v0.4.3 — 工程底座:转写缓存 + 处理产出回执
可靠性与透明度 / Reliability & transparency
两块「完全托管」的地基:让重复使用更快,让 AI 的处理过程可审计。
转写结果本地缓存 / Persistent transcript cache
转写是整条流水线最慢的一步。结果现在按(文件 size+mtime,引擎)缓存在本地——同一个文件下次再打开、想换设置多切几条,跳过重转写、秒进挑爆点;云端引擎(ElevenLabs)命中缓存还顺带省一次 API 费用。文件被改动(大小/修改时间变化)或更换引擎会自动失效;缓存带 LRU 上限、故障静默回退绝不阻断真实转写。
Transcription is the slowest step. Results are cached locally by (file size+mtime, engine): re-open the same file to cut more clips and it skips straight to highlight selection; a cloud engine (ElevenLabs) even saves an API charge on a hit. Editing the file or switching engines invalidates automatically; the cache is LRU-bounded and fail-open.
clips.json 处理产出回执 / Processing receipt in clips.json
每条切片的 clips.json 现在多一个 render 字段,如实记录 AI 到底做了什么:有效字幕样式、取景是跟脸(face-track)还是回退到中心裁、跳剪的剪除比例与拼接段数、剪掉了几个口头禅。矩阵/CMS 流水线可据此审计,也能一眼看出哪条片子触发了降级回退。
Every clip's clips.json now carries a render block recording exactly what the pipeline did: effective caption style, whether the reframe followed a face or fell back to center-crop, the jump-cut ratio and splice count, and how many fillers were removed — auditable by CMS/matrix pipelines, and a quick signal for which clips hit a fallback.
本地处理 · 免 Key · 无水印。All local · no key · no watermark.