If you work across two languages in one afternoon, you already know the failure. You dictate a sentence that starts in English, slips into Hindi for the part that only works in Hindi, and the tool returns something that is neither. So you stop dictating and go back to typing, which is the outcome nobody measures.
Hinglish is not a mistake to be corrected
A lot of speech tools are built as though every sentence belongs to exactly one language. That is a reasonable assumption in some places and plainly wrong in others. Switching mid-sentence is not sloppiness, it is how a very large number of people actually speak, and a tool that treats it as noise is unusable for them however good its English is.
Zen Whisper exposes 110 spoken language options in the app, including Hindi, Hinglish, English regional variants, and many Indian, regional, and Western languages, depending on the speech model you select.
Why running locally matters more, not less
There is a second reason this matters beyond privacy. Cloud speech services tend to be tuned hardest for the languages with the largest paying markets, and quality falls away at the edges. Choosing your own speech pack and running it on your Mac puts that choice in your hands rather than in a provider's roadmap.
Core speech recognition runs on your Mac. The audio of you dictating a family matter in Hindi does not need to become somebody else's training data for that to be a good outcome.
What this looks like on a normal day
- Pick the speech pack that matches how you actually talk, and download it once.
- Hold the shortcut in whatever app you are already in, and speak.
- The text lands at your cursor, formatted for where you are: casual in a chat, formal in mail.
- Come back later through searchable transcript history rather than hunting for the window.
Where this will disappoint you
- Accuracy varies by language and by model. A smaller pack is faster and less accurate, and the difference is more noticeable outside English.
- Heavily mixed sentences remain the hardest case in any speech system. This handles them better than tools that assume one language per sentence, not perfectly.
- Mac only, Apple Silicon, macOS 14 or later.
There is a 14-day free trial and a free tier of 3,000 words a week afterwards, which is enough to find out whether it handles the way you speak before paying anything. It is on Gumroad.
Why Hinglish is hard for speech recognition specifically
Most speech systems are built around a language choice made before you speak. That assumption holds for a monolingual speaker and breaks immediately for the way a lot of India actually talks, where a single sentence carries an English verb, a Hindi structure and a proper noun that belongs to neither.
- Code switching happens mid sentence, not between sentences, so a system that picks a language per utterance is already wrong.
- The same word can be written in Devanagari or in Roman script, and speakers switch script by context rather than by rule.
- Romanised Hindi has no fixed spelling. Nobody agrees on whether it is "kyun", "kyon" or "kyu", including the same person twice.
- English words in Indian speech carry Indian vowels, which pulls them away from the training distribution of a general English model.
What to expect, honestly
Recognition of clean Hindi is good. Recognition of clean Indian English is good. Recognition of a sentence that switches between them is the interesting case, and it is where you should set expectations rather than assume either extreme.
Where the errors actually land
| Input | Typical outcome | What to do about it |
|---|---|---|
| Hindi sentence, Devanagari expected | Reliable | Nothing |
| Indian English, technical vocabulary | Reliable, except product names | Add the names to a custom dictionary |
| Mixed sentence, natural speed | Mostly right, script may be inconsistent | Decide one target script and edit the rest |
| Names of people and places | The weakest area, by some margin | Dictionary entries, every time |
Practical habits that help more than settings do
- Decide the output script before you start, because mixed script output is much harder to clean up afterwards than a consistent one.
- Put proper nouns into a custom dictionary once. Names are the single biggest source of errors and the easiest to fix permanently.
- Slow down slightly at the switch points rather than throughout. The joins are where the model has least context.
- Dictate punctuation explicitly in long passages. It is faster than adding it afterwards and it gives the model sentence boundaries to work with.
- Read the numbers back. Digits, dates and amounts are the errors that survive a proofread because they look plausible.
None of this is specific to any one tool. It is the difference between treating dictation as a typewriter that listens, which disappoints, and treating it as a first draft that arrives faster than your hands, which is what it actually is.
