm_filter import _judge_target + + entries = parse_srt(_SRT) + monkeypatch.setenv("LLM_API_KEY", "sk-test") + fake = _patch_llm(monkeypatch, ["保留"]) + assert _judge_target(entries, 0, context_size=10, params={"model": "m/1"}) is False + assert fake.bodies[0]["model"] == "m/1" + assert fake.headers[0]["Authorization"] == "Bearer sk-test" + + +def test_invoke_filters_and_renumbers(monkeypatch, tmp_path) -> None: + """全链路（并发）：按 LLM 判定删除无意义条，保留条重新编号输出。""" + srt = tmp_path / "in.srt" + srt.write_text(_SRT, encoding="utf-8") + # 内容决策：目标字幕含"答："判删除，其余保留（任何线程顺序下结果确定）。 + _patch_llm(monkeypatch, decision_fn=_decision_by_target) + response = invoke( + InvokeRequest( + run_id="r", node_instance_id="", + inputs={"srt_uri": str(srt)}, + params={}, + output_dir=str(tmp_path / "out"), + ) + ) + assert response.status == "completed", response.error + assert response.outputs["kept"] == 3 + assert response.outputs["removed"] == 1 + out = Path(response.outputs["srt_uri"]).read_text(encoding="utf-8") + assert out.count("-->") == 3 + # 被删除的"答：无意义杂项"（第 3 条）时间轴不再出现。 + assert "00:00:09,000" not in out + # 保留条重新编号且时间轴不变。 + assert out.startswith("1\n00:00:01,000 --> 00:00:04,000\n还有没有什么困扰\n\n2\n") + assert "00:00:13,000 --> 00:00:16,000\n第二句正常字幕\n" in out + + +def test_invoke_context_size_param(monkeypatch, tmp_path) -> None: + """context_size 参数生效：窗口大小=2×context_size+1（两端截断除外）。""" + srt = tmp_path / "in.srt" + srt.write_text(_SRT, encoding="utf-8") + fake = _patch_llm(monkeypatch, decision_fn=_decision_by_target) + response = invoke( + InvokeRequest( + run_id="r", node_instance_id="", + inputs={"srt_uri": str(srt)}, + params={"context_size": 1}, + output_dir=str(tmp_path / "out"), + ) + ) + assert response.status == "completed", response.error + # 找到目标为第 2 条（"或者奇怪的地方吗"）的请求体：窗口应含 3 行。 + body = next( + b for b in fake.bodies + if "【目标】或者奇怪的地方吗" in b["messages"][1]["content"] + ) + window = body["messages"][1]["content"].splitlines() + assert len(window) == 3 + + +def test_invoke_missing_input(tmp_path) -> None: + """缺少 srt_uri 时返回失败。""" + response = invoke( + InvokeRequest(run_id="r", node_instance_id="", inputs={}, output_dir=str(tmp_path)) + ) + assert response.status == "failed" + assert "srt_uri" in response.error + + +def test_invoke_file_missing(tmp_path) -> None: + """srt 文件不存在时返回失败。""" + response = invoke( + InvokeRequest( + run_id="r", node_instance_id="", + inputs={"srt_uri": str(tmp_path / "none.srt")}, + output_dir=str(tmp_path), + ) + ) + assert response.status == "failed" + assert "not found" in response.error + + +def test_invoke_llm_error(monkeypatch, tmp_path) -> None: + """LLM 调用失败（网络错误）时返回 failed，不静默输出未过滤结果。""" + srt = tmp_path / "in.srt" + srt.write_text(_SRT, encoding="utf-8") + + def boom(request, timeout=None): + raise urllib.error.URLError("llm down") + + monkeypatch.setattr("nodes.llm_filter.urllib.request.urlopen", boom) + response = invoke( + InvokeRequest( + run_id="r", node_instance_id="", + inputs={"srt_uri": str(srt)}, + params={}, + output_dir=str(tmp_path / "out"), + ) + ) + assert response.status == "failed" + assert "llm down" in response.error + + +def test_invoke_empty_srt(monkeypatch, tmp_path) -> None: + """空 SRT（无条目）正常完成，输出空文件且不调用 LLM。""" + srt = tmp_path / "empty.srt" + srt.write_text("", encoding="utf-8") + fake = _patch_llm(monkeypatch, []) + response = invoke( + InvokeRequest( + run_id="r", node_instance_id="", + inputs={"srt_uri": str(srt)}, + params={}, + output_dir=str(tmp_path / "out"), + ) + ) + assert response.status == "completed", response.error + assert response.outputs["kept"] == 0 + assert response.outputs["removed"] == 0 + assert fake.bodies == [] diff --git a/tests/test_maintenance.py b/tests/test_maintenance.py new file mode 100644 index 0000000..437d9f6 --- /dev/null +++ b/tests/test_maintenance.py @@ -0,0 +1,165 @@ +"""孤儿数据清理器测试。 + +覆盖 COMPLETED 无文件任务的删除、各类保留分支（有文件/失败/宽限期内）、 +无任务记录的残留目录清理、清理线程启停以及防御性分支。 +""" + +from datetime import datetime, timezone +from pathlib import Path + +from wov_app.db import Database +from wov_app.maintenance import OrphanCleaner + + +def _db(tmp_path) -> Database: + """在临时目录创建独立数据库。""" + return Database(tmp_path / "wov.db") + + +def _make_run(db, run_id, status="COMPLETED", updated="2020-01-01T00:00:00+00:00", input_uri=None): + """创建指定状态与更新时间的工作流任务记录。""" + db.upsert_workflow({"id": "flow", "name": "F", "published": 1, "latest_version": 1}) + db.create_run( + { + "id": run_id, + "workflow_id": "flow", + "workflow_version": 1, + "status": status, + "progress": 1, + "input_uri": input_uri, + "created_at": updated, + "updated_at": updated, + } + ) + + +def _now_iso() -> str: + """返回当前 UTC 时间的 ISO 字符串。""" + return datetime.now(timezone.utc).isoformat() + + +def test_cleaner_removes_completed_orphan_run(tmp_path) -> None: + """验证 COMPLETED 且无任何产物文件、超过宽限期的任务被整体清理。""" + db = _db(tmp_path) + upload_dir = tmp_path / "storage" / "uploads" / "run_orphan" + upload_dir.mkdir(parents=True) + upload_file = upload_dir / "in.mp4" + upload_file.write_bytes(b"x") + _make_run(db, "run_orphan", input_uri=str(upload_file)) + db.create_artifact( + { + "run_id": "run_orphan", + "node_id": "asr", + "name": "asr.srt_uri", + "uri": str(tmp_path / "storage" / "runs" / "run_orphan" / "out.srt"), + "mime_type": "application/x-subrip", + "size": 1, + } + ) + cleaner = OrphanCleaner(db, tmp_path / "storage", grace_seconds=3600) + assert cleaner.clean_once() == 1 + assert db.get_run("run_orphan") is None + assert db.list_artifacts("run_orphan") == [] + assert not upload_dir.exists() + + +def test_cleaner_keeps_completed_run_with_files(tmp_path) -> None: + """验证仍有产物文件的 COMPLETED 任务不会被清理。""" + db = _db(tmp_path) + steps = tmp_path / "storage" / "runs" / "run_keep" + (steps / "asr").mkdir(parents=True) + (steps / "asr" / "out.srt").write_text("1\n00:00:00,000 --> 00:00:01,000\nok\n", encoding="utf-8") + _make_run(db, "run_keep") + cleaner = OrphanCleaner(db, tmp_path / "storage", grace_seconds=3600) + assert cleaner.clean_once() == 0 + assert db.get_run("run_keep") is not None + + +def test_cleaner_keeps_failed_and_recent_runs(tmp_path) -> None: + """验证 FAILED 任务与宽限期内的任务都不会被自动清理。""" + db = _db(tmp_path) + _make_run(db, "run_failed", status="FAILED") + _make_run(db, "run_recent", updated=_now_iso()) + cleaner = OrphanCleaner(db, tmp_path / "storage", grace_seconds=3600) + assert cleaner.clean_once() == 0 + assert db.get_run("run_failed") is not None + assert db.get_run("run_recent") is not None + + +def test_cleaner_removes_dangling_dirs_only(tmp_path) -> None: + """验证无任务记录的残留目录被删除，已有任务的上传目录被保留。""" + db = _db(tmp_path) + ghost_upload = tmp_path / "storage" / "uploads" / "ghost" + ghost_upload.mkdir(parents=True) + ghost_steps = tmp_path / "storage" / "runs" / "ghost" + ghost_steps.mkdir(parents=True) + keep_upload = tmp_path / "storage" / "uploads" / "run_keep" + keep_upload.mkdir(parents=True) + (keep_upload / "in.mp4").write_bytes(b"x") + # run_keep 存在产物文件，不属于孤儿任务。 + keep_steps = tmp_path / "storage" / "runs" / "run_keep" / "asr" + keep_steps.mkdir(parents=True) + (keep_steps / "out.srt").write_text("ok", encoding="utf-8") + _make_run(db, "run_keep") + cleaner = OrphanCleaner(db, tmp_path / "storage", grace_seconds=3600) + assert cleaner.clean_once() == 2 + assert not ghost_upload.exists() + assert not ghost_steps.exists() + assert keep_upload.exists() + + +def test_cleaner_removes_run_with_empty_steps_dir(tmp_path) -> None: + """验证步骤目录存在但为空（无文件）时仍视为孤儿清理。""" + db = _db(tmp_path) + steps = tmp_path / "storage" / "runs" / "run_empty" + (steps / "asr").mkdir(parents=True) + _make_run(db, "run_empty", input_uri="") + cleaner = OrphanCleaner(db, tmp_path / "storage", grace_seconds=3600) + assert cleaner.clean_once() == 1 + assert db.get_run("run_empty") is None + assert not steps.exists() + + +def test_cleaner_default_config_and_defensive_branches(tmp_path, monkeypatch) -> None: + """验证默认配置构造、缺失/非法时间与缺失任务记录的防御分支。""" + db = _db(tmp_path) + # 默认配置（interval/grace 走 config 默认值）。 + cleaner = OrphanCleaner(db, tmp_path / "storage") + assert cleaner.interval_seconds > 0 + assert cleaner.grace_seconds > 0 + # 无更新时间 / 非法时间均保守视为未过期。 + assert cleaner._expired(None) is False + assert cleaner._expired("not-a-date") is False + # 不存在的根目录直接返回 0。 + assert cleaner._clean_dangling(tmp_path / "missing", set()) == 0 + # list_run_ids 返回的 ID 在读取详情前已不存在时跳过。 + monkeypatch.setattr(db, "list_run_ids", lambda: ["ghost"]) + monkeypatch.setattr(db, "get_run", lambda run_id: None) + assert cleaner.clean_once() == 0 + + +def test_cleaner_start_stop_loop(tmp_path) -> None: + """验证清理线程可启动、周期执行并正常停止。""" + import time + + db = _db(tmp_path) + cleaner = OrphanCleaner(db, tmp_path / "storage", interval_seconds=0.05, grace_seconds=3600) + cleaner.start() + try: + cleaner.start() + time.sleep(0.2) + finally: + cleaner.stop() + assert cleaner._thread is None + + +def test_lifespan_starts_cleaner(monkeypatch) -> None: + """验证启用清理器时应用生命周期会启动清理线程并随退出停止。""" + from fastapi.testclient import TestClient + + from wov_app.main import app + + monkeypatch.setenv("WOV_CLEANUP_ENABLED", "1") + with TestClient(app) as client: + assert client.get("/health").status_code == 200 + assert app.state.cleaner._thread is not None diff --git a/tests/test_models.py b/tests/test_models.py new file mode 100755 index 0000000..3214685 --- /dev/null +++ b/tests/test_models.py @@ -0,0 +1,174 @@ +"""wov_sdk.models 的单元测试。 + +测试覆盖所有数据模型的 JSON 往返序列化、字段校验和 manifest 文件加载， +确保协议模型的稳定性。 +""" + +import json + +import pytest + +from wov_sdk.models import ( + HealthResponse, + InvokeRequest, + InvokeResponse, + NodeManifest, + ProgressEvent, + WorkflowDefinition, + WorkflowEdge, + WorkflowNode, +) + + +def valid_manifest() -> NodeManifest: + """构造一个覆盖全部字段的合法 NodeManifest，供测试复用。""" + return NodeManifest( + id="echo", + name="Echo", + version="1.0.0", + capability="echo", + command=["python", "-m", "echo"], + repo_dir="wov-node-echo", + env={"PORT": "0"}, + input_schema={"text": "string"}, + output_schema={"text": "string"}, + max_concurrency=2, + idle_ttl_seconds=15, + health_timeout_seconds=5, + keep_warm=True, + ) + + +def test_manifest_round_trip() -> None: + """验证 manifest 经过 to_dict/from_dict 后保持原值。""" + manifest = valid_manifest() + restored = NodeManifest.from_dict(manifest.to_dict()) + assert restored == manifest + + +@pytest.mark.parametrize( + ("field", "value"), + [ + ("id", ""), + ("name", ""), + ("version", ""), + ("capability", ""), + ("repo_dir", ""), + ("command", []), + ("max_concurrency", 0), + ("idle_ttl_seconds", -1), + ("health_timeout_seconds", 0), + ], +) +def test_manifest_validation(field: str, value: object) -> None: + """验证必填字段为空或数值越界时抛出 ValueError。""" + manifest = valid_manifest() + setattr(manifest, field, value) + with pytest.raises(ValueError): + manifest.validate() + + +def test_manifest_load(tmp_path) -> None: + """验证 NodeManifest.load 能从 JSON 文件读取并校验。""" + path = tmp_path / "node.manifest.json" + path.write_text(json.dumps(valid_manifest().to_dict()), encoding="utf-8") + loaded = NodeManifest.load(str(path)) + assert loaded.id == "echo" + + +def test_invoke_request_round_trip() -> None: + """验证 InvokeRequest 的 JSON 往返序列化。""" + request = InvokeRequest( + run_id="run_1", + node_instance_id="ni_1", + inputs={"text": "hello"}, + params={"temperature": 0.2}, + output_dir="out", + ) + restored = InvokeRequest.from_dict(request.to_dict()) + assert restored == request + + +def test_invoke_response_round_trip() -> None: + """验证 InvokeResponse 的 JSON 往返序列化。""" + response = InvokeResponse(status="completed", outputs={"text": "hello"}) + restored = InvokeResponse.from_dict(response.to_dict()) + assert restored == response + + +def test_health_and_progress_serialization() -> None: + """验证健康检查和进度事件模型的字典输出。""" + health = HealthResponse(status="ok", node_id="echo", version="1.0.0") + assert health.to_dict() == { + "status": "ok", + "node_id": "echo", + "version": "1.0.0", + } + + progress = ProgressEvent(run_id="run_1", node_id="echo", progress=0.5, message="half") + assert progress.to_dict() == { + "run_id": "run_1", + "node_id": "echo", + "progress": 0.5, + "message": "half", + } + + +def test_workflow_node_and_edge_round_trip() -> None: + """验证工作流节点与边的 JSON 往返序列化。""" + node = WorkflowNode( + id="asr", + node_type="faster-whisper", + params={"language": "ja"}, + inputs={"audio_uri": "extract.audio_uri"}, + ) + edge = WorkflowEdge(from_node="extract", to_node="asr") + assert WorkflowNode.from_dict(node.to_dict()) == node + assert WorkflowEdge.from_dict(edge.to_dict()) == edge + assert edge.to_dict() == {"from": "extract", "to": "asr"} + + assert node.to_dict()["inputs"] == {"audio_uri": "extract.audio_uri"} + + +def test_workflow_definition_round_trip_and_validation() -> None: + """验证完整 DAG 定义可往返序列化并通过校验。""" + definition = WorkflowDefinition( + name="demo", + version=1, + nodes=[ + WorkflowNode(id="extract", node_type="ffmpeg"), + WorkflowNode(id="asr", node_type="whisper"), + ], + edges=[WorkflowEdge(from_node="extract", to_node="asr")], + entry_inputs={"video_uri": "file"}, + final_outputs={"srt": "asr.srt_uri"}, + ) + restored = WorkflowDefinition.from_dict(definition.to_dict()) + assert restored == definition + restored.validate() + + +def test_workflow_definition_invalid() -> None: + """验证非法 DAG（空名、版本为 0、重复节点、未知边）被拒绝。""" + with pytest.raises(ValueError): + WorkflowDefinition(name="", version=1).validate() + + with pytest.raises(ValueError): + WorkflowDefinition(name="demo", version=0).validate() + + duplicate = WorkflowDefinition( + name="demo", + version=1, + nodes=[WorkflowNode(id="a", node_type="x"), WorkflowNode(id="a", node_type="y")], + ) + with pytest.raises(ValueError): + duplicate.validate() + + unknown_edge = WorkflowDefinition( + name="demo", + version=1, + nodes=[WorkflowNode(id="a", node_type="x")], + edges=[WorkflowEdge(from_node="a", to_node="missing")], + ) + with pytest.raises(ValueError): + unknown_edge.validate() diff --git a/tests/test_nodes.py b/tests/test_nodes.py new file mode 100644 index 0000000..1324aa8 --- /dev/null +++ b/tests/test_nodes.py @@ -0,0 +1,1295 @@ +"""进程内节点测试。 + +合并原 5 个节点仓库的测试：echo、ffmpeg、whisper、llm、ass。节点已变为 +进程内模块（nodes/*.py），移除了原入口点（__main__/run_node）相关测试。 +""" + +import base64 +import json +import subprocess +import sys +import threading +import types +import urllib.error +import urllib.request +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer +from pathlib import Path + +from nodes.ass import invoke as ass_invoke +from nodes.ass import parse_srt, write_ass +from nodes.echo import invoke as echo_invoke +from nodes.ffmpeg import _ffmpeg_bin, invoke as ffmpeg_invoke +from nodes.llm import invoke as llm_invoke +from nodes.llm import translate_lines +from nodes.whisper import format_timestamp +from nodes.whisper import invoke as whisper_invoke +from nodes.whisper import resolve_model_path +from wov_sdk.models import InvokeRequest + +# 单体根目录：tests/ 的上一级。 +WORKSPACE = Path(__file__).resolve().parent.parent + +# --------------------------------------------------------------------------- +# Echo 节点 +# --------------------------------------------------------------------------- + + +def test_echo_invoke_text(tmp_path) -> None: + """验证直接传入 text 时 echo 节点原样写出文本。""" + response = echo_invoke( + InvokeRequest( + run_id="run_1", + node_instance_id="ni_1", + inputs={"text": "hello echo"}, + output_dir=str(tmp_path), + ) + ) + assert response.status == "completed" + assert response.outputs["text"] == "hello echo" + assert Path(response.outputs["file_uri"]).read_text(encoding="utf-8") == "hello echo" + + +def test_echo_invoke_absolute_file(tmp_path) -> None: + """验证绝对路径 file_uri 的文件内容被读取为输入。""" + source = tmp_path / "input.txt" + source.write_text("from file", encoding="utf-8") + response = echo_invoke( + InvokeRequest( + run_id="run_2", + node_instance_id="ni_2", + inputs={"file_uri": str(source)}, + output_dir=str(tmp_path / "out"), + ) + ) + assert response.status == "completed" + assert response.outputs["text"] == "from file" + + +def test_echo_invoke_relative_file(tmp_path) -> None: + """验证相对路径 file_uri 以单体根目录为基准解析。""" + source = WORKSPACE / "relative_input.txt" + source.write_text("relative", encoding="utf-8") + try: + response = echo_invoke( + InvokeRequest( + run_id="run_3", + node_instance_id="ni_3", + inputs={"file_uri": "relative_input.txt"}, + output_dir=str(tmp_path / "out"), + ) + ) + finally: + source.unlink() + assert response.status == "completed" + assert response.outputs["text"] == "relative" + + +def test_echo_invoke_default_text(tmp_path) -> None: + """验证无任何输入时 echo 节点返回默认文本。""" + response = echo_invoke( + InvokeRequest( + run_id="run_4", + node_instance_id="ni_4", + inputs={}, + output_dir=str(tmp_path), + ) + ) + assert response.status == "completed" + assert response.outputs["text"] == "echo" + + +# --------------------------------------------------------------------------- +# FFmpeg 节点 +# --------------------------------------------------------------------------- + + +def _ffmpeg_request(tmp_path, **overrides) -> InvokeRequest: + """构造包含默认视频输入与提音参数的调用请求。""" + payload = { + "run_id": "run_1", + "node_instance_id": "ni_1", + "inputs": {"video_uri": str(tmp_path / "input.mp4")}, + "params": {"sample_rate": 16000, "channels": 1}, + "output_dir": str(tmp_path / "out"), + } + payload.update(overrides) + return InvokeRequest(**payload) + + +def test_ffmpeg_success(tmp_path, monkeypatch) -> None: + """验证成功调用会生成 audio.wav 产物。""" + def fake_run(command, **kwargs): + output = Path(command[-1]) + output.parent.mkdir(parents=True, exist_ok=True) + output.write_bytes(b"fake wav") + return subprocess.CompletedProcess(command, 0) + + monkeypatch.setattr("nodes.ffmpeg.shutil.which", lambda _: "ffmpeg") + monkeypatch.setattr("nodes.ffmpeg.subprocess.run", fake_run) + response = ffmpeg_invoke(_ffmpeg_request(tmp_path)) + assert response.status == "completed" + assert Path(response.outputs["audio_uri"]).name == "audio.wav" + + +def test_ffmpeg_configured_bin(tmp_path, monkeypatch) -> None: + """验证 FFMPEG_BIN 环境变量优先于 PATH 查找。""" + fake_bin = tmp_path / "ffmpeg.exe" + fake_bin.write_bytes(b"") + monkeypatch.setenv("FFMPEG_BIN", str(fake_bin)) + monkeypatch.setattr("nodes.ffmpeg.subprocess.run", lambda *a, **k: subprocess.CompletedProcess([], 0)) + response = ffmpeg_invoke(_ffmpeg_request(tmp_path)) + assert response.status == "completed" + + +def test_ffmpeg_bundled_fallback(tmp_path, monkeypatch) -> None: + """验证无系统 ffmpeg 时回退到 imageio-ffmpeg 内置二进制。""" + monkeypatch.delenv("FFMPEG_BIN", raising=False) + monkeypatch.setattr("nodes.ffmpeg.shutil.which", lambda _: None) + bundled = _ffmpeg_bin() + assert bundled != "ffmpeg" + assert Path(bundled).is_file() + monkeypatch.setattr("nodes.ffmpeg._ffmpeg_bin", lambda: bundled) + + def fake_run(command, **kwargs): + Path(command[-1]).parent.mkdir(parents=True, exist_ok=True) + Path(command[-1]).write_bytes(b"wav") + return subprocess.CompletedProcess(command, 0) + + monkeypatch.setattr("nodes.ffmpeg.subprocess.run", fake_run) + response = ffmpeg_invoke(_ffmpeg_request(tmp_path)) + assert response.status == "completed" + + +def test_ffmpeg_bundled_import_error(monkeypatch) -> None: + """验证 imageio-ffmpeg 不可用时最终回退为 "ffmpeg" 字符串。""" + monkeypatch.delenv("FFMPEG_BIN", raising=False) + monkeypatch.setattr("nodes.ffmpeg.shutil.which", lambda _: None) + monkeypatch.setitem(sys.modules, "imageio_ffmpeg", None) + assert _ffmpeg_bin() == "ffmpeg" + + +def test_ffmpeg_missing_video_uri(tmp_path) -> None: + """验证缺少 video_uri 时返回失败。""" + response = ffmpeg_invoke(_ffmpeg_request(tmp_path, inputs={})) + assert response.status == "failed" + assert "video_uri" in response.error + + +def test_ffmpeg_missing_bin(tmp_path, monkeypatch) -> None: + """验证找不到任何 ffmpeg 时返回明确失败信息。""" + monkeypatch.delenv("FFMPEG_BIN", raising=False) + monkeypatch.setattr("nodes.ffmpeg.shutil.which", lambda _: None) + monkeypatch.setattr("nodes.ffmpeg._bundled_ffmpeg", lambda: None) + response = ffmpeg_invoke(_ffmpeg_request(tmp_path)) + assert response.status == "failed" + assert "ffmpeg not found" in response.error + + +def test_ffmpeg_failure(tmp_path, monkeypatch) -> None: + """验证 ffmpeg 返回非零退出码时透传 stderr 错误。""" + def fake_run(command, **kwargs): + return subprocess.CompletedProcess(command, 1, stderr="boom") + + monkeypatch.setattr("nodes.ffmpeg.shutil.which", lambda _: "ffmpeg") + monkeypatch.setattr("nodes.ffmpeg.subprocess.run", fake_run) + response = ffmpeg_invoke(_ffmpeg_request(tmp_path)) + assert response.status == "failed" + assert "boom" in response.error + + +# --------------------------------------------------------------------------- +# Whisper 节点 +# --------------------------------------------------------------------------- + + +class FakeSegment: + """模拟 faster-whisper 的分段对象，只提供转写测试需要的字段。""" + + def __init__(self, start, end, text): + self.start = start + self.end = end + self.text = text + + +class FakeWhisperModel: + """记录构造参数并返回固定分段的假 WhisperModel。""" + + instances: list[tuple[tuple, dict]] = [] + + def __init__(self, *args, **kwargs): + # 记录每次构造参数，测试据此断言 device/compute_type 传递。 + FakeWhisperModel.instances.append((args, kwargs)) + self.args = args + self.kwargs = kwargs + + def transcribe(self, path, **kwargs): + # 返回固定两个分段：一个普通时长，一个跨小时验证时间戳格式。 + return ( + [ + FakeSegment(0, 1, "第一段"), + FakeSegment(3600.5, 3602.25, "第二段"), + ], + None, + ) + + +def _install_fake_whisper(monkeypatch, model_class=FakeWhisperModel) -> None: + """把假 faster_whisper 模块注入 sys.modules，替代真实依赖。""" + fake_module = types.SimpleNamespace(WhisperModel=model_class) + monkeypatch.setitem(sys.modules, "faster_whisper", fake_module) + + +def _whisper_request(tmp_path, **overrides) -> InvokeRequest: + """构造默认音频输入与日语参数的调用请求。""" + payload = { + "run_id": "run_1", + "node_instance_id": "ni_1", + "inputs": {"audio_uri": str(tmp_path / "audio.wav")}, + "params": {"language": "ja"}, + "output_dir": str(tmp_path / "out"), + } + payload.update(overrides) + return InvokeRequest(**payload) + + +def test_resolve_explicit_param_wins(tmp_path) -> None: + """验证请求参数中的 model_path 优先级最高，覆盖环境变量与本地候选。""" + local = tmp_path / "model" + local.mkdir() + (local / "model.bin").write_bytes(b"x") + resolved = resolve_model_path( + {"model_path": "/opt/custom-model"}, + env={"WHISPER_MODEL_PATH": "/env/model"}, + candidates=[local], + ) + assert resolved == "/opt/custom-model" + + +def test_resolve_env_wins_over_local(tmp_path) -> None: + """验证 WHISPER_MODEL_PATH 环境变量优先于本地候选目录。""" + local = tmp_path / "model" + local.mkdir() + (local / "model.bin").write_bytes(b"x") + resolved = resolve_model_path( + {}, + env={"WHISPER_MODEL_PATH": "/env/model"}, + candidates=[local], + ) + assert resolved == "/env/model" + + +def test_resolve_local_candidate_used(tmp_path) -> None: + """验证无参数与环境变量时优先使用含 model.bin 的本地候选目录。""" + local = tmp_path / "model" + local.mkdir() + (local / "model.bin").write_bytes(b"x") + resolved = resolve_model_path({}, env={}, candidates=[local]) + assert resolved == str(local) + + +def test_resolve_incomplete_candidate_skipped(tmp_path) -> None: + """验证缺少 model.bin 的候选目录被跳过，避免加载残缺模型。""" + empty = tmp_path / "empty" + empty.mkdir() + resolved = resolve_model_path({}, env={}, candidates=[empty]) + assert resolved == "large-v3" + + +def test_resolve_fallback_remote() -> None: + """验证全部本地候选缺失时回退到远端 large-v3 作为最后兜底。""" + resolved = resolve_model_path({}, env={}, candidates=[]) + assert resolved == "large-v3" + + +def test_format_timestamp() -> None: + """验证秒数到 SRT 时间戳的格式化结果。""" + assert format_timestamp(0) == "00:00:00,000" + assert format_timestamp(3600.5) == "01:00:00,500" + assert format_timestamp(61.25) == "00:01:01,250" + + +def test_whisper_success(tmp_path, monkeypatch) -> None: + """验证成功转写会生成 SRT 并默认使用 auto 设备/计算类型。""" + FakeWhisperModel.instances.clear() + _install_fake_whisper(monkeypatch) + _make_wav(tmp_path / "audio.wav", 5) + response = whisper_invoke(_whisper_request(tmp_path)) + assert response.status == "completed" + content = Path(response.outputs["srt_uri"]).read_text(encoding="utf-8") + assert "第一段" in content + assert "01:00:00,500 --> 01:00:02,250" in content + _, kwargs = FakeWhisperModel.instances[-1] + assert kwargs["device"] == "auto" + assert kwargs["compute_type"] == "auto" + + +def test_load_cuda_libraries_linux(monkeypatch) -> None: + """验证 Linux 下进程内预加载 nvidia 动态库，含失败跳过分支。""" + import ctypes + + from nodes.whisper import _load_cuda_libraries + + # 用真实 nvidia 轮子路径加载，不应抛出异常。 + _load_cuda_libraries() + + # 模拟加载失败分支：部分库抛 OSError 时应被跳过。 + calls: list[str] = [] + real_cdll = ctypes.CDLL + + def fake_cdll(path): + calls.append(str(path)) + if "cudnn" in str(path): + raise OSError("boom") + return real_cdll(path) + + monkeypatch.setattr("nodes.whisper.ctypes.CDLL", fake_cdll) + _load_cuda_libraries() + assert calls + + +def test_load_cuda_libraries_windows(monkeypatch, tmp_path) -> None: + """验证 Windows 分支通过 add_dll_directory 注册 DLL 搜索目录。""" + import os + import sysconfig + + from nodes.whisper import _load_cuda_libraries + + site = tmp_path / "site" + (site / "nvidia" / "cublas" / "bin").mkdir(parents=True) + (site / "nvidia" / "cudnn" / "bin").mkdir(parents=True) + monkeypatch.setattr(sysconfig, "get_paths", lambda: {"purelib": str(site)}) + added: list[str] = [] + # 只注入平台判断与 DLL 目录注册，不改动全局 os.name，避免 pathlib 出错。 + monkeypatch.setattr("nodes.whisper._is_windows", lambda: True) + monkeypatch.setattr(os, "add_dll_directory", lambda d: added.append(d), raising=False) + _load_cuda_libraries() + assert any("cublas" in d and d.endswith("bin") for d in added) +def test_whisper_compute_type_override(tmp_path, monkeypatch) -> None: + """验证请求参数可以覆盖默认计算类型。""" + FakeWhisperModel.instances.clear() + _install_fake_whisper(monkeypatch) + _make_wav(tmp_path / "audio.wav", 5) + response = whisper_invoke( + _whisper_request(tmp_path, params={"language": "ja", "compute_type": "int8"}) + ) + assert response.status == "completed" + _, kwargs = FakeWhisperModel.instances[-1] + assert kwargs["compute_type"] == "int8" + + +def test_whisper_model_raises(tmp_path, monkeypatch) -> None: + """验证模型加载失败时返回 failed 与错误信息。""" + class BrokenModel: + def __init__(self, *args, **kwargs): + raise RuntimeError("model load failed") + + _install_fake_whisper(monkeypatch, BrokenModel) + _make_wav(tmp_path / "audio.wav", 5) + response = whisper_invoke(_whisper_request(tmp_path)) + assert response.status == "failed" + assert "model load failed" in response.error + + +def test_whisper_missing_input(tmp_path) -> None: + """验证缺少 audio_uri 时返回失败。""" + response = whisper_invoke(_whisper_request(tmp_path, inputs={})) + assert response.status == "failed" + + +def test_whisper_missing_file(tmp_path) -> None: + """验证音频文件不存在时返回失败。""" + response = whisper_invoke(_whisper_request(tmp_path)) + assert response.status == "failed" + assert "audio file not found" in response.error + + +# --------------------------------------------------------------------------- +# LLM 节点 +# --------------------------------------------------------------------------- + + +class FakeUrlOpenResponse: + """模拟 urllib 响应对象，提供固定 LLM 译文内容。""" + + def __init__(self, content: str) -> None: + # 预编码为 Chat Completions 风格的 JSON 响应体。 + self._payload = json.dumps( + {"choices": [{"message": {"content": content}}]} + ).encode("utf-8") + + def read(self) -> bytes: + return self._payload + + def __enter__(self): + return self + + def __exit__(self, *args) -> bool: + return False + + +def _make_srt(tmp_path, count=5) -> Path: + """生成标准 SRT 测试文件，文本行为"原文字幕N"。""" + lines = [] + for index in range(count): + lines.extend( + [ + str(index + 1), + f"00:00:{index:02d},000 --> 00:00:{index + 1:02d},000", + f"原文字幕{index + 1}", + "", + ] + ) + path = tmp_path / "in.srt" + path.write_text("\n".join(lines), encoding="utf-8") + return path + + +def test_llm_translate_lines_via_fake_api(monkeypatch) -> None: + """验证通过真实 HTTP 服务器调用 LLM 接口并保持行顺序。""" + class Handler(BaseHTTPRequestHandler): + def do_POST(self) -> None: + length = int(self.headers.get("Content-Length", "0")) + self.rfile.read(length) + body = json.dumps( + { + "choices": [ + { + "message": { + "content": "译文一\n译文二\n译文三\n译文四\n译文五" + } + } + ] + } + ).encode("utf-8") + self.send_response(200) + self.send_header("Content-Type", "application/json") + self.send_header("Content-Length", str(len(body))) + self.end_headers() + self.wfile.write(body) + + def log_message(self, format, *args) -> None: + return + + server = ThreadingHTTPServer(("127.0.0.1", 0), Handler) + thread = threading.Thread(target=server.serve_forever, daemon=True) + thread.start() + try: + monkeypatch.setenv( + "LLM_API_BASE", + f"http://127.0.0.1:{server.server_address[1]}/v1/chat/completions", + ) + monkeypatch.setenv("LLM_API_KEY", "test-key") + result = translate_lines( + ["一", "二", "三", "四", "五"], + {"target_language": "zh-CN"}, + ) + assert result == ["译文一", "译文二", "译文三", "译文四", "译文五"] + finally: + server.shutdown() + server.server_close() + thread.join(timeout=5) + + +def test_llm_translate_lines_api_error(monkeypatch) -> None: + """验证 LLM 接口不可用时抛出 URLError。""" + def fail_open(request, timeout): + raise urllib.error.URLError("api down") + + monkeypatch.setattr("nodes.llm.urllib.request.urlopen", fail_open) + try: + translate_lines(["一"], {}) + raise AssertionError("expected failure") + except urllib.error.URLError: + pass + + +def test_llm_translate_lines_default_timeout(monkeypatch) -> None: + """验证未配置超时时使用默认 600 