AI Subtitle Generation

Transcription-based captioning runs the audio through a speech-to-text model and prints whatever it heard. It is the only option when you are captioning footage somebody else shot, and it will misspell every proper noun in your niche at least once.

Script-based captioning skips the guessing entirely. The caption text is the script, which already exists, so the only open question is timing. Orange runs a word-timestamp pass over the generated audio and aligns it against the narration it already knows, which means the words are right by construction and only the milliseconds are estimated. Names, product names, and acronyms come out correct on the first render.

Word-level timing is also what makes the karaoke style possible: one word lighting up at a time, in sync with the voice. That is more than decoration. Captions that move hold the eye through a sentence in a way static two-line blocks do not, and eye movement is the mechanism behind the retention numbers.

Those numbers are the reason to bother at all. Around 85% of social video gets watched with the sound off, and captioned video sees up to 40% more watch time than the same cut without. On a 60-second video that gap is whole extra seconds per viewer, multiplied by everyone the feed reached. Watch time is the input recommendation systems weigh most heavily, so captions compound into distribution rather than sitting in the accessibility column.

Two things still want a human eye. Line breaks land badly on long compound words, and a figure written as digits reads differently on screen than it sounded in the voice. Both take seconds to fix, and neither is worth a second render to avoid.