Hindi is the fourth-most-spoken language in the world. It also has some of the worst voice AI coverage of any major language. The reason isn't lack of data — it's that off-the-shelf TTS and STT models are trained on read speech in standard Hindi, while real conversations involve regional accents, English loanwords, and rapid code-switching.
What breaks first
When you take a Whisper or generic Hindi TTS into production, three things fail almost immediately:
- Proper nouns — names of people, brands, and places get mangled, because the model has never seen them in its training data.
- Numbers and digits — "9876543210" gets read as a date or chopped into smaller numbers. Customers spelling out a phone number trip up most STT models.
- Code-switching — a Hindi sentence like "Mera account number kya hai?" contains an English noun ("account number") that the model either skips or transliterates badly.
What TVoice does differently
We fine-tune on real customer-support recordings (not read speech), include a proper-noun lexicon, and handle code-switching at the language-modeling layer rather than relying on the acoustic model to figure it out. The result is a voice agent that sounds like the people it's talking to.
If you're building in Hindi (or any Indic language), talk to us.