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.
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.
Swap the top and bottom text blocks in bilingual SRT subtitles — structurally, without language detection or guessing.