Upload your finished Short, Reel, or TikTok. Get timestamped checks and fixes for captions, on-screen text, and visual mistakes before you publish.
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.
Paste a video title and summary and get a thumbnail brief, a four-reviewer mobile legibility check with an issues table, and a generator-neutral image prompt.
Paste the text of an SRT subtitle file and get it back with lines reflowed for readability: split at natural phrase breaks, capped at your line length, with a flag on every cue that reads too fast for its duration. Your cue numbers and timestamps are kept exactly as pasted. It works on pasted text only. It does not transcribe audio, translate, burn captions into video, or upload files.
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.
Upload a podcast or long-video transcript and get ~10 ready-to-cut clips for Shorts, TikTok & Reels: exact timecodes, explainable scores, a reused-content risk check, editor files, a cutting script and vertical-video captions.
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.
YouTube video-to-text tool for students turning lectures into notes, podcast listeners hunting a quote, and anyone tired of scrubbing long videos. Paste any link — tutorials, talks, podcasts, lectures, or interviews — and get a clean verbatim transcript with AI summary and key points, often under two minutes. Output: plain text, timestamped Markdown, or SRT subtitles. Transcripts stay in the source language so quoted lines stay accurate, while summaries auto-translate to your chat language.
Give it a blog post or article and it writes a script with a hook and 5 to 8 beats. Two local Python scripts do the rest: edge-tts voices each sentence, then Pillow and ffmpeg combine a Ken Burns background (your image or a gradient), keyword-highlight captions and a progress bar into a 1080x1920 video. In our sample, an 870-character Korean article became a 48.3-second video. No API key needed; with BGM, sidechain ducking is automatic.