Batch validate, clean, normalize, swap, and convert subtitle files with deterministic processing and failure-safe reports.
Paste SRT subtitle text and get a cleaned SRT back — lines reflowed at natural phrase breaks, cue numbers and timestamps unchanged, plus a flagged-cue report with each fast cue's reading speed and the fix to make first.
Check your .srt or .vtt captions before you post. An offline script finds exact cues with reading speed, line length, overlaps, timing errors, broken characters and script mismatches, fixes the mechanical problems without changing a word, and re-checks. It cannot hear audio or test platform acceptance. Download package: bring your own AI agent and Python 3.
한국어 강의·인터뷰·팟캐스트·브이로그 MP4·MOV 한 편에서 내용이 독립적으로 통하는 15~60초 구간 3~5개를 골라 9:16 MP4로 만듭니다. 한글 자막과 상단 제목을 영상에 넣고 SRT도 따로 드립니다. 얼굴이 잡히면 위치를 표본 추적해 세로 크롭하고, 없으면 원본과 흐린 배경을 함께 씁니다. 각 클립의 제목·선정 이유·해시태그를 TXT로 정리하며 음량은 -14 LUFS로 맞춥니다. 최대 10분이며 받아쓰기와 선택 결과는 게시 전에 확인해 주세요.
Paste your prompt and what went wrong (flicker, extra limbs, warped faces, floating specks, objects in the wrong place) and get a diagnosis, a rewritten prompt, two variants and a QC checklist.
Fix SRT subtitle timing with precise offset, FPS conversion, and two-anchor drift correction. Deterministic, local, and fail-safe — no AI guessing.
Hands-on prompt engineering and LLM evaluation. Built around a diagnose-then-ship loop: forensic transcript critique ▎ that names the exact failure mode, paired with a fixer that produces complete, deployment-ready rewrites — not ▎ vague advice.
Batch-export selected video clips with correctly retimed SRT subtitles and a complete delivery report.