The short answer
No captioning tool handles Hinglish well, including this one, and you should be suspicious of any that claims otherwise. What varies is how fast you can fix the draft.
Pick a tool that recognises the Hindi as Devanagari and romanizes from there rather than guessing Latin spellings straight from the audio — that is what makes the same word spell the same way throughout your video. Then review in a fixed order: English words inside Hindi sentences first, then names, then numbers. On a 45-second clip that is about three minutes of work.
What actually breaks inside the model
Speech recognition combines two things: what the audio sounds like, and what is statistically likely to come next. The second part is what makes it work at all on real recordings, because the raw acoustic signal is frequently ambiguous.
Code-mixing attacks exactly that second part. A model processing "toh basically mera point ye hai ki consistency matters" has to decide, at every word boundary, which language it is now hearing. Each decision changes which vocabulary and which statistics apply to the next word. Get one wrong and the context that should rescue the following word is now pulling in the wrong direction.
It is worse than running two languages separately, because the switch points are unpredictable. English nouns take Hindi grammar. Hindi verbs govern English objects. The switch can happen twice in one clause, and it happens differently for every speaker. There is no pattern for the model to settle into.
This is why a tool that is excellent at Hindi and excellent at English can still be poor at Hinglish. The difficulty is not in either language — it is in the decision between them, made hundreds of times per minute.
Romanizing from Devanagari versus guessing from audio
There are two ways to produce Hinglish in Latin script, and the quality gap between them is the single biggest technical choice in this problem.
The direct way has the model write Latin characters straight from the audio. It seems simpler and it is noticeably worse, because the model is now doing two uncertain jobs at once: identifying the word, and inventing a spelling for it. Romanized Hindi has no agreed standard — "kyun", "kyon" and "kyu" are all in use — so the model has nothing to anchor to.
The symptom is inconsistency, and it is more annoying than outright errors. The same word appears three different ways in one video. You fix it in caption four and it is wrong differently in caption eleven. There is no find-and-replace that catches them all because they are not the same string.
The two-step way recognises the speech as Devanagari first, where the model is working in the script Hindi is genuinely written in and has real orthographic ground truth, then transliterates that Devanagari to Latin by a fixed rule. Because the romanization is a deterministic mapping applied to text rather than a guess from sound, a given Hindi word comes out identically every time. You fix a spelling preference once.
This is the approach CaptionFX uses, and it is worth checking which one any tool you evaluate is doing — export a clip where a common Hindi word repeats several times and see whether it is spelled the same way each time.
Devanagari or Latin: pick by audience
Neither script is more correct for Hinglish. They serve different viewers, and the mistake is picking by instinct rather than by who is watching.
Latin script keeps a mixed sentence in one alphabet. "Toh basically mera point ye hai" reads in a single pass. The mixed-script alternative — "तो basically मेरा point ये है" — makes the eye switch alphabets twice in one line, which costs real reading time when the caption is on screen for under two seconds.
Devanagari wins for an audience that is comfortably Hindi-literate and for content that is mostly Hindi with only occasional English. It reads more naturally and it looks less like a chat message, which matters for educational or professional content.
The rough rule: short-form social content aimed at younger urban or diaspora viewers tends to work better romanized. Longer-form content for a Hindi-first audience tends to work better in Devanagari. If most of your sentence is already English words, romanized is almost always the right call.
A three-minute review method
Because Hinglish always needs review, the speed of the review is what actually matters. Reading every caption with equal attention is the slow way and it catches less.
Go in this order. First, the English words sitting inside Hindi sentences — this is where the language decision breaks, so it has by far the highest error rate. Second, proper nouns: names, brands, places. Third, numbers. Fourth, anything field-specific.
Do it with the video playing rather than in a transcript window. For Hinglish this matters more than for a single-language clip, because judging whether a word should have been written as Hindi or English often needs the surrounding sentence and the speaker's delivery — information a text box does not give you.
And use word-level editing where it exists. A single wrongly-romanized word should be one correction, not a retyped line. Hinglish speech also carries more false starts and filler than scripted English, so being able to cut a single word out of the audio and video comes up constantly.
Recording better is worth more than switching tools
The gap between a good and a mediocre captioning tool on Hinglish is smaller than the gap between good and bad audio. Both matter, but only one is under your direct control and it is not the software.
Close mic. A clip-on lavalier beats a phone across the room by a wide margin, because proximity raises the voice relative to the room. Record somewhere with soft surfaces, since reverb smears the boundaries between words and word boundaries are precisely where the language decision happens. Keep background music off the voice track until after captioning. One speaker at a time.
And say your unusual words clearly. Names, brands and technical terms are the words with no statistical safety net in any language, so the acoustic signal is all the model has to work with.