Swap the top and bottom text blocks in bilingual SRT subtitles — structurally, without language detection or guessing.
Fix SRT subtitle timing with precise offset, FPS conversion, and two-anchor drift correction. Deterministic, local, and fail-safe — no AI guessing.
Turn a script into a vertical narrated short with voice and subtitles that light up word by word. Speech recognition recovers the timings from the generated audio, so captions never drift when you change the voice, the speed, or a line.
Check SRT files in batches, get cue-level QA reports, and export conservative repair copies while preserving your originals. Includes explicit time shifts, overlap trimming and two-line track splitting. English edition. Python 3.10+; no API key needed for the local engine.
Batch validate, clean, normalize, swap, and convert subtitle files with deterministic processing and failure-safe reports.
Check existing SRT subtitles for timing errors, overlaps and readability warnings. Get source-linked HTML, Markdown and JSON reports plus an optional format-only normalized SRT. Python 3.10+, no third-party packages or script API key. No transcription, translation or automatic retiming.
영상 하나를 넣으면 faster-whisper로 단어 단위 STT를 뽑고, 방송 자막 가이드 수준 규칙(최대 2줄, 줄당 글자수, 초당 글자수 CPS, 최소·최대 노출시간)으로 큐를 다시 나눕니다. 실측 샘플(41.78초)에서 whisper 원시 세그먼트 7개(최대 51자 한 줄)가 재세그먼트 큐 12개(최대 18자, 2줄)로 바뀌었고 STT 단어 정확도는 98.4%였습니다. 번역은 에이전트가 작업지를 채우는 방식이라 API 키가 필요 없고, 원본과 큐 개수·타임코드를 스크립트가 기계적으로 대조 검증해 하나라도 어긋나면 파일을 만들지 않습니다. 완성된 자막은 outline/box 스타일로 Pillow+ffmpeg overlay 번인까지 만들고, QC 스크립트로 줄 겹침·CPS 초과·좁은 간격을 검사해 세 언어 모두 위반 0건(PASS)을 받았습니다.
Enforce consistent names, terms, and recurring corrections across SRT subtitles using your own glossary. Review matches before applying changes — deterministic, local, and no AI guessing.
A free retention check for one published short video: paste your average percentage viewed and get a PASS / REWORK / REBUILD verdict with the threshold shown, plus the single biggest retention leak named and one concrete fix.