Hinglish caption generator
Generate timed captions for Hindi-English video and export an SRT file. The mixing is the easy part. The part that makes captions look amateur is the same word spelled three ways in one file.
Devanagari settles how a Hindi word is written. Roman letters do not. Nothing decides between kya, kia and kyaa; between nahi, nahin and nahee; between accha, acha and achha; between bohot, bahut and bohut. All of them are readable and none of them is wrong.
Which is fine in a chat message and a problem in a caption file, because a draft picks whichever spelling best fits the audio at that moment. The result is one word written three ways across a three-minute video. Individually every cue looks fine. Read end to end, the file looks careless, and it is the first thing a Hindi-reading viewer notices.
So the fix is a decision rather than a correction. Before editing, pick a spelling for the ten or fifteen words you say constantly, then hold it. The subtitle spelling checker groups the words your file spells more than one way and replaces a variant across every cue without touching timing, which turns that pass from a manual search into a few clicks. It is the difference between captions that read as published and captions that read as generated.
A Hinglish sentence can be captioned three different ways, and they are not interchangeable.
All Devanagari, with English words transliterated into Devanagari script. It reads as one coherent language and suits an audience that reads Hindi comfortably. It also makes English brand names look strange, because a viewer who knows the brand expects to see it in Latin letters.
All Roman, with Hindi romanized. It reaches the widest audience, including people who speak Hindi but read it slowly, and it is what most short-form creators use. The cost is the spelling problem above, which lands entirely on this option.
Mixed script, Hindi in Devanagari and English in Latin. This is closest to how people actually type, and it keeps brand names right, but some readers find the alternation unsettled to read at caption speed.
Pick before you start editing, not after. Switching later means re-reading every cue, and a file that is half-converted is worse than either option done consistently. The longer guide on choosing between Hinglish and Devanagari walks through the trade-off with more examples.
It is tempting to assume that a tool supporting Hindi and a tool supporting English will between them handle a sentence containing both. In practice they pull in opposite directions. Recognition anchored on Hindi tends to force English words into Hindi shapes; recognition anchored on English drops or mangles the Hindi it does not expect. Neither is a bug exactly, and both produce a draft that needs work.
This is increasingly recognised in the tooling itself: preserving code-switching is now stated as an explicit design goal in speech pipelines rather than assumed to fall out of supporting both languages, and speech vendors name Hinglish as a separate target alongside Hindi. It matches what creators independently work out, which is that a single automatic pass rarely lands and the draft needs a human read before it goes out.
Which is the argument for an editable draft rather than burnt-in captions generated in one shot. Fix the wording while it is still text.
Names and brands first. Proper nouns are where recognition is least reliable in any language and where your audience is most likely to notice.
Then spelling consistency. The ten or fifteen words you repeat. Make them agree.
Then timing against the speech. Read the cue while listening, not against the clock. If captions drift uniformly, the timing shifter fixes the whole file at once.
Line lengths last. Fixing wording changes line lengths, so measuring earlier wastes the pass. The validator reports lengths and reading speed, and the line breaker re-breaks anything over the limit without touching timing. Both count Devanagari the way a reader sees it rather than by code units, which most tools get wrong and which makes Devanagari lines measure at roughly double their real width.
Export SRT and keep it. YouTube, Instagram, LinkedIn and every editor accept it, and a file you hold can be corrected, converted and re-uploaded later, which burnt-in captions cannot. For a web player, convert onward with the SRT to VTT converter. For show notes or a blog post, the SRT to text converter strips the timing and leaves a transcript.
Styled captions burnt into the video are worth doing as well as the file, not instead of it, when the platform autoplays without captions on. The comparison of hardcoded and soft subtitles covers when each one is right.
If the speech is closer to Urdu or Hindustani and the audience reads Latin letters, start from the Roman Urdu caption generator instead, since the everyday vocabulary overlaps heavily. For Urdu script, use the Urdu subtitle generator. For Marathi, which shares Devanagari with Hindi and drifts toward it in drafts, see the Marathi page. The South Asian language hub lists every language covered here and how each one fails differently.
Yes. Generate the captions, review them, and export SRT. SRT is the format to keep, because YouTube, Instagram, LinkedIn and every editor accept it, and because a file you hold can be corrected and re-used later. WebVTT is there too for web players, and a plain transcript if you need the text without timing.
Three, and picking one is a decision rather than a preference. You can put everything in Devanagari, put everything in Latin letters, or keep each language in its own script. The last is closest to how people type and the first reads as one coherent language. Whichever you choose, choose it before editing, because changing your mind means going through every cue again.
Because romanized Hindi has no standard spelling. Nothing decides between kya, kia and kyaa, or between nahi, nahin and nahee, so a draft picks whichever fits the audio in that moment and you end up with the same word written three ways in one file. This is the single most common fault in Hinglish captions and it is the thing to check first.
Not in practice. Recognition trained on Hindi tends to force English words into Hindi shapes, and recognition trained on English drops Hindi words it does not know. Tools increasingly treat preserving the code-switching as its own problem rather than something that falls out of supporting both languages, which matches what creators find: one pass rarely gets it right, and the draft needs a read.
In everyday use, nothing. Strictly, subtitles carry dialogue for someone who cannot follow the language, and captions also carry sound information for someone who cannot hear. For social video the words are used interchangeably and both mean the same file.
In AutoCaption they do now. Most tools measure a line with a string length that counts code units, which over-counts Devanagari because a syllable with a vowel sign is several code units and one visual unit. Our validator and line breaker count grapheme clusters instead, so a Devanagari line is measured the way a reader sees it rather than at roughly double.
If the speech is closer to Urdu or Hindustani and your audience reads Latin letters, yes, start there. The two overlap heavily in everyday vocabulary. Switch back for anything with Sanskritised Hindi vocabulary, which a Roman Urdu draft will handle less well.
Generate the draft, settle the spelling, then export an SRT file you keep.