Speech AI Companies · Hindi
Hindi Training Data for Speech AI Companies
Teams whose core product is speech recognition or synthesis, where dataset quality is the product roadmap and word error rate is the metric everyone watches. For Hindi specifically, the work is shaped by 7 dialect varieties and by how much English enters the speech.
- Buyer profile
- Speech AI Companies
- Language
- Hindi (hi-IN)
- Typical ask
- 500-2
Your problem
- WER on Indian languages is dominated by dialect and code-mixing failures that generic corpora do not cover
- Public Indic corpora are read speech and do not transfer to spontaneous production audio
- Benchmark sets leak speakers into training splits, inflating reported accuracy
What Hindi requires
- Four-way stop contrast (voiced/voiceless x aspirated/unaspirated) that collapses in models trained on English-first acoustic units
- Dialects: Khari Boli, Awadhi, Braj, Bhojpuri-influenced Hindi
- Urban Hindi speech is Hinglish in practice. Expect 15-40% English tokens in spontaneous speech: numbers, brands, technology terms, and whole clause switches. Any Hindi corpus that excludes English tokens will not match production traffic.
- Public Hindi corpora skew heavily towards read newspaper text from educated urban speakers in Delhi and NCR. Rural Bihar and eastern UP speech, elderly speakers, and low-literacy speakers reading prompts aloud are largely absent.
How you will evaluate the delivery
- Are train/dev/test splits speaker-disjoint by construction?
- Is transcription verbatim, with disfluencies preserved?
- Is per-token language ID available for code-mixed speech?
- Is inter-annotator agreement measured and reported?
Recommended cohort
Largest recruitment pool in the network. A 1,000-speaker Hindi cohort with balanced gender and 18-45 age bands is typically fielded across four cities to avoid a single-city accent bias.
| Dimension | Typical split | Why it matters for Hindi |
|---|---|---|
| Gender | 50 / 50 | Pitch range differences change acoustic model behaviour; unbalanced cohorts bias recognition |
| Age | 18-25: 30%, 26-40: 40%, 41-60: 30% | Older speakers retain conservative Hindi forms that younger urban speakers have lost |
| Region | Uttar Pradesh / Bihar / Madhya Pradesh and others | Dialect spread across 7 recognised varieties |
| Education | Mixed, including below-graduate | Prompt-reading fluency correlates with education and skews prosody |
| Condition | