DesiCaptionsManual Subtitles vs Auto Captions
Typing subtitles by hand is accurate and slow. Auto captions are fast and need checking. The best answer is usually both, in that order.
Short answer
Typing and timing subtitles by hand takes roughly 10 to 20 times the length of the video for most creators: 10 to 20 minutes for a one-minute Reel, one to two hours for a five-minute video.
Auto captions plus a proofread take roughly 2 to 3 times the length of the video. Use auto captions for almost everything, then fix names, numbers and slang by hand. Type from scratch only for songs, very noisy audio, or captions that are part of the design.
If you have been typing your own subtitles, you already know the cost: every minute of video turns into a long session of play, pause, type, rewind, and drag caption edges onto the right frame. The question is whether automatic captions are good enough now to stop, and for what kind of video.
How long manual subtitles take
Manual subtitling is two jobs. Transcribing means typing every word, which for conversational speech runs at about four to six times the video length even for a fast typist, because people speak faster than anyone types and you keep rewinding. Timing means setting where each caption starts and ends, which roughly doubles it. Word-by-word styles, where each word lights up as it is spoken, need a timestamp on every single word and are not realistic by hand at all.
Rough totals, for a creator who is not a professional captioner:
| Video length | Typed and timed by hand | Auto captions plus a proofread |
|---|---|---|
| 30-second Reel | 5 to 10 minutes | About 2 minutes |
| 60-second Short | 10 to 20 minutes | 3 to 4 minutes |
| 5-minute video | 1 to 2 hours | 10 to 15 minutes |
| 15-minute video | 3 to 5 hours | 30 to 45 minutes |
The auto column includes the processing wait and a careful read-through against the audio. On DesiCaptions a 5-minute video is typically processed in 2 to 3 minutes; the rest of that time is you checking it.
Where typing them yourself still wins
- Songs and lyrics. Speech recognition is trained on speech. Singing over music trips it up, and you probably know the lyrics anyway.
- Very noisy audio. Crowds, wind, traffic, two people talking over each other. If you have to strain to hear a word, so does the AI.
- Captions as design. If each caption is placed, sized and animated by hand as part of the edit, you are designing, not transcribing, and the text is the small part.
- Exact wording that carries risk. A legal statement, a medical claim, a quote from someone else. Proofread an auto transcript word for word, or type it.
Where auto captions win
- Volume. Posting daily means the hours of manual captioning add up to a part-time job.
- Timing. Automatic timing is tighter than most people manage by hand, and word-level timing makes karaoke and word-pop styles possible.
- Consistency. The same word is spelled the same way in every video, which matters for Hinglish, where "kya", "kyaa" and "kia" all turn up in hand-typed captions.
- Long videos. The longer the video, the bigger the gap between the two columns in the table above.
The workflow that works
Generate first, then proofread. Do not start from a blank file. A proofread pass is three checks:
- Names, brands and numbers. These are what any speech model gets wrong most often, because they are rare words. Fix each one once.
- Slang and code-switching. In Hinglish, check that English words stayed English and Hindi words are spelled the way your audience types them.
- Cuts. Watch the captions across every edit point. If you trimmed the video after making the captions, regenerate them from the final export rather than nudging every line.
Make captions from the final export. Most "my captions are out of sync" problems come from captioning a draft cut and then editing the video afterwards. See why subtitles go out of sync after export.
Hindi and Hinglish change the math
Typing Hinglish by hand is slower than typing English, because there is no fixed spelling to fall back on and you make a spelling decision on almost every Hindi word. Auto-captioning Hinglish used to be its own problem, because tools built for one language garbled the mix. A tool that transcribes code-switched speech as one stream, and writes it in Roman script, removes both problems: the spelling is decided once, consistently, and you only fix what it misheard.