秒。""" + captured = {} + + def fake_open(request, timeout): + captured["timeout"] = timeout + return FakeUrlOpenResponse("译文一") + + monkeypatch.setattr("nodes.llm.urllib.request.urlopen", fake_open) + monkeypatch.setenv("LLM_API_BASE", "http://fake/v1/chat/completions") + + result = translate_lines(["一"], {}) + + assert result == ["译文一"] + assert captured["timeout"] == 600 + + +def test_llm_translate_lines_env_timeout(monkeypatch) -> None: + """验证 LLM_TIMEOUT_SECONDS 环境变量可覆盖超时。""" + captured = {} + + def fake_open(request, timeout): + captured["timeout"] = timeout + return FakeUrlOpenResponse("译文一") + + monkeypatch.setattr("nodes.llm.urllib.request.urlopen", fake_open) + monkeypatch.setenv("LLM_API_BASE", "http://fake/v1/chat/completions") + monkeypatch.setenv("LLM_TIMEOUT_SECONDS", "45") + + translate_lines(["一"], {}) + + assert captured["timeout"] == 45 + + +def test_llm_invoke_success(tmp_path, monkeypatch) -> None: + """验证成功调用会把译文回填到 SRT 并输出 cn.srt。""" + source = _make_srt(tmp_path) + + def fake_translate(lines, params): + return [f"译文{i + 1}" for i in range(len(lines))] + + monkeypatch.setattr("nodes.llm.translate_lines", fake_translate) + response = llm_invoke( + InvokeRequest( + run_id="run_1", + node_instance_id="ni_1", + inputs={"srt_uri": str(source)}, + params={"target_language": "zh-CN"}, + output_dir=str(tmp_path / "out"), + ) + ) + assert response.status == "completed" + content = Path(response.outputs["cn_srt_uri"]).read_text(encoding="utf-8") + assert "译文1" in content + + +def test_llm_invoke_pads_short_translation(tmp_path, monkeypatch) -> None: + """验证译文行数不足时用空行补齐，保持 SRT 结构完整。""" + source = _make_srt(tmp_path, count=3) + monkeypatch.setattr( + "nodes.llm.translate_lines", + lambda lines, params: ["only one"], + ) + response = llm_invoke( + InvokeRequest( + run_id="run_2", + node_instance_id="ni_2", + inputs={"srt_uri": str(source)}, + output_dir=str(tmp_path / "out2"), + ) + ) + assert response.status == "completed" + + +def test_llm_invoke_missing_input(tmp_path) -> None: + """验证缺少 srt_uri 时返回失败。""" + response = llm_invoke( + InvokeRequest( + run_id="run_3", + node_instance_id="ni_3", + inputs={}, + output_dir=str(tmp_path), + ) + ) + assert response.status == "failed" + + +def test_llm_invoke_missing_file(tmp_path) -> None: + """验证 SRT 文件不存在时返回失败。""" + response = llm_invoke( + InvokeRequest( + run_id="run_4", + node_instance_id="ni_4", + inputs={"srt_uri": str(tmp_path / "missing.srt")}, + output_dir=str(tmp_path), + ) + ) + assert response.status == "failed" + + +# --------------------------------------------------------------------------- +# ASS 节点 +# --------------------------------------------------------------------------- + + +SAMPLE_SRT = """ +1 +00:00:01,000 --> 00:00:03,000 +第一行 +第二行 + +2 +00:00:04,000 --> 00:00:06,000 +第三行 +""" + + +def test_ass_parse_and_write(tmp_path) -> None: + """验证多行字幕会被解析并通过左右眼样式写出。""" + entries = parse_srt(SAMPLE_SRT) + assert len(entries) == 2 + assert entries[0][2] == r"第一行\N第二行" + + output = tmp_path / "out.ass" + write_ass(entries, output, "3840x1920") + content = output.read_text(encoding="utf-8") + assert "PlayResX: 3840" in content + assert "PlayResY: 1920" in content + assert "LeftEye" in content + assert "RightEye" in content + assert r"第一行\N第二行" in content + + +def test_ass_parse_malformed(tmp_path) -> None: + """验证畸形 SRT 不会抛出异常且返回空条目或忽略坏行。""" + source = tmp_path / "bad.srt" + source.write_text("1\nnot a time line\n", encoding="utf-8") + assert parse_srt(source.read_text(encoding="utf-8")) == [] + + source.write_text("1", encoding="utf-8") + assert parse_srt(source.read_text(encoding="utf-8")) == [] + + +def test_ass_invoke_success(tmp_path) -> None: + """验证成功调用会按指定分辨率输出 ASS 产物。""" + source = tmp_path / "in.srt" + source.write_text(SAMPLE_SRT, encoding="utf-8") + response = ass_invoke( + InvokeRequest( + run_id="run_1", + node_instance_id="ni_1", + inputs={"cn_srt_uri": str(source)}, + params={"resolution": "1920x1080"}, + output_dir=str(tmp_path / "out"), + ) + ) + assert response.status == "completed" + assert "PlayResX: 1920" in Path(response.outputs["ass_uri"]).read_text(encoding="utf-8") + + +def test_ass_invoke_missing_input(tmp_path) -> None: + """验证缺少 cn_srt_uri 时返回失败。""" + response = ass_invoke( + InvokeRequest( + run_id="run_2", + node_instance_id="ni_2", + inputs={}, + output_dir=str(tmp_path), + ) + ) + assert response.status == "failed" + + +def test_ass_invoke_missing_file(tmp_path) -> None: + """验证 SRT 文件不存在时返回失败。""" + response = ass_invoke( + InvokeRequest( + run_id="run_3", + node_instance_id="ni_3", + inputs={"cn_srt_uri": str(tmp_path / "missing.srt")}, + output_dir=str(tmp_path), + ) + ) + assert response.status == "failed" + + +def test_resolve_bare_name_found(tmp_path) -> None: + """验证裸模型名会在候选目录父级下按名解析到本地模型。""" + named = tmp_path / "zh-ct2" + named.mkdir() + (named / "model.bin").write_bytes(b"x") + # 候选目录取父目录的兄弟布局：candidates 首项父级即模型根目录。 + candidates = [tmp_path / "models"] + resolved = resolve_model_path({"model_path": "zh-ct2"}, env={}, candidates=candidates) + assert resolved == str(named) + + +def test_resolve_bare_name_missing(tmp_path) -> None: + """验证裸模型名在本地不存在时原样返回，交由 faster-whisper 处理。""" + resolved = resolve_model_path( + {"model_path": "no-such-model"}, env={}, candidates=[tmp_path / "models"] + ) + assert resolved == "no-such-model" + + +def test_whisper_task_translate(tmp_path, monkeypatch) -> None: + """验证 task=translate 参数会传递给 faster-whisper 的 transcribe。""" + class TaskRecorderModel: + def __init__(self, *args, **kwargs): + self.transcribe_kwargs = None + + def transcribe(self, path, **kwargs): + self.transcribe_kwargs = kwargs + return ( + [FakeSegment(0, 1, "中文直出")], + None, + ) + + recorder = TaskRecorderModel() + monkeypatch.setitem(sys.modules, "faster_whisper", types.SimpleNamespace(WhisperModel=lambda *a, **k: recorder)) + _make_wav(tmp_path / "audio.wav", 5) + response = whisper_invoke( + _whisper_request(tmp_path, params={"language": "ja", "task": "translate"}) + ) + assert response.status == "completed" + assert recorder.transcribe_kwargs["task"] == "translate" + assert "中文直出" in Path(response.outputs["srt_uri"]).read_text(encoding="utf-8") + + +def test_whisper_condition_on_previous_text(tmp_path, monkeypatch) -> None: + """验证长音频参数 condition_on_previous_text 可配置并默认关闭。""" + captured = {} + + class CondRecorderModel: + def __init__(self, *args, **kwargs): + pass + + def transcribe(self, path, **kwargs): + captured["condition_on_previous_text"] = kwargs.get("condition_on_previous_text") + return ([FakeSegment(0, 1, "ok")], None) + + monkeypatch.setitem( + sys.modules, + "faster_whisper", + types.SimpleNamespace(WhisperModel=lambda *a, **k: CondRecorderModel()), + ) + _make_wav(tmp_path / "audio.wav", 5) + # 默认 False（长音频稳定）；显式传 True 可开启。 + whisper_invoke(_whisper_request(tmp_path)) + assert captured["condition_on_previous_text"] is False + whisper_invoke(_whisper_request(tmp_path, params={"condition_on_previous_text": True})) + assert captured["condition_on_previous_text"] is True + + +def test_split_audio_disabled_or_no_ffmpeg(tmp_path, monkeypatch) -> None: + """验证 chunk_seconds<=0 或缺少 ffmpeg 时回退整段，不调用切块。""" + from nodes.whisper import _split_audio + + audio = _make_wav(tmp_path / "in.wav", 5) + called = [] + + def fake_run(*args, **kwargs): + called.append(args) + return subprocess.CompletedProcess([], 0) + + monkeypatch.setattr("nodes.whisper.subprocess.run", fake_run) + # chunk_seconds<=0：直接返回整段，不执行 ffmpeg。 + assert _split_audio(audio, tmp_path, 0, "ffmpeg") == [audio] + # 缺少 ffmpeg：直接返回整段。 + assert _split_audio(audio, tmp_path, 600, None) == [audio] + assert called == [] + + +def test_split_audio_failure_fallback(tmp_path, monkeypatch) -> None: + """验证 ffmpeg 切块失败时回退整段单次转写。""" + from nodes.whisper import _split_audio + + audio = _make_wav(tmp_path / "in.wav", 5) + monkeypatch.setattr( + "nodes.whisper.subprocess.run", + lambda *a, **k: subprocess.CompletedProcess([], 1, stderr="boom"), + ) + assert _split_audio(audio, tmp_path, 600, "ffmpeg") == [audio] + + +def test_split_audio_success_and_empty(tmp_path, monkeypatch) -> None: + """验证切块成功返回块列表；产出为空时回退整段。""" + from nodes.whisper import _split_audio + + audio = _make_wav(tmp_path / "in.wav", 5) + + def fake_run_success(command, **kwargs): + # 模拟 ffmpeg 产出两个真实的块 WAV。 + pattern = command[-1] + for name in ("chunk_000.wav", "chunk_001.wav"): + _make_wav(tmp_path / "chunks" / name, 2) + return subprocess.CompletedProcess(command, 0) + + monkeypatch.setattr("nodes.whisper.subprocess.run", fake_run_success) + chunks = _split_audio(audio, tmp_path, 600, "ffmpeg") + assert len(chunks) == 2 + assert chunks[0].name == "chunk_000.wav" + + # 切块成功但没有产出文件时回退整段。 + monkeypatch.setattr( + "nodes.whisper.subprocess.run", + lambda *a, **k: subprocess.CompletedProcess([], 0), + ) + assert _split_audio(audio, tmp_path / "other", 600, "ffmpeg") == [audio] + + +def test_whisper_chunked_transcription_merges_offsets(tmp_path, monkeypatch) -> None: + """验证分块转写用真实 WAV 块，第二块时间轴按实际时长偏移合并到同一 SRT。""" + from nodes.whisper import _split_audio + + chunk_dir = tmp_path / "out" / "chunks" + chunk_dir.mkdir(parents=True) + # 真实 WAV 块（各 60s），偏移按实际时长累积为 60s。 + chunk1 = _make_wav(chunk_dir / "chunk_000.wav", 60) + chunk2 = _make_wav(chunk_dir / "chunk_001.wav", 60) + monkeypatch.setattr("nodes.whisper._split_audio", lambda a, o, c, f: [chunk1, chunk2]) + FakeWhisperModel.instances.clear() + _install_fake_whisper(monkeypatch) + _make_wav(tmp_path / "audio.wav", 5) + response = whisper_invoke(_whisper_request(tmp_path, params={"chunk_seconds": 60})) + assert response.status == "completed" + content = Path(response.outputs["srt_uri"]).read_text(encoding="utf-8") + # 序号从 1 开始连续递增；第一块无偏移，第二块偏移 60 秒。 + assert content.startswith("1\n") + assert "3\n00:01:00,000" in content + assert "00:00:00,000 --> 00:00:01,000" in content + assert "01:00:00,500 --> 01:00:02,250" in content + assert "00:01:00,000 --> 00:01:01,000" in content + assert "01:01:00,500 --> 01:01:02,250" in content + + +def test_whisper_chunk_disabled_single_call(tmp_path, monkeypatch) -> None: + """验证 chunk_seconds=0 时单次调用、不切块。""" + FakeWhisperModel.instances.clear() + _install_fake_whisper(monkeypatch) + _make_wav(tmp_path / "audio.wav", 5) + response = whisper_invoke(_whisper_request(tmp_path, params={"chunk_seconds": 0})) + assert response.status == "completed" + # 单次调用：init 只记录一次实例。 + assert len(FakeWhisperModel.instances) == 1 + + +def test_whisper_vad_filter_default_off(tmp_path, monkeypatch) -> None: + """验证 vad_filter 默认开启，可显式关闭。""" + captured = {} + + class VadRecorderModel: + def __init__(self, *args, **kwargs): + pass + + def transcribe(self, path, **kwargs): + captured["vad_filter"] = kwargs.get("vad_filter") + return ([FakeSegment(0, 1, "ok")], None) + + monkeypatch.setitem( + sys.modules, + "faster_whisper", + types.SimpleNamespace(WhisperModel=lambda *a, **k: VadRecorderModel()), + ) + _make_wav(tmp_path / "audio.wav", 5) + whisper_invoke(_whisper_request(tmp_path)) + assert captured["vad_filter"] is True + whisper_invoke(_whisper_request(tmp_path, params={"vad_filter": False})) + assert captured["vad_filter"] is False + + +def _make_wav(path, seconds, rate=16000) -> Path: + """生成指定时长的 16kHz 单声道 16bit 静音 WAV。""" + import wave + + with wave.open(str(path), "wb") as wav: + wav.setnchannels(1) + wav.setsampwidth(2) + wav.setframerate(rate) + wav.writeframes(b"\x00\x00" * int(rate * seconds)) + return path + + +def test_whisper_chunk_offset_uses_actual_duration(tmp_path, monkeypatch) -> None: + """验证分块偏移按 WAV 实际时长累积（1s+2s 块 → 第二块偏移 1s 而非块长 60s）。""" + chunk_dir = tmp_path / "out" / "chunks" + chunk_dir.mkdir(parents=True) + chunk1 = _make_wav(chunk_dir / "chunk_000.wav", 1) + chunk2 = _make_wav(chunk_dir / "chunk_001.wav", 2) + monkeypatch.setattr("nodes.whisper._split_audio", lambda a, o, c, f: [chunk1, chunk2]) + FakeWhisperModel.instances.clear() + _install_fake_whisper(monkeypatch) + _make_wav(tmp_path / "audio.wav", 5) + response = whisper_invoke(_whisper_request(tmp_path, params={"chunk_seconds": 60})) + assert response.status == "completed" + content = Path(response.outputs["srt_uri"]).read_text(encoding="utf-8") + # 第二块偏移 = 第一块实际时长 1s（若用块长假设则会是 60s → 00:01:00）。 + assert "00:00:01,000 --> 00:00:02,000" in content + assert "00:01:00,000 --> 00:01:01,000" not in content + + +def test_whisper_segment_logs_full_video_time(caplog, tmp_path, monkeypatch) -> None: + """验证每条分段日志包含编号与完整视频角度（含分块偏移）的时间范围。""" + chunk_dir = tmp_path / "out" / "chunks" + chunk_dir.mkdir(parents=True) + chunk1 = _make_wav(chunk_dir / "chunk_000.wav", 60) + chunk2 = _make_wav(chunk_dir / "chunk_001.wav", 60) + monkeypatch.setattr("nodes.whisper._split_audio", lambda a, o, c, f: [chunk1, chunk2]) + FakeWhisperModel.instances.clear() + _install_fake_whisper(monkeypatch) + _make_wav(tmp_path / "audio.wav", 5) + with caplog.at_level("INFO", logger="vrsub.whisper"): + response = whisper_invoke(_whisper_request(tmp_path, params={"chunk_seconds": 60})) + assert response.status == "completed" + seg_logs = [r.message for r in caplog.records if r.message.startswith("分段 #")] + # 第一块：0s 起；第二块：偏移 60s（第一块实际时长）。 + assert any("分段 #1: 00:00:00,000 --> 00:00:01,000" in m for m in seg_logs) + assert any(m.startswith("分段 #3: 00:01:00,000") for m in seg_logs) + + +def test_wav_duration_fallback_on_invalid_file(tmp_path) -> None: + """验证读取时长时，损坏/缺失文件回退 fallback 值（真实非法文件，非占位字节）。""" + from nodes.whisper import _wav_duration_seconds + + bad = tmp_path / "bad.wav" + bad.write_text("this is not a wav file", encoding="utf-8") + assert _wav_duration_seconds(bad, 60.0) == 60.0 + missing = tmp_path / "missing.wav" + assert _wav_duration_seconds(missing, 60.0) == 60.0 + + +# --------------------------------------------------------------------------- +# VLM OCR 节点（直接请求 Ollama /api/generate，流式） +# --------------------------------------------------------------------------- + + +# 真实的最小 PNG（1x1 像素，合法文件），用于构造真实图片输入。 +_MIN_PNG_BASE64 = ( + "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8" + "z8BQDwAEhQGAhKmMIQAAAABJRU5ErkJggg==" +) + + +class FakeChatResponse: + """模拟 Ollama /api/generate 流式响应：readline() 逐行返回 JSON 块。 + + done=False（默认）时仅一行内容，随后 readline 返回空表示流结束； + done=True 时末尾追加一行带 done 标记的结束块。 + """ + + def __init__(self, content: str, done: bool = False) -> None: + self._lines = [ + json.dumps({"message": {"role": "assistant", "content": content}}).encode() + ] + if done: + self._lines.append( + json.dumps({"message": {"role": "assistant", "content": ""}, "done": True}).encode() + ) + + def readline(self) -> bytes: + return self._lines.pop(0) if self._lines else b"" + + def __enter__(self): + return self + + def __exit__(self, *args) -> bool: + return False + + +def _real_png(tmp_path) -> Path: + """生成真实 PNG 图片文件（解码自合法 base64）。""" + image = tmp_path / "frame.png" + image.write_bytes(base64.b64decode(_MIN_PNG_BASE64)) + return image + + +def test_vlm_success(tmp_path, monkeypatch) -> None: + """验证真实图片经流式读取后输出清洗过的文字与 ocr.txt 产物。""" + from nodes.vlm import invoke as vlm_invoke + + image = _real_png(tmp_path) + captured = {} + + def fake_urlopen(request, timeout): + captured["timeout"] = timeout + captured["url"] = request.full_url + return FakeChatResponse("HELLO WORLD 123\n```markdown\n```\n```\n") + + monkeypatch.setattr("nodes.vlm.urllib.request.urlopen", fake_urlopen) + response = vlm_invoke( + InvokeRequest( + run_id="run_1", + node_instance_id="", + inputs={"image_uri": str(image)}, + params={"model": "glm-ocr:latest"}, + output_dir=str(tmp_path / "out"), + ) + ) + assert response.status == "completed", response.error + assert response.outputs["text"] == "HELLO WORLD 123" + assert captured["url"].endswith("/api/chat") + assert captured["timeout"] == 5 + content = Path(response.outputs["text_uri"]).read_text(encoding="utf-8") + assert "HELLO WORLD 123" in content + + +def test_vlm_missing_input_and_file(tmp_path) -> None: + """验证缺少 image_uri 或图片不存在时返回失败。""" + from nodes.vlm import invoke as vlm_invoke + + assert vlm_invoke( + InvokeRequest(run_id="r", node_instance_id="", inputs={}, output_dir=str(tmp_path)) + ).status == "failed" + assert vlm_invoke( + InvokeRequest( + run_id="r", node_instance_id="", + inputs={"image_uri": str(tmp_path / "missing.png")}, + output_dir=str(tmp_path), + ) + ).status == "failed" + + +def test_vlm_network_error(tmp_path, monkeypatch) -> None: + """验证 Ollama 服务不可达时返回 failed。""" + from nodes.vlm import invoke as vlm_invoke + + image = _real_png(tmp_path) + + def fail_open(request, timeout): + raise urllib.error.URLError("ollama down") + + monkeypatch.setattr("nodes.vlm.urllib.request.urlopen", fail_open) + response = vlm_invoke( + InvokeRequest( + run_id="r", node_instance_id="", + inputs={"image_uri": str(image)}, + output_dir=str(tmp_path), + ) + ) + assert response.status == "failed" + assert "ollama down" in response.error + + +def test_vlm_response_format_error(tmp_path, monkeypatch) -> None: + """验证流式块既无 response 也无 done 标记时（格式错误）返回 failed。""" + from nodes.vlm import invoke as vlm_invoke + + image = _real_png(tmp_path) + + class BadResponse: + def readline(self) -> bytes: + return b'{"foo": 1}' + + def __enter__(self): + return self + + def __exit__(self, *args) -> bool: + return False + + monkeypatch.setattr("nodes.vlm.urllib.request.urlopen", lambda *a, **k: BadResponse()) + response = vlm_invoke( + InvokeRequest( + run_id="r", node_instance_id="", + inputs={"image_uri": str(image)}, + output_dir=str(tmp_path), + ) + ) + assert response.status == "failed" + + +def test_vlm_clean_ocr_text() -> None: + """验证清洗逻辑会剔除 markdown 围栏与空行。""" + from nodes.vlm import _clean_ocr_text + + cleaned = _clean_ocr_text("第一行\n```markdown\n```\n\n第二行\n```") + assert cleaned == "第一行\n第二行" + + +def test_vlm_options_in_body(tmp_path, monkeypatch) -> None: + """验证请求体携带采样选项（temperature=0/repeat_penalty/num_predict），可参数覆盖。""" + from nodes.vlm import invoke as vlm_invoke + + image = _real_png(tmp_path) + captured = {} + + def fake_urlopen(request, timeout): + import json as _json + + captured["body"] = _json.loads(request.data.decode("utf-8")) + return FakeChatResponse("SUB 001") + + monkeypatch.setattr("nodes.vlm.urllib.request.urlopen", fake_urlopen) + vlm_invoke( + InvokeRequest( + run_id="r", node_instance_id="", + inputs={"image_uri": str(image)}, + params={"repeat_penalty": 1.3, "num_predict": 128}, + output_dir=str(tmp_path), + ) + ) + assert captured["body"]["options"] == { + "temperature": 0.3, + "repeat_penalty": 1.3, + "num_predict": 128, + } + + +def test_vlm_call_structure_system_prompt_and_stop(tmp_path, monkeypatch) -> None: + """验证 /api/chat 调用结构：system 承载指令、user 只携带图片、stop=。""" + from nodes.vlm import invoke as vlm_invoke + + image = _real_png(tmp_path) + captured = {} + + def fake_urlopen(request, timeout): + import json as _json + + captured["body"] = _json.loads(request.data.decode("utf-8")) + return FakeChatResponse("SUB 001") + + monkeypatch.setattr("nodes.vlm.urllib.request.urlopen", fake_urlopen) + vlm_invoke( + InvokeRequest( + run_id="r", node_instance_id="", + inputs={"image_uri": str(image)}, + params={"prompt": "识别图片中的所有文字，原样输出。"}, + output_dir=str(tmp_path), + ) + ) + body = captured["body"] + assert body["stop"] == ["\n", "\n答", "答"] + assert body["stream"] is True + assert body["messages"][0]["role"] == "system" + assert "识别图片" in body["messages"][0]["content"] + assert body["messages"][1]["role"] == "user" + assert body["messages"][1]["content"] == "" + assert len(body["messages"][1]["images"]) == 1 +def test_vlm_stream_stop_sequence_truncation(tmp_path, monkeypatch) -> None: + """验证流式读取命中终止序列（换行+答，\n答）即停止并截断该标记。""" + from nodes.vlm import invoke as vlm_invoke + + image = _real_png(tmp_path) + + def fake_urlopen(request, timeout): + # 模型输出先给出真实文本，随后进入“答：”式重复循环并输出终止序列。 + return FakeChatResponse("SUB 001\n答：重复循环垃圾") + + monkeypatch.setattr("nodes.vlm.urllib.request.urlopen", fake_urlopen) + response = vlm_invoke( + InvokeRequest( + run_id="r", node_instance_id="", + inputs={"image_uri": str(image)}, + output_dir=str(tmp_path), + ) + ) + assert response.status == "completed", response.error + # 终止序列及其后内容必须被截掉，只保留终止前的真实识别文本。 + assert response.outputs["text"] == "SUB 001" + + +def test_vlm_stream_done_with_message(tmp_path, monkeypatch) -> None: + """验证带 done 标记的流式结束块触发停止，结果正常返回。""" + from nodes.vlm import invoke as vlm_invoke + + image = _real_png(tmp_path) + + def fake_urlopen(request, timeout): + return FakeChatResponse("SUB 001", done=True) + + monkeypatch.setattr("nodes.vlm.urllib.request.urlopen", fake_urlopen) + response = vlm_invoke( + InvokeRequest( + run_id="r", node_instance_id="", + inputs={"image_uri": str(image)}, + output_dir=str(tmp_path), + ) + ) + assert response.status == "completed", response.error + assert response.outputs["text"] == "SUB 001" + + +def test_vlm_stream_done_only_end(tmp_path, monkeypatch) -> None: + """验证仅含 done 标记（无 response）的行视为流结束，空结果正常完成。""" + from nodes.vlm import invoke as vlm_invoke + + image = _real_png(tmp_path) + + class DoneOnlyResponse: + def readline(self) -> bytes: + if not self._consumed: + self._consumed = True + return b'{"done": true}' + return b"" + + def __enter__(self): + self._consumed = False + return self + + def __exit__(self, *args) -> bool: + return False + + monkeypatch.setattr("nodes.vlm.urllib.request.urlopen", lambda *a, **k: DoneOnlyResponse()) + response = vlm_invoke( + InvokeRequest( + run_id="r", node_instance_id="", + inputs={"image_uri": str(image)}, + output_dir=str(tmp_path), + ) + ) + assert response.status == "completed", response.error + assert response.outputs["text"] == "" + + +def test_vlm_stream_deadline_timeout(tmp_path, monkeypatch) -> None: + """验证整体 5 秒截止：流式读取超过 deadline 立即终止并返回 failed。""" + from nodes.vlm import invoke as vlm_invoke + + image = _real_png(tmp_path) + # 第一次调用计算 deadline（100+5=105），第二次调用已越过截止（200>=105）。 + monotonic_values = iter([100.0, 200.0]) + monkeypatch.setattr("nodes.vlm.time.monotonic", lambda: next(monotonic_values)) + + def fake_urlopen(request, timeout): + return FakeChatResponse("SUB 001") + + monkeypatch.setattr("nodes.vlm.urllib.request.urlopen", fake_urlopen) + response = vlm_invoke( + InvokeRequest( + run_id="r", node_instance_id="", + inputs={"image_uri": str(image)}, + params={"timeout_seconds": 5}, + output_dir=str(tmp_path), + ) + ) + assert response.status == "failed" + assert "timed out" in response.error + + +def test_vlm_extract_gettext() -> None: + """验证从模型输出中提取 标签内容的各种形态。""" + from nodes.vlm import _extract_gettext + + # 正常：标签包裹的内容被提取。 + assert _extract_gettext("还有没有什么困扰 或者奇怪的地方吗") == ( + "还有没有什么困扰 或者奇怪的地方吗" + ) + # 空标签：返回空字符串。 + assert _extract_gettext("前缀后缀") == "" + # 多个标签（重复循环）：只取第一个。 + assert _extract_gettext("SUB 001SUB 001") == "SUB 001" + # 跨行内容：DOTALL 让 . 匹配换行。 + assert _extract_gettext("第一行\n第二行") == "第一行\n第二行" + # 未按格式输出（无标签）：回退原始文本，保持旧行为。 + assert _extract_gettext("没有标签的裸文本") == "没有标签的裸文本" + + +def test_vlm_gettext_in_invoke(tmp_path, monkeypatch) -> None: + """验证整条调用链：模型返回 包裹内容时，产物只含标签内文本。""" + from nodes.vlm import invoke as vlm_invoke + + image = _real_png(tmp_path) + + def fake_urlopen(request, timeout): + return FakeChatResponse("SUB 001 围栏垃圾```") + + monkeypatch.setattr("nodes.vlm.urllib.request.urlopen", fake_urlopen) + response = vlm_invoke( + InvokeRequest( + run_id="r", node_instance_id="", + inputs={"image_uri": str(image)}, + output_dir=str(tmp_path), + ) + ) + assert response.status == "completed", response.error + assert response.outputs["text"] == "SUB 001" + +def test_vlm_truncate_at_stop() -> None: + """验证多个终止序列的截断：取最先命中位置，未命中原样返回。""" + from nodes.vlm import _truncate_at_stop + + # 命中 "\n答"（位置更靠前）。 + assert _truncate_at_stop("SUB 001\n答：重复") == "SUB 001" + # 未命中 "\n答" 但命中单个 "答"。 + assert _truncate_at_stop("SUB 001 答") == "SUB 001 " + # 多个序列均命中：取最早出现的位置（"答" 在 "答：" 之前）。 + assert _truncate_at_stop("SUB 001答\n答：循环") == "SUB 001" + # 未命中任何序列：原样返回。 + assert _truncate_at_stop("还有没有什么困扰 或者奇怪的地方吗") == ( + "还有没有什么困扰 或者奇怪的地方吗" + ) diff --git a/tests/test_ocr_flow.py b/tests/test_ocr_flow.py new file mode 100644 index 0000000..b8b0bbb --- /dev/null +++ b/tests/test_ocr_flow.py @@ -0,0 +1,401 @@ +"""抽帧与字幕 OCR 节点单元测试。 + +frame-extract 用 testdata 真实视频抽帧+裁切+720p 压缩；subtitle-ocr 的 OCR +网络调用（vlm-ocr）按 I/O 边界 mock，但喂给它的帧图片是真实的 testdata 资产。 +应用层不再加工模型输出文本，只做长度上限校验（超长报错跳过）。 +""" + +import json +from pathlib import Path + +from wov_sdk.models import InvokeRequest, InvokeResponse + +from nodes.frame_extract import invoke as frame_invoke +from nodes.subtitle_ocr import _assemble_srt +from nodes.subtitle_ocr import invoke as ocr_invoke + +# 单体根目录：tests/ 的上一级。 +WORKSPACE = Path(__file__).resolve().parent.parent +TESTDATA = WORKSPACE / "testdata" +# 10s 测试视频：SUB 001 在 1-4s、SUB 002 在 6-9s。 +VIDEO = TESTDATA / "subtitle_10s.mp4" +TEXT_IMG = TESTDATA / "ocr_text.png" + + +def _png_size(path: Path) -> tuple[int, int]: + """从 PNG 头读取宽高（真实图片尺寸断言）。""" + data = path.read_bytes() + assert data[:8] == b"\x89PNG\r\n\x1a\n", "not a real png" + width = int.from_bytes(data[16:20], "big") + height = int.from_bytes(data[20:24], "big") + return width, height + + +def _frame_request(tmp_path, video=VIDEO, **params) -> InvokeRequest: + """构造 frame-extract 调用请求。""" + return InvokeRequest( + run_id="run_fx", + node_instance_id="", + inputs={"video_uri": str(video)}, + params=params, + output_dir=str(tmp_path / "out"), + ) + + +# --------------------------------------------------------------------------- +# frame-extract：抽帧 + 裁切 + 720p 压缩 +# --------------------------------------------------------------------------- + + +def test_frame_extract_crop_and_manifest(tmp_path) -> None: + """真实视频抽帧：裁切下半 50% 后帧尺寸为 1280x360，清单时间轴正确。""" + response = frame_invoke( + _frame_request(tmp_path, interval_seconds=1, crop=[0, 0.5, 1, 0.5]) + ) + assert response.status == "completed", response.error + manifest = json.loads(Path(response.outputs["frames_manifest"]).read_text(encoding="utf-8")) + assert len(manifest) >= 9 + assert [round(item["time"], 3) for item in manifest] == [ + round(i * 1.0, 3) for i in range(len(manifest)) + ] + first = Path(manifest[0]["image_uri"]) + assert first.is_file() + # 1280x360 已在 720p 内，压缩不改变尺寸。 + assert _png_size(first) == (1280, 360) + + +def test_frame_extract_default_params(tmp_path) -> None: + """未指定参数时使用默认值：抽帧间隔 0.5 秒 + 默认底部裁切区域。""" + response = frame_invoke(_frame_request(tmp_path)) + assert response.status == "completed", response.error + manifest = json.loads(Path(response.outputs["frames_manifest"]).read_text(encoding="utf-8")) + assert manifest + # 默认间隔 0.5s：25fps 下 step=round(12.5)=12（银行家舍入）， + # 帧时间按 step/fps=12/25=0.48s 步进（帧号精确，采样周期由帧量化决定）。 + assert [round(item["time"], 3) for item in manifest] == [ + round(i * 12 / 25, 3) for i in range(len(manifest)) + ] + +def test_frame_extract_missing_video(tmp_path) -> None: + """缺少 video_uri 时返回失败。""" + response = frame_invoke( + InvokeRequest( + run_id="r", node_instance_id="", inputs={}, output_dir=str(tmp_path) + ) + ) + assert response.status == "failed" + + +def test_frame_extract_bad_crop(tmp_path) -> None: + """crop 比例越界（超出画面）时返回失败。""" + assert frame_invoke(_frame_request(tmp_path, crop=[0, 0.5, 1, 1.5])).status == "failed" + assert frame_invoke(_frame_request(tmp_path, crop=[-0.1, 0, 1, 0.5])).status == "failed" + assert frame_invoke(_frame_request(tmp_path, crop="abc")).status == "failed" + assert frame_invoke(_frame_request(tmp_path, crop=[0, 0.5, 1])).status == "failed" + # 各值域合法但 x+w 越出画面。 + assert frame_invoke(_frame_request(tmp_path, crop=[0.6, 0, 0.5, 0.3])).status == "failed" + + +def test_frame_extract_video_missing_file(tmp_path) -> None: + """video_uri 指向不存在的文件时返回失败。""" + response = frame_invoke(_frame_request(tmp_path, video=tmp_path / "none.mp4")) + assert response.status == "failed" + assert "not found" in response.error + + +def test_frame_extract_bad_interval(tmp_path) -> None: + """间隔 <= 0 时返回失败。""" + response = frame_invoke(_frame_request(tmp_path, interval_seconds=0)) + assert response.status == "failed" + + +def test_frame_extract_ffmpeg_fails(monkeypatch, tmp_path) -> None: + """ffmpeg 抽帧失败时透传错误。""" + import subprocess as sp + + monkeypatch.setattr("nodes.frame_extract._video_size", lambda *a, **k: (1280, 720)) + monkeypatch.setattr("nodes.frame_extract._video_fps", lambda *a, **k: 25.0) + monkeypatch.setattr("nodes.frame_extract._video_duration", lambda *a, **k: 10.0) + monkeypatch.setattr( + "nodes.frame_extract.subprocess.run", + lambda *a, **k: sp.CompletedProcess([], 1, stderr="boom"), + ) + response = frame_invoke(_frame_request(tmp_path)) + assert response.status == "failed" + assert "boom" in response.error + + +def test_video_size_unreadable(monkeypatch) -> None: + """ffmpeg -i 输出不含视频流信息时返回 None。""" + import subprocess as sp + + from nodes.frame_extract import _video_size + + monkeypatch.setattr( + "nodes.frame_extract.subprocess.run", + lambda *a, **k: sp.CompletedProcess([], 0, stderr="no video stream"), + ) + assert _video_size(Path("/tmp/x.mp4"), "ffmpeg") is None + + +def test_frame_extract_video_size_unknown(monkeypatch, tmp_path) -> None: + """无法读取视频分辨率时返回失败。""" + monkeypatch.setattr("nodes.frame_extract._video_size", lambda *a, **k: None) + response = frame_invoke(_frame_request(tmp_path)) + assert response.status == "failed" + assert "video size" in response.error + + +def test_video_duration_unreadable(monkeypatch) -> None: + """ffmpeg -i 输出缺少 Duration 时返回 None。""" + import subprocess as sp + + from nodes.frame_extract import _video_duration + + monkeypatch.setattr( + "nodes.frame_extract.subprocess.run", + lambda *a, **k: sp.CompletedProcess([], 0, stderr="no duration info"), + ) + assert _video_duration(Path("/tmp/x.mp4"), "ffmpeg") is None + + +def test_frame_extract_duration_unknown(monkeypatch, tmp_path) -> None: + """无法读取视频时长时返回失败。""" + monkeypatch.setattr("nodes.frame_extract._video_size", lambda *a, **k: (1280, 720)) + monkeypatch.setattr("nodes.frame_extract._video_duration", lambda *a, **k: None) + response = frame_invoke(_frame_request(tmp_path)) + assert response.status == "failed" + assert "duration" in response.error + + +def test_frame_extract_fps_unknown(monkeypatch, tmp_path) -> None: + """无法读取视频帧率时返回失败。""" + monkeypatch.setattr("nodes.frame_extract._video_size", lambda *a, **k: (1280, 720)) + monkeypatch.setattr("nodes.frame_extract._video_duration", lambda *a, **k: 10.0) + monkeypatch.setattr("nodes.frame_extract._video_fps", lambda *a, **k: None) + response = frame_invoke(_frame_request(tmp_path)) + assert response.status == "failed" + assert "fps" in response.error + +def test_frame_step_conversion() -> None: + """帧间隔换算：step=round(间隔秒×fps)，至少为 1。""" + from nodes.frame_extract import _frame_step + + assert _frame_step(fps=25.0, interval=0.2) == 5 + assert _frame_step(fps=25.0, interval=1.0) == 25 + assert _frame_step(fps=29.97, interval=1.0) == 30 + # fps 很低时 step 也不会小于 1（每帧都取）。 + assert _frame_step(fps=1.0, interval=0.2) == 1 + + +def test_video_fps_parse(monkeypatch) -> None: + """帧率解析：支持小数（29.97）与有理数（30000/1001）。""" + import subprocess as sp + + from nodes.frame_extract import _video_fps + + monkeypatch.setattr( + "nodes.frame_extract.subprocess.run", + lambda *a, **k: sp.CompletedProcess( + [], 0, stderr="Stream #0:0: Video: h264, 1280x720, 30000/1001 fps, 30000/1001 tbr" + ), + ) + assert _video_fps(Path("/tmp/x.mp4"), "ffmpeg") == 30000 / 1001 + monkeypatch.setattr( + "nodes.frame_extract.subprocess.run", + lambda *a, **k: sp.CompletedProcess( + [], 0, stderr="Stream #0:0: Video: h264, 1280x720, 25 fps, 25 tbr" + ), + ) + assert _video_fps(Path("/tmp/x.mp4"), "ffmpeg") == 25.0 + monkeypatch.setattr( + "nodes.frame_extract.subprocess.run", + lambda *a, **k: sp.CompletedProcess([], 0, stderr="no video stream"), + ) + assert _video_fps(Path("/tmp/x.mp4"), "ffmpeg") is None + +def test_frame_extract_one_second_exact_frames(tmp_path) -> None: + """真实视频 1s 间隔按秒 seek 精确抽帧：10s 视频应得 10 帧，时间 0..9。""" + response = frame_invoke( + _frame_request(tmp_path, interval_seconds=1, crop=[0, 0.7, 1, 0.3]) + ) + assert response.status == "completed", response.error + manifest = json.loads(Path(response.outputs["frames_manifest"]).read_text(encoding="utf-8")) + assert [round(item["time"], 3) for item in manifest] == [ + round(i * 1.0, 3) for i in range(len(manifest)) + ] + assert len(manifest) == 10 + + +# --------------------------------------------------------------------------- +# subtitle-ocr：OCR 循环 + 长度上限 + 合并 + SRT 组装 +# --------------------------------------------------------------------------- + + +def _frames_manifest(tmp_path, frame_specs) -> Path: + """构造真实 frames.json；frame_specs=[(time, image_path), ...]。""" + items = [{"time": time, "image_uri": str(image)} for time, image in frame_specs] + path = tmp_path / "frames.json" + path.write_text(json.dumps(items), encoding="utf-8") + return path + + +def test_assemble_srt_real_timeline() -> None: + """SRT 组装：起始=帧时间，结束=最后可见帧时间+采样间隔。""" + lines = _assemble_srt([(0.0, 6.0, "A"), (6.0, 8.0, "B")], interval_seconds=2.0) + text = "\n".join(lines) + assert text.startswith("1\n") + # A 最后可见帧 6.0 + 间隔 2.0 = 8.0（而非下一条字幕的出现时间）。 + assert "00:00:00,000 --> 00:00:08,000" in text + assert "00:00:06,000 --> 00:00:10,000" in text + + +def test_sampling_interval_from_manifest() -> None: + """采样间隔从帧清单时间轴推导：均匀间隔取相邻差，退化清单回退默认值。""" + from nodes.subtitle_ocr import _sampling_interval + + manifest = [{"time": i * 0.2, "image_uri": f"f{i}.png"} for i in range(10)] + assert _sampling_interval(manifest, 2.0) == 0.2 + # 单帧（无法算差）与异常时间序：回退默认值。 + assert _sampling_interval([{"time": 0.0, "image_uri": "f0.png"}], 2.0) == 2.0 + assert _sampling_interval( + [{"time": 0.0}, {"time": 0.0}, {"time": 0.2}], 2.0 + ) == 0.2 + +def test_ocr_merges_consecutive_same_text(monkeypatch, tmp_path) -> None: + """连续帧相同字幕合并为一条；消失时间=最后可见帧+间隔，空白段保留。""" + # SUB 001 在 0/2s，4s 为空帧，SUB 002 在 6/8s。 + frame_texts = [ + (0.0, "SUB 001"), (2.0, "SUB 001"), (4.0, ""), + (6.0, "SUB 002"), (8.0, "SUB 002"), + ] + frames = [] + for index, (time, _text) in enumerate(frame_texts): + image = tmp_path / f"f{index}.png" + image.write_bytes(TEXT_IMG.read_bytes()) + frames.append((time, image)) + mapping = {str(image): text for (time, image), (_, text) in zip(frames, frame_texts)} + + def fake_vlm(node_id, request): + return InvokeResponse(status="completed", outputs={"text": mapping[request.inputs["image_uri"]]}) + + monkeypatch.setattr("wov_app.registry.invoke", fake_vlm) + manifest = _frames_manifest(tmp_path, frames) + response = ocr_invoke( + InvokeRequest( + run_id="run_ocr", + node_instance_id="", + inputs={"frames_manifest": str(manifest)}, + params={}, + output_dir=str(tmp_path / "out"), + ) + ) + assert response.status == "completed", response.error + srt = Path(response.outputs["srt_uri"]).read_text(encoding="utf-8") + assert srt.count("SUB 001") == 1 + assert srt.count("SUB 002") == 1 + # SUB 001 最后可见帧 2.0 + 间隔 2.0 = 4.0 消失（而非拖到 SUB 002 出现）。 + assert "00:00:00,000 --> 00:00:04,000" in srt + assert "00:00:06,000 --> 00:00:10,000" in srt + + +def test_ocr_skips_failed_frames(monkeypatch, tmp_path) -> None: + """个别帧 OCR 失败时跳过，不影响其余帧汇总。""" + frames = [] + for index in range(3): + image = tmp_path / f"f{index}.png" + image.write_bytes(TEXT_IMG.read_bytes()) + frames.append((index * 2.0, image)) + + def fake_vlm(node_id, request): + if "f1" in request.inputs["image_uri"]: + return InvokeResponse(status="failed", error="boom") + return InvokeResponse(status="completed", outputs={"text": "SUB 001"}) + + monkeypatch.setattr("wov_app.registry.invoke", fake_vlm) + manifest = _frames_manifest(tmp_path, frames) + response = ocr_invoke( + InvokeRequest( + run_id="run_ocr", + node_instance_id="", + inputs={"frames_manifest": str(manifest)}, + params={}, + output_dir=str(tmp_path / "out"), + ) + ) + assert response.status == "completed", response.error + srt = Path(response.outputs["srt_uri"]).read_text(encoding="utf-8") + assert "SUB 001" in srt + + +def test_ocr_missing_manifest(tmp_path) -> None: + """缺少 frames_manifest 或清单文件不存在时返回失败。""" + response = ocr_invoke( + InvokeRequest(run_id="r", node_instance_id="", inputs={}, output_dir=str(tmp_path)) + ) + assert response.status == "failed" + response = ocr_invoke( + InvokeRequest( + run_id="r", node_instance_id="", + inputs={"frames_manifest": str(tmp_path / "none.json")}, + output_dir=str(tmp_path), + ) + ) + assert response.status == "failed" + + +def test_ocr_skips_oversized_output(monkeypatch, tmp_path) -> None: + """超长输出（模型重复循环等）直接报错跳过该帧，不进入 SRT。""" + frames = [] + for index in range(3): + image = tmp_path / f"o{index}.png" + image.write_bytes(TEXT_IMG.read_bytes()) + frames.append((index * 2.0, image)) + + def fake_vlm(node_id, request): + if "o0" in request.inputs["image_uri"]: + return InvokeResponse(status="completed", outputs={"text": "重复字幕\n" * 50}) + if "o1" in request.inputs["image_uri"]: + # 空输出帧跳过。 + return InvokeResponse(status="completed", outputs={"text": ""}) + return InvokeResponse(status="completed", outputs={"text": "SUB 001"}) + + monkeypatch.setattr("wov_app.registry.invoke", fake_vlm) + manifest = _frames_manifest(tmp_path, frames) + response = ocr_invoke( + InvokeRequest( + run_id="run_ocr", + node_instance_id="", + inputs={"frames_manifest": str(manifest)}, + params={"max_result_chars": 200}, + output_dir=str(tmp_path / "out"), + ) + ) + assert response.status == "completed", response.error + srt = Path(response.outputs["srt_uri"]).read_text(encoding="utf-8") + assert "SUB 001" in srt + assert "重复字幕" not in srt + + +def test_ocr_passes_short_text_through(monkeypatch, tmp_path) -> None: + """不超过上限的模型输出原样进入 SRT（不再做应用层过滤）。""" + image = tmp_path / "s0.png" + image.write_bytes(TEXT_IMG.read_bytes()) + frames = [(0.0, image)] + + def fake_vlm(node_id, request): + return InvokeResponse(status="completed", outputs={"text": " SUB 001 "}) + + monkeypatch.setattr("wov_app.registry.invoke", fake_vlm) + manifest = _frames_manifest(tmp_path, frames) + response = ocr_invoke( + InvokeRequest( + run_id="run_ocr", node_instance_id="", + inputs={"frames_manifest": str(manifest)}, + params={}, + output_dir=str(tmp_path / "out"), + ) + ) + assert response.status == "completed", response.error + srt = Path(response.outputs["srt_uri"]).read_text(encoding="utf-8") + assert "SUB 001" in srt diff --git a/tests/test_registry.py b/tests/test_registry.py new file mode 100644 index 0000000..7512418 --- /dev/null +++ b/tests/test_registry.py @@ -0,0 +1,116 @@ +"""进程内节点注册表测试。 + +覆盖节点注册、全量注册、查询、进程内调用以及未注册节点的报错路径， +验证注册表作为调度器唯一调用入口的正确性。 +""" + +import pytest + +from wov_app import registry +from wov_sdk.models import InvokeRequest, InvokeResponse, NodeManifest + + +def _echo_manifest() -> NodeManifest: + """构造最小合法 echo 节点清单。""" + return NodeManifest( + id="echo", + name="Echo", + version="1.0.0", + capability="echo", + command=["python", "-m", "echo"], + repo_dir="nodes", + ) + + +def test_register_and_list() -> None: + """验证注册后可查询与列出节点，且按 ID 排序。""" + registry.register(_echo_manifest(), lambda request: InvokeResponse(status="completed")) + registry.register( + NodeManifest( + id="z-node", + name="Z", + version="1", + capability="x", + command=["python", "-m", "z"], + repo_dir="nodes", + ), + lambda request: InvokeResponse(status="completed"), + ) + assert [node.id for node in registry.list_nodes()] == ["echo", "z-node"] + assert registry.get_node("echo").capability == "echo" + assert registry.get_node("missing") is None + + +def test_register_validation() -> None: + """验证非法 manifest 注册会被协议校验拒绝。""" + invalid = _echo_manifest() + invalid.id = "" + with pytest.raises(ValueError): + registry.register(invalid, lambda request: InvokeResponse(status="completed")) + + +def test_register_all_loads_builtin_nodes() -> None: + """验证 register_all 会加载 manifests/ 下全部内置节点。""" + registry.register_all() + ids = {node.id for node in registry.list_nodes()} + assert { + "echo", + "ffmpeg-extract", + "faster-whisper", + "llm-translate", + "vlm-ocr", + "srt-to-dual-eye-ass", + } <= ids + + +def test_invoke_calls_handler() -> None: + """验证 invoke 会把请求转发给注册的进程内处理器。""" + captured = {} + + def handler(request: InvokeRequest) -> InvokeResponse: + captured["run_id"] = request.run_id + return InvokeResponse(status="completed", outputs={"text": "ok"}) + + registry.register(_echo_manifest(), handler) + response = registry.invoke("echo", InvokeRequest(run_id="run_1", node_instance_id="")) + assert response.status == "completed" + assert response.outputs == {"text": "ok"} + assert captured["run_id"] == "run_1" + + +def test_invoke_unknown_node() -> None: + """验证调用未注册节点时抛出 ValueError。""" + with pytest.raises(ValueError, match="not registered"): + registry.invoke("missing", InvokeRequest(run_id="run_1", node_instance_id="")) + + +def test_invoke_logs_node_lifecycle(caplog) -> None: + """验证 invoke 会记录节点的开始/完成/耗时日志（主进程可见）。""" + registry.register( + _echo_manifest(), lambda request: InvokeResponse(status="completed", outputs={"text": "ok"}) + ) + with caplog.at_level("INFO", logger="vrsub.node"): + registry.invoke("echo", InvokeRequest(run_id="run_1", node_instance_id="")) + assert any("节点 echo 开始" in record.message for record in caplog.records) + assert any("节点 echo 完成" in record.message for record in caplog.records) + + +def test_invoke_logs_node_failure(caplog) -> None: + """验证节点返回 failed 时记录失败日志。""" + registry.register( + _echo_manifest(), lambda request: InvokeResponse(status="failed", error="boom") + ) + with caplog.at_level("INFO", logger="vrsub.node"): + registry.invoke("echo", InvokeRequest(run_id="run_1", node_instance_id="")) + assert any("节点 echo 失败" in record.message for record in caplog.records) + + +def test_get_logger_idempotent() -> None: + """验证日志器重复获取不会重复附加控制台处理器。""" + from wov_app.logging import get_logger + + logger = get_logger("idempotent") + handler_count = len(logger.handlers) + again = get_logger("idempotent") + assert again is logger + assert len(again.handlers) == handler_count diff --git a/tests/test_scheduler.py b/tests/test_scheduler.py new file mode 100644 index 0000000..e52fac8 --- /dev/null +++ b/tests/test_scheduler.py @@ -0,0 +1,655 @@ +"""调度器单元测试。 + +覆盖拓扑排序、任务执行成功/失败分支、输入引用解析、MIME 推断以及 +后台轮询线程的启动与停止。节点调用改为进程内注册表直接调用。 +""" + +import time +from pathlib import Path + +import pytest + +from wov_app import registry +from wov_app.db import Database +from wov_app.scheduler import WorkflowScheduler, topological_sort +from wov_sdk.models import ( + InvokeResponse, + NodeManifest, + WorkflowDefinition, + WorkflowEdge, + WorkflowNode, +) + +# 单体根目录：tests/ 的上一级。 +WORKSPACE = Path(__file__).resolve().parent.parent + + +def _register_echo() -> None: + """把内置 echo 节点注册到进程内注册表。""" + from nodes.echo import invoke + + registry.register(NodeManifest.load(str(WORKSPACE / "manifests" / "echo.json")), invoke) + + +def _db(tmp_path) -> Database: + """在临时目录创建独立数据库。""" + return Database(tmp_path / "wov.db") + + +def _echo_definition() -> WorkflowDefinition: + """构造引用 Echo 节点的单步骤工作流定义。""" + return WorkflowDefinition( + name="echo-flow", + version=1, + nodes=[ + WorkflowNode( + id="step", + node_type="echo", + inputs={"file_uri": "input.video_uri"}, + ) + ], + edges=[], + entry_inputs={"video_uri": "file"}, + final_outputs={"result": "step.file_uri"}, + ) + + +def test_topological_sort() -> None: + """验证 DAG 排序保持依赖顺序，并拒绝环与未知边。""" + definition = WorkflowDefinition( + name="dag", + version=1, + nodes=[ + WorkflowNode(id="a", node_type="x"), + WorkflowNode(id="b", node_type="x"), + WorkflowNode(id="c", node_type="x"), + ], + edges=[ + WorkflowEdge(from_node="a", to_node="b"), + WorkflowEdge(from_node="a", to_node="c"), + ], + ) + order = topological_sort(definition) + assert order.index("a") < order.index("b") + assert order.index("a") < order.index("c") + + cycle = WorkflowDefinition( + name="cycle", + version=1, + nodes=[ + WorkflowNode(id="a", node_type="x"), + WorkflowNode(id="b", node_type="x"), + ], + edges=[ + WorkflowEdge(from_node="a", to_node="b"), + WorkflowEdge(from_node="b", to_node="a"), + ], + ) + with pytest.raises(ValueError, match="cycle"): + topological_sort(cycle) + + with pytest.raises(ValueError, match="unknown edge"): + topological_sort( + WorkflowDefinition( + name="bad", + version=1, + nodes=[WorkflowNode(id="a", node_type="x")], + edges=[WorkflowEdge(from_node="a", to_node="missing")], + ) + ) + + +def test_execute_echo_workflow(tmp_path) -> None: + """验证排队任务可被完整执行并登记全部产物。""" + db = _db(tmp_path) + input_file = tmp_path / "input.txt" + input_file.write_text("hello scheduler", encoding="utf-8") + _register_echo() + db.upsert_workflow({"id": "flow", "name": "Flow", "published": 1, "latest_version": 1}) + db.create_workflow_version("flow", 1, _echo_definition().to_dict()) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_1", + "workflow_id": "flow", + "workflow_version": 1, + "status": "QUEUED", + "progress": 0, + "input_uri": str(input_file), + "created_at": now, + "updated_at": now, + } + ) + + scheduler = WorkflowScheduler(db, tmp_path / "storage") + scheduler.execute_run("run_1") + + run = db.get_run("run_1") + assert run["status"] == "COMPLETED" + artifacts = db.list_artifacts("run_1") + assert {item["name"] for item in artifacts} == {"step.text", "step.file_uri", "result"} + + +def test_execute_run_missing_workflow(tmp_path, monkeypatch) -> None: + """验证工作流记录缺失时任务被标记为失败。""" + db = _db(tmp_path) + db.upsert_workflow({"id": "flow", "name": "Flow", "published": 1, "latest_version": 1}) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_missing", + "workflow_id": "flow", + "workflow_version": 1, + "status": "QUEUED", + "progress": 0, + "created_at": now, + "updated_at": now, + } + ) + monkeypatch.setattr(db, "get_workflow", lambda workflow_id: None) + scheduler = WorkflowScheduler(db, tmp_path / "storage") + scheduler.execute_run("run_missing") + assert db.get_run("run_missing")["status"] == "FAILED" + + +def test_execute_run_missing_version(tmp_path) -> None: + """验证版本记录缺失时任务被标记为失败。""" + db = _db(tmp_path) + db.upsert_workflow({"id": "flow", "name": "Flow", "published": 1, "latest_version": 1}) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_version", + "workflow_id": "flow", + "workflow_version": 1, + "status": "QUEUED", + "progress": 0, + "created_at": now, + "updated_at": now, + } + ) + scheduler = WorkflowScheduler(db, tmp_path / "storage") + scheduler.execute_run("run_version") + assert db.get_run("run_version")["status"] == "FAILED" + + +def test_execute_run_missing_node(tmp_path) -> None: + """验证未注册节点被调用时任务失败。""" + db = _db(tmp_path) + definition = WorkflowDefinition( + name="bad", + version=1, + nodes=[ + WorkflowNode( + id="step", + node_type="missing-node", + inputs={"text": "input.video_uri"}, + ) + ], + entry_inputs={"video_uri": "file"}, + ) + db.upsert_workflow({"id": "flow", "name": "Flow", "published": 1, "latest_version": 1}) + db.create_workflow_version("flow", 1, definition.to_dict()) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_node", + "workflow_id": "flow", + "workflow_version": 1, + "status": "QUEUED", + "progress": 0, + "input_uri": str(tmp_path / "in.txt"), + "created_at": now, + "updated_at": now, + } + ) + scheduler = WorkflowScheduler(db, tmp_path / "storage") + scheduler.execute_run("run_node") + assert db.get_run("run_node")["status"] == "FAILED" + + +def test_resolve_ref_and_mime(tmp_path) -> None: + """验证输入引用解析、MIME 推断与文件大小读取。""" + db = _db(tmp_path) + scheduler = WorkflowScheduler(db, tmp_path / "storage") + assert scheduler._resolve_ref("input.video", "in.mp4", {}) == "in.mp4" + assert ( + scheduler._resolve_ref( + "a.out", None, {"a": {"out": "result.txt"}} + ) + == "result.txt" + ) + assert scheduler._resolve_ref("a.out", None, {}) is None + assert scheduler._resolve_ref("nodot", "in.mp4", {}) is None + assert scheduler._mime_type("x.srt") == "application/x-subrip" + assert scheduler._mime_type("x.ass") == "text/plain" + assert scheduler._mime_type("x.wav") == "audio/wav" + assert scheduler._mime_type("x.mp4") == "video/mp4" + assert scheduler._mime_type("x.txt") == "text/plain" + assert scheduler._mime_type("x.bin") == "application/octet-stream" + existing = tmp_path / "existing.txt" + existing.write_text("x", encoding="utf-8") + assert scheduler._file_size(str(existing)) == 1 + missing = tmp_path / "missing.bin" + assert scheduler._file_size(str(missing)) == 0 + + +def test_execute_unknown_or_non_queued_run(tmp_path) -> None: + """验证未知任务或非排队任务会被忽略。""" + db = _db(tmp_path) + db.upsert_workflow({"id": "flow", "name": "Flow", "published": 1, "latest_version": 1}) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_done", + "workflow_id": "flow", + "workflow_version": 1, + "status": "COMPLETED", + "progress": 1, + "created_at": now, + "updated_at": now, + } + ) + scheduler = WorkflowScheduler(db, tmp_path / "storage") + scheduler.execute_run("missing") + scheduler.execute_run("run_done") + assert db.get_run("run_done")["status"] == "COMPLETED" + + +def test_execute_missing_input(tmp_path) -> None: + """验证输入引用无法解析时任务失败。""" + db = _db(tmp_path) + definition = WorkflowDefinition( + name="missing-input", + version=1, + nodes=[ + WorkflowNode( + id="step", + node_type="echo", + inputs={"text": "missing.output"}, + ) + ], + entry_inputs={"video_uri": "file"}, + ) + db.upsert_workflow({"id": "flow", "name": "Flow", "published": 1, "latest_version": 1}) + db.create_workflow_version("flow", 1, definition.to_dict()) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_input", + "workflow_id": "flow", + "workflow_version": 1, + "status": "QUEUED", + "progress": 0, + "input_uri": str(tmp_path / "in.txt"), + "created_at": now, + "updated_at": now, + } + ) + scheduler = WorkflowScheduler(db, tmp_path / "storage") + scheduler.execute_run("run_input") + assert db.get_run("run_input")["status"] == "FAILED" + + +def test_execute_node_failed_response(tmp_path) -> None: + """验证节点返回 failed 时任务被标记为失败。""" + db = _db(tmp_path) + registry.register( + NodeManifest( + id="fail-node", + name="Fail", + version="1", + capability="echo", + repo_dir="nodes", + command=["python", "-m", "fail"], + ), + lambda request: InvokeResponse(status="failed", error="boom"), + ) + definition = WorkflowDefinition( + name="fail-flow", + version=1, + nodes=[ + WorkflowNode( + id="step", + node_type="fail-node", + inputs={"text": "input.video_uri"}, + ) + ], + entry_inputs={"video_uri": "file"}, + ) + db.upsert_workflow({"id": "flow", "name": "Flow", "published": 1, "latest_version": 1}) + db.create_workflow_version("flow", 1, definition.to_dict()) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_fail_node", + "workflow_id": "flow", + "workflow_version": 1, + "status": "QUEUED", + "progress": 0, + "input_uri": str(tmp_path / "in.txt"), + "created_at": now, + "updated_at": now, + } + ) + scheduler = WorkflowScheduler(db, tmp_path / "storage") + scheduler.execute_run("run_fail_node") + assert db.get_run("run_fail_node")["status"] == "FAILED" + + +def test_scheduler_start_stop_loop(tmp_path) -> None: + """验证调度线程可重复启动并正常停止。""" + db = _db(tmp_path) + scheduler = WorkflowScheduler(db, tmp_path / "storage", interval_seconds=0.05) + scheduler.start() + try: + scheduler.start() + time.sleep(0.15) + finally: + scheduler.stop() + assert scheduler._thread is None + + +def test_scheduler_background_executes_queued_run(tmp_path) -> None: + """验证后台线程会自动执行排队中的任务。""" + db = _db(tmp_path) + input_file = tmp_path / "input.txt" + input_file.write_text("background", encoding="utf-8") + _register_echo() + db.upsert_workflow({"id": "flow", "name": "Flow", "published": 1, "latest_version": 1}) + db.create_workflow_version("flow", 1, _echo_definition().to_dict()) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_bg", + "workflow_id": "flow", + "workflow_version": 1, + "status": "QUEUED", + "progress": 0, + "input_uri": str(input_file), + "created_at": now, + "updated_at": now, + } + ) + scheduler = WorkflowScheduler(db, tmp_path / "storage", interval_seconds=0.05) + scheduler.start() + try: + deadline = time.monotonic() + 10 + while time.monotonic() < deadline: + if db.get_run("run_bg")["status"] in {"COMPLETED", "FAILED"}: + break + time.sleep(0.1) + finally: + scheduler.stop() + assert db.get_run("run_bg")["status"] == "COMPLETED" + + +def test_final_artifact_renamed_with_language_tag(tmp_path) -> None: + """验证最终产物按 上传文件名.语言.时间戳 重命名并登记新 URI。""" + db = _db(tmp_path) + input_file = tmp_path / "movie01.mp4" + input_file.write_text("video", encoding="utf-8") + _register_echo() + definition = WorkflowDefinition( + name="lang-flow", + version=1, + nodes=[ + WorkflowNode( + id="step", + node_type="echo", + params={"target_language": "zh-CN"}, + inputs={"file_uri": "input.video_uri"}, + ) + ], + edges=[], + entry_inputs={"video_uri": "file"}, + final_outputs={"cn_srt": "step.file_uri"}, + ) + db.upsert_workflow({"id": "flow", "name": "Flow", "published": 1, "latest_version": 1}) + db.create_workflow_version("flow", 1, definition.to_dict()) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_1", + "workflow_id": "flow", + "workflow_version": 1, + "status": "QUEUED", + "progress": 0, + "input_uri": str(input_file), + "created_at": now, + "updated_at": now, + } + ) + scheduler = WorkflowScheduler(db, tmp_path / "storage") + scheduler.execute_run("run_1") + artifacts = db.list_artifacts("run_1") + final = next(item for item in artifacts if item["name"] == "cn_srt") + filename = Path(final["uri"]).name + # 命名规则：movie01.zh-CN.<14位时间戳>.txt + assert filename.startswith("movie01.zh-CN.") + assert filename.endswith(".txt") + assert Path(final["uri"]).is_file() + # 原始未重命名文件不应残留。 + step_artifacts = [item for item in artifacts if item["name"] == "step.file_uri"] + assert not Path(step_artifacts[0]["uri"]).exists() + + +def test_final_artifact_renamed_fallback_base_and_tag(tmp_path) -> None: + """验证无上传文件时基础名回退 subtitle，无语言参数时标识回退别名。""" + db = _db(tmp_path) + _register_echo() + definition = WorkflowDefinition( + name="fallback-flow", + version=1, + nodes=[ + # 空输入让 echo 走默认文本路径，避免 input_uri 缺失导致解析失败。 + WorkflowNode(id="step", node_type="echo", inputs={}) + ], + entry_inputs={"video_uri": "file"}, + final_outputs={"result": "step.file_uri"}, + ) + db.upsert_workflow({"id": "flow", "name": "Flow", "published": 1, "latest_version": 1}) + db.create_workflow_version("flow", 1, definition.to_dict()) + now = "2026-01-01T00:00:00+00:00" + # 故意不提供 input_uri，验证基础名回退。 + db.create_run( + { + "id": "run_1", + "workflow_id": "flow", + "workflow_version": 1, + "status": "QUEUED", + "progress": 0, + "input_uri": None, + "created_at": now, + "updated_at": now, + } + ) + scheduler = WorkflowScheduler(db, tmp_path / "storage") + scheduler.execute_run("run_1") + final = next(item for item in db.list_artifacts("run_1") if item["name"] == "result") + filename = Path(final["uri"]).name + # 基础名回退 subtitle、标识回退别名 result。 + assert filename.startswith("subtitle.result.") + assert filename.endswith(".txt") + + +def test_execute_run_merges_param_overrides(tmp_path) -> None: + """验证调度执行时把 param_overrides 合并进节点参数。""" + db = _db(tmp_path) + input_file = tmp_path / "input.txt" + input_file.write_text("x", encoding="utf-8") + captured = {} + + def recording_handler(request): + captured["params"] = dict(request.params) + return InvokeResponse(status="completed", outputs={"text": "ok"}) + + registry.register( + NodeManifest( + id="record-node", + name="Record", + version="1", + capability="echo", + repo_dir="nodes", + command=["python", "-m", "record"], + ), + recording_handler, + ) + definition = WorkflowDefinition( + name="ov-flow", + version=1, + nodes=[WorkflowNode(id="step", node_type="record-node", params={"base": 1})], + entry_inputs={"video_uri": "file"}, + ) + db.upsert_workflow({"id": "flow", "name": "F", "published": 1, "latest_version": 1}) + db.create_workflow_version("flow", 1, definition.to_dict()) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_ov", + "workflow_id": "flow", + "workflow_version": 1, + "status": "QUEUED", + "progress": 0, + "param_overrides": {"step": {"crop": [0, 0.5, 1, 0.5]}}, + "input_uri": str(input_file), + "created_at": now, + "updated_at": now, + } + ) + scheduler = WorkflowScheduler(db, tmp_path / "storage") + scheduler.execute_run("run_ov") + assert captured["params"] == {"base": 1, "crop": [0, 0.5, 1, 0.5]} + + +def _two_node_definition() -> WorkflowDefinition: + """构造 a→b 两节点工作流：b 引用 a 的输出。""" + return WorkflowDefinition( + name="two-flow", + version=1, + nodes=[ + WorkflowNode(id="a", node_type="x", inputs={"video_uri": "input.video_uri"}), + WorkflowNode(id="b", node_type="x", inputs={"data_uri": "a.data_uri"}), + ], + edges=[WorkflowEdge(from_node="a", to_node="b")], + entry_inputs={"video_uri": "file"}, + final_outputs={"result": "b.data_uri"}, + ) + + +def test_execute_pause_between_nodes_and_resume(tmp_path, monkeypatch) -> None: + """验证运行中暂停：节点边界停下保持 PAUSED；续跑时跳过已完成节点。""" + db = _db(tmp_path) + db.upsert_workflow({"id": "flow", "name": "Flow", "published": 1, "latest_version": 1}) + db.create_workflow_version("flow", 1, _two_node_definition().to_dict()) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_pause", + "workflow_id": "flow", + "workflow_version": 1, + "status": "QUEUED", + "progress": 0, + "input_uri": str(tmp_path / "in.txt"), + "created_at": now, + "updated_at": now, + } + ) + data_file = tmp_path / "data.bin" + data_file.write_bytes(b"x") + + calls: list[str] = [] + + def fake_invoke(node_type, request): + # registry.invoke 首参是 node_type；用产物目录名（steps/）识别节点。 + node_id = Path(request.output_dir).name + calls.append(node_id) + # 第一个节点完成后立刻暂停任务，模拟用户在运行中点暂停。 + if node_id == "a": + db.pause_run("run_pause", now) + return InvokeResponse(status="completed", outputs={"data_uri": str(data_file)}) + + monkeypatch.setattr(registry, "invoke", fake_invoke) + scheduler = WorkflowScheduler(db, tmp_path / "storage") + scheduler.execute_run("run_pause") + assert db.get_run("run_pause")["status"] == "PAUSED" + assert calls == ["a"] # 节点 b 未执行。 + + # 继续：恢复排队并再次执行，节点 a 已产出结果应被跳过，只执行 b。 + db.resume_run("run_pause", now) + scheduler.execute_run("run_pause") + assert db.get_run("run_pause")["status"] == "COMPLETED" + assert calls == ["a", "b"] + artifacts = db.list_artifacts("run_pause") + assert {item["name"] for item in artifacts} == {"a.data_uri", "b.data_uri", "result"} + + +def test_execute_paused_run_not_run(tmp_path, monkeypatch) -> None: + """验证非可执行状态（如 RUNNING 之外的值）的任务不会被执行。""" + db = _db(tmp_path) + db.upsert_workflow({"id": "flow", "name": "Flow", "published": 1, "latest_version": 1}) + db.create_workflow_version("flow", 1, _two_node_definition().to_dict()) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_done", + "workflow_id": "flow", + "workflow_version": 1, + "status": "COMPLETED", + "progress": 1.0, + "created_at": now, + "updated_at": now, + } + ) + called = [] + + def fake_invoke(node_id, request): + called.append(node_id) + return InvokeResponse(status="completed", outputs={}) + + monkeypatch.setattr(registry, "invoke", fake_invoke) + WorkflowScheduler(db, tmp_path / "storage").execute_run("run_done") + assert called == [] + + +def test_execute_pause_after_last_node_keeps_paused(tmp_path, monkeypatch) -> None: + """验证全部节点完成但运行中被暂停时保持 PAUSED；续跑补做收尾后完成。""" + db = _db(tmp_path) + db.upsert_workflow({"id": "flow", "name": "Flow", "published": 1, "latest_version": 1}) + db.create_workflow_version("flow", 1, _two_node_definition().to_dict()) + now = "2026-01-01T00:00:00+00:00" + db.create_run( + { + "id": "run_tail", + "workflow_id": "flow", + "workflow_version": 1, + "status": "QUEUED", + "progress": 0, + "input_uri": str(tmp_path / "in.txt"), + "created_at": now, + "updated_at": now, + } + ) + data_file = tmp_path / "data.bin" + data_file.write_bytes(b"x") + calls: list[str] = [] + + def fake_invoke(node_type, request): + node_id = Path(request.output_dir).name + calls.append(node_id) + if node_id == "b": # 最后一个节点执行时暂停。 + db.pause_run("run_tail", now) + return InvokeResponse(status="completed", outputs={"data_uri": str(data_file)}) + + monkeypatch.setattr(registry, "invoke", fake_invoke) + scheduler = WorkflowScheduler(db, tmp_path / "storage") + scheduler.execute_run("run_tail") + # 全部节点已执行，但收尾前被暂停 → 保持 PAUSED 而不是 COMPLETED。 + assert db.get_run("run_tail")["status"] == "PAUSED" + assert calls == ["a", "b"] + + # 续跑：节点产物齐备全部跳过，补做收尾后完成。 + db.resume_run("run_tail", now) + scheduler.execute_run("run_tail") + assert db.get_run("run_tail")["status"] == "COMPLETED" + assert calls == ["a", "b"] diff --git a/tests/test_seed.py b/tests/test_seed.py new file mode 100644 index 0000000..f592fc9 --- /dev/null +++ b/tests/test_seed.py @@ -0,0 +1,108 @@ +"""种子数据测试。 + +验证从 workflows/*.json 数据文件加载默认工作流、幂等性，以及 +开启种子与调度器后的应用生命周期。工作流定义来自数据文件而非代码。 +""" + +from pathlib import Path + +from fastapi.testclient import TestClient + +from wov_app.db import Database +from wov_app.main import app +from wov_app.seed import seed_default_workflows + +# 单体根目录：tests/ 的上一级。 +WORKSPACE = Path(__file__).resolve().parent.parent + + +def test_seed_default_workflows_idempotent(tmp_path) -> None: + """验证从数据文件加载 demo/zh-direct 两个工作流且重复调用幂等。""" + db = Database(tmp_path / "wov.db") + created = seed_default_workflows(db) + assert created == 3 + assert db.get_workflow("demo") is not None + assert db.get_workflow("zh-direct") is not None + + # demo：asr 显式声明 model_path 与长音频参数，模型选择完全数据化。 + demo = db.get_latest_workflow_version("demo")["definition"] + assert demo["name"] == "视频字幕生成" + demo_asr = next(node for node in demo["nodes"] if node["id"] == "asr") + assert demo_asr["params"]["model_path"] == "faster-whisper-large-v3" + assert demo_asr["params"]["condition_on_previous_text"] is False + + # zh-direct：使用中文直出模型并开启翻译任务。 + zh = db.get_latest_workflow_version("zh-direct")["definition"] + assert zh["name"] == "中文直出字幕" + zh_asr = next(node for node in zh["nodes"] if node["id"] == "asr") + assert zh_asr["params"]["model_path"] == "whisper-large-v2-translate-zh-v0.2-st-ct2" + assert zh_asr["params"]["task"] == "translate" + + # 再次调用不重复创建。 + assert seed_default_workflows(db) == 0 + assert len(db.list_workflow_versions("demo")) == 1 + + +def test_seed_custom_dir_and_empty(tmp_path) -> None: + """验证自定义数据目录的加载与空目录返回 0。""" + db = Database(tmp_path / "wov.db") + custom = tmp_path / "workflows" + custom.mkdir() + (custom / "a.json").write_text( + """ + { + "id": "flow-a", + "name": "Flow A", + "description": "custom", + "version": 1, + "definition": { + "name": "Flow A", + "version": 1, + "nodes": [{"id": "step", "node_type": "echo"}], + "edges": [], + "entry_inputs": {}, + "final_outputs": {} + } + } + """, + encoding="utf-8", + ) + assert seed_default_workflows(db, custom) == 1 + assert db.get_workflow("flow-a") is not None + # 空目录返回 0。 + empty = tmp_path / "empty" + empty.mkdir() + assert seed_default_workflows(db, empty) == 0 + # 已存在的工作流被跳过。 + assert seed_default_workflows(db, custom) == 0 + + +def test_lifespan_with_seed_and_scheduler(monkeypatch) -> None: + """验证启用自动种子与调度器后应用正常启动，demo 与中文直出应用均可见。""" + monkeypatch.setenv("WOV_AUTO_SEED", "1") + monkeypatch.setenv("WOV_SCHEDULER_ENABLED", "1") + with TestClient(app) as client: + apps = client.get("/api/apps") + assert apps.status_code == 200 + assert any(item["id"] == "demo" for item in apps.json()) + assert any(item["id"] == "zh-direct" for item in apps.json()) + + +def test_seed_workflows_have_chunk_seconds(tmp_path) -> None: + """验证内置工作流的 asr 节点均显式声明分块参数。""" + db = Database(tmp_path / "wov.db") + seed_default_workflows(db) + for workflow_id in ("demo", "zh-direct"): + definition = db.get_latest_workflow_version(workflow_id)["definition"] + asr = next(node for node in definition["nodes"] if node["id"] == "asr") + assert asr["params"]["chunk_seconds"] == 60 + + +def test_seed_workflows_vad_filter_off(tmp_path) -> None: + """验证内置工作流 asr 显式开启 VAD。""" + db = Database(tmp_path / "wov.db") + seed_default_workflows(db) + for workflow_id in ("demo", "zh-direct"): + definition = db.get_latest_workflow_version(workflow_id)["definition"] + asr = next(node for node in definition["nodes"] if node["id"] == "asr") + assert asr["params"]["vad_filter"] is True diff --git a/tests/test_uvicorn_smoke.py b/tests/test_uvicorn_smoke.py new file mode 100644 index 0000000..82202b4 --- /dev/null +++ b/tests/test_uvicorn_smoke.py @@ -0,0 +1,34 @@ +"""uvicorn 冒烟测试。 + +用真实套接字启动 uvicorn 服务并请求 /health，验证应用能脱离 TestClient +在实际 Web 服务环境中正常工作。 +""" + +import threading +import time +import urllib.request + +from uvicorn import Config, Server + +from wov_app.main import app + + +def test_uvicorn_serves_app_over_real_socket() -> None: + """验证 uvicorn 监听真实端口后健康检查可用。""" + config = Config(app=app, host="127.0.0.1", port=0, log_level="error") + server = Server(config) + thread = threading.Thread(target=server.run, daemon=True) + thread.start() + try: + deadline = time.monotonic() + 10 + while not server.started and time.monotonic() < deadline: + time.sleep(0.05) + assert server.started + + port = server.servers[0].sockets[0].getsockname()[1] + with urllib.request.urlopen(f"http://127.0.0.1:{port}/health", timeout=5) as response: + assert response.status == 200 + assert b'"wov-api"' in response.read() + finally: + server.should_exit = True + thread.join(timeout=10) diff --git a/tests/test_workflow_api.py b/tests/test_workflow_api.py new file mode 100644 index 0000000..da3909b --- /dev/null +++ b/tests/test_workflow_api.py @@ -0,0 +1,117 @@ +"""工作流管理 API 测试。 + +覆盖工作流的创建、查询、校验、发布、版本列表与删除等管理接口。 +""" + +from fastapi.testclient import TestClient + +from wov_app.main import app + + +def definition() -> dict: + """构造一个引用 Echo 节点的合法工作流定义。""" + return { + "name": "echo-flow", + "version": 1, + "nodes": [ + { + "id": "step", + "node_type": "echo", + "inputs": {"file_uri": "input.video_uri"}, + } + ], + "edges": [], + "entry_inputs": {"video_uri": "file"}, + "final_outputs": {"result": "step.file_uri"}, + } + + +def test_workflow_crud_and_publish() -> None: + """验证工作流 CRUD、校验、发布与版本列表的完整流程。""" + with TestClient(app) as client: + created = client.post( + "/api/admin/workflows", + json={ + "id": "echo-flow", + "name": "Echo Flow", + "description": "demo", + "definition": definition(), + }, + ) + assert created.status_code == 200 + assert created.json()["id"] == "echo-flow" + + assert client.get("/api/admin/workflows").status_code == 200 + assert client.get("/api/admin/workflows/echo-flow").status_code == 200 + assert client.get("/api/admin/workflows/missing").status_code == 404 + + validated = client.post( + "/api/admin/workflows/echo-flow/validate", + json=definition(), + ) + assert validated.status_code == 200 + assert validated.json()["valid"] is True + assert client.post( + "/api/admin/workflows/missing/validate", + json=definition(), + ).status_code == 404 + + published = client.post("/api/admin/workflows/echo-flow/publish") + assert published.status_code == 200 + assert published.json()["published"] == "echo-flow" + assert client.post("/api/admin/workflows/missing/publish").status_code == 404 + + versions = client.get("/api/admin/workflows/echo-flow/versions") + assert versions.status_code == 200 + assert len(versions.json()) == 1 + assert client.get("/api/admin/workflows/missing/versions").status_code == 404 + + assert client.delete("/api/admin/workflows/echo-flow").status_code == 200 + assert client.delete("/api/admin/workflows/echo-flow").status_code == 404 + + +def test_workflow_slug_without_id() -> None: + """验证未提供 ID 时后端会从名称生成 slug。""" + with TestClient(app) as client: + created = client.post( + "/api/admin/workflows", + json={ + "name": "Echo Flow", + "definition": definition(), + }, + ) + assert created.status_code == 200 + assert 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= 79859, upload-time = "2026-08-13T16:50:01.323Z" }, +] + +[[package]] +name = "vrsub" +version = "0.1.0" +source = { editable = "." } +dependencies = [ + { name = "fastapi" }, + { name = "faster-whisper" }, + { name = "imageio-ffmpeg" }, + { name = "nvidia-cublas-cu12" }, + { name = "nvidia-cudnn-cu12" }, + { name = "python-dotenv" }, + { name = "python-multipart" }, + { name = "uvicorn" }, +] + +[package.dev-dependencies] +dev = [ + { name = "httpx" }, + { name = "pytest" }, + { name = "pytest-cov" }, +] + +[package.metadata] +requires-dist = [ + { name = "fastapi" }, + { name = "faster-whisper", specifier = ">=1.2.1" }, + { name = "imageio-ffmpeg", specifier = ">=0.6" }, + { name = "nvidia-cublas-cu12", specifier = ">=12.9.2.10" }, + { name = "nvidia-cudnn-cu12", specifier = ">=9.24.0.43" }, + { name = "python-dotenv", specifier = ">=1.2.2" }, + { name = "python-multipart" }, + { name = "uvicorn" }, +] + +[package.metadata.requires-dev] +dev = [ + { name = "httpx" }, + { name = "pytest" }, + { name = "pytest-cov" }, +] diff --git a/web/admin.html b/web/admin.html new file mode 100755 index 0000000..b8ea96b --- /dev/null +++ b/web/admin.html @@ -0,0 +1,46 @@ + + + + + + + VRSub 管理后台 + + + +

+ VRSub + +
+ +
+

管理后台

+ + +
+

已发布工作流

+
+ + + + + + + + + + + +
ID名称版本状态操作
+
+
+
+ + + + diff --git a/web/assets/app.js b/web/assets/app.js new file mode 100644 index 0000000..fbdb2ca --- /dev/null +++ b/web/assets/app.js @@ -0,0 +1,647 @@ +// VRSub 静态前端公共脚本：所有页面共用的 API 封装、渲染函数与事件绑定。 +// 首页只负责发起任务；任务管理页展示全部任务的进度、产物下载与失败重试。 + +// 演示"视频字幕生成"工作流的 DAG 定义，预填在工作流编排页。 +const DEMO_WORKFLOW = { + name: "视频字幕生成", + version: 1, + nodes: [ + { + id: "extract", + node_type: "ffmpeg-extract", + params: { sample_rate: 16000, channels: 1 }, + inputs: { video_uri: "input.video_uri" }, + }, + { + id: "asr", + node_type: "faster-whisper", + params: { language: "ja" }, + inputs: { audio_uri: "extract.audio_uri" }, + }, + { + id: "translate", + node_type: "llm-translate", + params: { target_language: "zh-CN" }, + inputs: { srt_uri: "asr.srt_uri" }, + }, + { + id: "ass", + node_type: "srt-to-dual-eye-ass", + params: { resolution: "3840x1920" }, + inputs: { cn_srt_uri: "translate.cn_srt_uri" }, + }, + ], + edges: [ + { from: "extract", to: "asr" }, + { from: "asr", to: "translate" }, + { from: "translate", to: "ass" }, + ], + entry_inputs: { video_uri: "file" }, + final_outputs: { + cn_srt: "translate.cn_srt_uri", + ass: "ass.ass_uri", + }, +}; + +// 统一封装 fetch：自动携带 JSON 头、解析响应并在失败时抛出可读错误。 +async function api(path, options = {}) { + const response = await fetch(path, { + headers: { "Content-Type": "application/json", ...(options.headers || {}) }, + ...options, + }); + const data = await response.json().catch(() => null); + if (!response.ok) { + // FastAPI 的校验错误 detail 可能是数组，统一序列化为字符串展示。 + const detail = data && data.detail ? JSON.stringify(data.detail) : response.statusText; + throw new Error(`${response.status} ${detail}`); + } + return data; +} + +// 转义用户可控文本，防止 XSS 注入到表格或状态 HTML 中。 +function escapeHtml(value) { + return String(value) + .replaceAll("&", "&") + .replaceAll("<", "<") + .replaceAll(">", ">") + .replaceAll('"', """) + .replaceAll("'", "'"); +} + +// 根据状态生成带语义颜色的徽章 HTML。 +function badge(status) { + // 统一转小写比较，兼容后端返回的不同大小写。 + const value = String(status).toLowerCase(); + const className = + value === "completed" || value === "ok" + ? "ok" + : value === "error" || value === "failed" + ? "error" + : "warn"; + return `${escapeHtml(status)}`; +} + +// 把 ISO 时间格式化为本地时间；非法值原样返回。 +function formatTime(value) { + if (!value) return ""; + const date = new Date(value); + return Number.isNaN(date.getTime()) ? value : date.toLocaleString(); +} + +// 计算距给定时间的流逝时长，用于任务页展示已运行时间。 +function formatElapsed(value) { + if (!value) return ""; + const seconds = Math.floor((Date.now() - new Date(value).getTime()) / 1000); + if (Number.isNaN(seconds) || seconds < 0) return ""; + if (seconds < 60) return `${seconds} 秒`; + return `${Math.floor(seconds / 60)} 分钟`; +} + +// 健康检查失败时在页面顶部展示后端不可用提示。 +async function loadHealth() { + try { + await api("/health"); + } catch (error) { + const banner = document.createElement("div"); + banner.className = "result"; + banner.textContent = `后端不可用：${error.message}`; + document.body.prepend(banner); + } +} + +// 加载工作流列表并渲染发布状态与操作按钮。 +async function loadWorkflows() { + const workflows = await api("/api/admin/workflows"); + const tbody = document.getElementById("workflowList"); + if (!tbody) return; + tbody.innerHTML = workflows.length + ? workflows + .map( + (workflow) => ` + + ${escapeHtml(workflow.id)} + ${escapeHtml(workflow.name)} + ${escapeHtml(workflow.latest_version)} + ${workflow.published ? badge("ok") : badge("draft")} + + + ${workflow.published ? "" : ``} + + `, + ) + .join("") + : '暂无工作流'; +} + +// 加载已发布的工作流（应用），填充首页的下拉选择框。 +// 已发布工作流缓存（id → {name, definition}），供按需切换 OCR 面板。 +let workflowApps = []; +// 需要 crop 的节点 ID（如 frame-extract）与用户框选的 crop 值。 +let cropNodeId = null; +let selectedCrop = null; +// 框选模式：开启后才让画布接收鼠标事件，平时不拦截视频控件。 +let drawMode = false; +// 当前选择的视频文件：change 时保存，提交时使用（避免被重置清空 input 丢失）。 +let selectedVideoFile = null; + +async function loadWorkflowOptions() { + const select = document.getElementById("workflowSelect"); + if (!select) return; + workflowApps = await api("/api/apps"); + select.innerHTML = workflowApps.length + ? workflowApps + .map( + (app) => + ``, + ) + .join("") + : ''; + // 切换工作流时按需展示框选面板。 + select.addEventListener("change", onWorkflowChange); + await onWorkflowChange(); +} + +// 切换工作流：若 DAG 中存在带 crop 参数的节点（字幕 OCR 流程）， +// 切换到框选面板；否则使用标准上传。 +async function onWorkflowChange() { + const select = document.getElementById("workflowSelect"); + const ocrCard = document.getElementById("ocrCard"); + const standardCard = document.getElementById("standardCard"); + if (!select || !ocrCard || !standardCard) return; + cropNodeId = null; + selectedCrop = null; + const app = workflowApps.find((item) => item.id === select.value); + // 数据驱动判断：节点 params 中声明了 crop 即需要框选。 + const cropNode = (app?.definition?.nodes || []).find( + (node) => node.params && node.params.crop !== undefined, + ); + const needsCrop = Boolean(cropNode); + cropNodeId = needsCrop ? cropNode.id : null; + ocrCard.hidden = !needsCrop; + standardCard.hidden = needsCrop; + resetOcrPanel(); + if (needsCrop) { + document.getElementById("ocrProgress").textContent = "请选择视频并框选字幕区域"; + } +} + +// 重置 OCR 面板：清除视频、画布与框选状态。 +function resetOcrPanel() { + const video = document.getElementById("ocrVideo"); + const canvas = document.getElementById("ocrCanvas"); + const fileInput = document.getElementById("ocrVideoFile"); + const cropValue = document.getElementById("cropValue"); + const submit = document.getElementById("ocrSubmit"); + selectedCrop = null; + if (video) video.removeAttribute("src"); + if (canvas) { + canvas.width = 0; + canvas.height = 0; + } + if (fileInput) fileInput.value = ""; + if (cropValue) cropValue.value = ""; + if (submit) submit.disabled = true; + selectedVideoFile = null; + exitDrawMode(); +} + +// 用 crop.js 把画布上的框选矩形归一化为 crop 比例并展示。 +function applyCropRect(rect) { + const video = document.getElementById("ocrVideo"); + const box = document.getElementById("videoBox"); + if (!video.videoWidth || !box) return; + const crop = rectToCrop( + rect, + video.videoWidth, + video.videoHeight, + box.clientWidth, + box.clientHeight, + ); + selectedCrop = crop; + document.getElementById("cropValue").value = crop.join(", "); + document.getElementById("ocrSubmit").disabled = false; + document.getElementById("ocrProgress").textContent = + `已框选 crop=[${crop.join(", ")}]，可提交任务`; +} + +// 进入框选模式：暂停视频、隐藏原生控件、启用画布绘制。 +function enterDrawMode() { + const video = document.getElementById("ocrVideo"); + const canvas = document.getElementById("ocrCanvas"); + const toggle = document.getElementById("ocrDrawMode"); + if (!video.videoWidth) { + document.getElementById("ocrProgress").textContent = "请先选择视频并定位到有字幕的画面"; + return; + } + drawMode = true; + video.pause(); + video.removeAttribute("controls"); // 隐藏进度条，避免遮挡框选操作。 + canvas.classList.add("drawable"); + if (toggle) toggle.textContent = "退出框选模式"; + document.getElementById("ocrProgress").textContent = "请拖动框选字幕区域"; +} + +// 退出框选模式：恢复原生控件，画布不再拦截指针。 +function exitDrawMode() { + const video = document.getElementById("ocrVideo"); + const canvas = document.getElementById("ocrCanvas"); + const toggle = document.getElementById("ocrDrawMode"); + drawMode = false; + if (video) video.setAttribute("controls", ""); + if (canvas) canvas.classList.remove("drawable"); + if (toggle) toggle.textContent = "进入框选模式"; +} + +// 初始化 OCR 面板：视频预览 + 画布拖动框选。 +function setupOcrPanel() { + const fileInput = document.getElementById("ocrVideoFile"); + const video = document.getElementById("ocrVideo"); + const canvas = document.getElementById("ocrCanvas"); + const resetButton = document.getElementById("ocrReset"); + if (!fileInput || !video || !canvas) return; + + // 选择视频后显示预览并设置画布尺寸与坐标换算。 + // 注意顺序：必须先 reset（会清空旧 src），再设置新 src，否则被清掉。 + fileInput.addEventListener("change", () => { + const file = fileInput.files[0]; + if (!file) return; + resetOcrPanel(); + // 重置会清空 input，这里把文件引用保存下来供提交使用。 + selectedVideoFile = file; + video.src = URL.createObjectURL(file); + video.load(); + video.onloadedmetadata = () => { + const box = document.getElementById("videoBox"); + canvas.width = box.clientWidth; + canvas.height = box.clientHeight; + document.getElementById("ocrProgress").textContent = "请在预览中拖动框选字幕区域"; + }; + }); + + // 框选模式开关：进入/退出。 + const drawToggle = document.getElementById("ocrDrawMode"); + if (drawToggle) { + drawToggle.addEventListener("click", () => { + if (drawMode) { + exitDrawMode(); + } else { + enterDrawMode(); + } + }); + } + + // 拖动绘制框选矩形：mousedown 起点 → mousemove 更新 → mouseup 生成 crop。 + let startX = 0; + let startY = 0; + let drawing = false; + canvas.addEventListener("mousedown", (event) => { + if (!drawMode) return; + const rect = canvas.getBoundingClientRect(); + startX = event.clientX - rect.left; + startY = event.clientY - rect.top; + drawing = true; + }); + canvas.addEventListener("mousemove", (event) => { + if (!drawing || !drawMode) return; + const rect = canvas.getBoundingClientRect(); + const x = event.clientX - rect.left; + const y = event.clientY - rect.top; + const ctx = canvas.getContext("2d"); + ctx.clearRect(0, 0, canvas.width, canvas.height); + ctx.strokeStyle = "#ff5252"; + ctx.lineWidth = 2; + ctx.strokeRect(Math.min(startX, x), Math.min(startY, y), Math.abs(x - startX), Math.abs(y - startY)); + }); + canvas.addEventListener("mouseup", (event) => { + if (!drawing || !drawMode) return; + drawing = false; + const rect = canvas.getBoundingClientRect(); + const x = event.clientX - rect.left; + const y = event.clientY - rect.top; + const w = Math.abs(x - startX); + const h = Math.abs(y - startY); + if (w < 5 || h < 5) return; + applyCropRect({ + x: Math.min(startX, x), + y: Math.min(startY, y), + w, + h, + }); + }); + + // 清除框选：清空画布与 crop，禁用提交。 + resetButton.addEventListener("click", () => { + const ctx = canvas.getContext("2d"); + ctx.clearRect(0, 0, canvas.width, canvas.height); + selectedCrop = null; + document.getElementById("cropValue").value = ""; + document.getElementById("ocrSubmit").disabled = true; + document.getElementById("ocrProgress").textContent = "请在预览中拖动框选字幕区域"; + }); +} + +// 提交字幕 OCR 任务：携带 crop 覆盖参数，框选完成才可用。 +async function submitOcr() { + const fileInput = document.getElementById("ocrVideoFile"); + const progress = document.getElementById("ocrProgress"); + if (!selectedCrop) { + progress.textContent = "请先框选字幕区域"; + return; + } + const file = selectedVideoFile; + if (!file) { + progress.textContent = "请先选择视频文件"; + return; + } + const workflowId = document.getElementById("workflowSelect").value; + const form = new FormData(); + form.append("file", file); + // 把框选的 crop 传给需要它的节点（如 frame-extract）。 + form.append("params", JSON.stringify({ [cropNodeId]: { crop: selectedCrop } })); + progress.textContent = "上传中..."; + try { + const response = await fetch(`/api/apps/${encodeURIComponent(workflowId)}/runs`, { + method: "POST", + body: form, + }); + const data = await response.json().catch(() => null); + if (!response.ok) { + throw new Error(data && data.detail ? JSON.stringify(data.detail) : response.statusText); + } + progress.textContent = `任务 ${data.id} 已创建，正在跳转到任务管理...`; + window.location.href = `/tasks.html?run=${encodeURIComponent(data.id)}`; + } catch (error) { + progress.textContent = `创建失败：${error.message}`; + } +} + +// 渲染单个任务的进度条 HTML；失败任务用红色填充。 +function progressBar(percent, failed) { + return `
`; +} + +// 为已完成任务生成最终产物下载链接（cn_srt / ass）。 +function artifactLinks(runId, status) { + if (status !== "COMPLETED") return "-"; + return ` +
+ 中文 SRT + VR ASS +
`; +} + +// 加载最近任务并渲染任务表格：状态、当前节点、进度条、产物与重试。 +async function loadRuns() { + const tbody = document.getElementById("runList"); + if (!tbody) return; + const runs = await api("/api/runs"); + tbody.innerHTML = runs.length + ? runs + .map((run) => { + const percent = Math.round((run.progress || 0) * 100); + const failed = run.status === "FAILED"; + // RUNNING/QUEUED 附加耗时或排队提示，其余状态只显示徽章。 + const statusHtml = + run.status === "RUNNING" + ? `${badge(run.status)} 已运行 ${formatElapsed(run.updated_at)}` + : run.status === "QUEUED" + ? `${badge(run.status)} 排队中` + : run.status === "PAUSED" + ? `${badge(run.status)} 已暂停 ${formatElapsed(run.updated_at)}` + : badge(run.status); + // 失败任务提供重试，所有任务均可删除。 + // 排队/运行中可暂停，暂停后可继续，失败可重试，所有任务可删除。 + const canPause = run.status === "RUNNING" || run.status === "QUEUED"; + const canResume = run.status === "PAUSED"; + const actions = ` + ${canPause ? `` : ""} + ${canResume ? `` : ""} + ${failed ? `` : ""} + + `; + return ` + + ${escapeHtml(run.id)} + ${escapeHtml(run.workflow_id)} + ${statusHtml} + ${escapeHtml(run.current_node_id || "-")} + ${progressBar(percent, failed)} ${percent}% + ${escapeHtml(formatTime(run.created_at))} + ${artifactLinks(run.id, run.status)} + ${actions} + `; + }) + .join("") + : '暂无任务，请到首页发起。'; +} + +// 请求后端重试失败任务，成功后刷新列表。 +async function retryRun(runId) { + try { + const result = await api(`/api/runs/${runId}/retry`, { method: "POST" }); + alert(`任务 ${result.id} 已重新排队`); + await loadRuns(); + } catch (error) { + alert(`重试失败：${error.message}`); + } +} + +// 暂停任务：排队或运行中的任务置为 PAUSED，运行中的任务在节点边界停下。 +async function pauseRun(runId) { + try { + const result = await api(`/api/runs/${runId}/pause`, { method: "POST" }); + alert(`任务 ${result.id} 已暂停`); + await loadRuns(); + } catch (error) { + alert(`暂停失败：${error.message}`); + } +} + +// 继续任务：PAUSED 恢复排队，由调度器从断点继续执行。 +async function resumeRun(runId) { + try { + const result = await api(`/api/runs/${runId}/resume`, { method: "POST" }); + alert(`任务 ${result.id} 已恢复执行`); + await loadRuns(); + } catch (error) { + alert(`继续失败：${error.message}`); + } +} +// 删除任务：二次确认后调用后端删除接口并刷新列表。 +async function deleteRun(runId) { + if (!window.confirm(`确认删除任务 ${runId}？相关产物文件将一并删除。`)) { + return; + } + try { + await api(`/api/runs/${runId}`, { method: "DELETE" }); + await loadRuns(); + } catch (error) { + alert(`删除失败：${error.message}`); + } +} + +// 创建或更新工作流：解析 DAG JSON 后提交，随后刷新列表。 +async function createWorkflow() { + const workflowId = document.getElementById("workflowId").value.trim(); + const name = document.getElementById("workflowName").value.trim(); + const description = document.getElementById("workflowDescription").value.trim(); + let definition; + try { + definition = JSON.parse(document.getElementById("workflowDefinition").value); + } catch (error) { + alert(`DAG JSON 无效：${error.message}`); + return; + } + try { + await api("/api/admin/workflows", { + method: "POST", + body: JSON.stringify({ + id: workflowId || undefined, + name, + description, + definition, + }), + }); + alert("工作流已保存"); + await loadWorkflows(); + } catch (error) { + alert(`保存失败：${error.message}`); + } +} + +// 发布指定工作流，使其出现在用户应用中心。 +async function publishWorkflow(workflowId) { + try { + await api(`/api/admin/workflows/${workflowId}/publish`, { method: "POST" }); + await loadWorkflows(); + } catch (error) { + alert(`发布失败：${error.message}`); + } +} + +// 删除指定工作流。 +async function deleteWorkflow(workflowId) { + try { + await api(`/api/admin/workflows/${workflowId}`, { method: "DELETE" }); + await loadWorkflows(); + } catch (error) { + alert(`删除失败：${error.message}`); + } +} + +// 首页发起任务：选择工作流并上传视频，创建任务后跳转到任务管理页查看进度。 +async function uploadVideo() { + const workflowId = document.getElementById("workflowSelect").value; + const fileInput = document.getElementById("videoFile"); + const progress = document.getElementById("runProgress"); + if (!workflowId) { + progress.textContent = "暂无可用工作流"; + return; + } + if (!fileInput.files.length) { + progress.textContent = "请先选择视频文件"; + return; + } + const form = new FormData(); + form.append("file", fileInput.files[0]); + progress.textContent = "上传中..."; + try { + const response = await fetch(`/api/apps/${encodeURIComponent(workflowId)}/runs`, { + method: "POST", + body: form, + }); + const data = await response.json().catch(() => null); + if (!response.ok) { + throw new Error(data && data.detail ? JSON.stringify(data.detail) : response.statusText); + } + progress.textContent = `任务 ${data.id} 已创建，正在跳转到任务管理...`; + // 首页只负责发起任务，进度展示交给任务管理页。 + window.location.href = `/tasks.html?run=${encodeURIComponent(data.id)}`; + } catch (error) { + progress.textContent = `创建失败：${error.message}`; + } +} + +// 全局点击委托：按按钮上的 data-* 属性分发到对应操作。 +document.addEventListener("click", (event) => { + const deleteWorkflowButton = event.target.closest("[data-delete-workflow]"); + if (deleteWorkflowButton) { + deleteWorkflow(deleteWorkflowButton.dataset.deleteWorkflow); + return; + } + const publishWorkflowButton = event.target.closest("[data-publish-workflow]"); + if (publishWorkflowButton) { + publishWorkflow(publishWorkflowButton.dataset.publishWorkflow); + return; + } + const retryButton = event.target.closest("[data-retry-run]"); + if (retryButton) { + retryRun(retryButton.dataset.retryRun); + return; + } + const pauseButton = event.target.closest("[data-pause-run]"); + if (pauseButton) { + pauseRun(pauseButton.dataset.pauseRun); + return; + } + const resumeButton = event.target.closest("[data-resume-run]"); + if (resumeButton) { + resumeRun(resumeButton.dataset.resumeRun); + return; + } + const deleteRunButton = event.target.closest("[data-delete-run]"); + if (deleteRunButton) { + deleteRun(deleteRunButton.dataset.deleteRun); + } +}); + +// 页面初始化：预填 DAG、绑定按钮事件并加载对应页面数据。 +document.addEventListener("DOMContentLoaded", async () => { + const workflowDefinition = document.getElementById("workflowDefinition"); + if (workflowDefinition) { + workflowDefinition.value = JSON.stringify(DEMO_WORKFLOW, null, 2); + } + const createWorkflowButton = document.getElementById("createWorkflow"); + if (createWorkflowButton) { + createWorkflowButton.addEventListener("click", createWorkflow); + } + const publishWorkflowButton = document.getElementById("publishWorkflow"); + if (publishWorkflowButton) { + publishWorkflowButton.addEventListener("click", () => { + publishWorkflow(document.getElementById("workflowId").value.trim()); + }); + } + const uploadButton = document.getElementById("uploadVideo"); + if (uploadButton) { + uploadButton.addEventListener("click", uploadVideo); + // 首页加载已发布工作流供用户选择。 + await loadWorkflowOptions(); + // 字幕 OCR 面板：视频预览 + 拖动框选（仅需 crop 的工作流展示）。 + setupOcrPanel(); + } + const ocrSubmitButton = document.getElementById("ocrSubmit"); + if (ocrSubmitButton) { + ocrSubmitButton.addEventListener("click", submitOcr); + } + + await loadHealth(); + if (document.getElementById("workflowList")) { + await loadWorkflows(); + } + // 任务管理页：每 3 秒刷新一次全部任务的进度，支持高亮跳转参数 run=ID。 + if (document.getElementById("runList")) { + await loadRuns(); + const params = new URLSearchParams(window.location.search); + const target = params.get("run"); + if (target) { + const row = [...document.querySelectorAll("#runList tr")].find((item) => + item.textContent.includes(target), + ); + if (row) { + row.scrollIntoView({ block: "center" }); + row.style.background = "#fff7db"; + } + } + setInterval(loadRuns, 3000); + } +}); diff --git a/web/assets/crop.js b/web/assets/crop.js new file mode 100644 index 0000000..17db3f5 --- /dev/null +++ b/web/assets/crop.js @@ -0,0 +1,38 @@ +// WOV OCR 前端 crop 归一化工具（纯函数，供 node 单测与浏览器共用）。 +// 负责把用户在视频预览上框选的矩形（显示坐标）与 crop 比例 [x,y,w,h]（0~1） +// 互转，映射基于视频固有分辨率并处理 object-fit: contain 的留边（letterbox）。 + +// 计算