Ask a generic speech model to transcribe a Hindi vlog and you will see the problem fast: dropped words, English phrases mangled into Devanagari, and Urdu vowels guessed wrong. In 2026 the gap between generic and Indic-specialist models is still large — and measurable.
The numbers
On standard Hindi benchmarks, general-purpose models hover around a 25–30% word error rate. Speech models trained specifically on Indian audio — over a million hours of real accents, telephony, and code-mixed speech — bring that down to roughly 10–13%. That is the difference between fixing every third caption line and shipping most lines untouched.
Why Hindi and Urdu are hard
- Code-mixing — everyday speech switches between Hindi/Urdu and English mid-sentence
- Script ambiguity — Urdu’s Nastaliq script omits short vowels, so romanization needs context
- Accent range — Delhi, Lahore, Hyderabad, and diaspora accents differ widely
What Hatcut uses
For Hindi, Urdu, and 20 other Indian languages, Hatcut routes transcription to Indic-specialist speech AI and can output native script or romanized captions directly. For other languages it uses Whisper. You always get word-level timing for caption sync.
Test it free: paste a video URL into the transcript generator or start a project at hatcut.com.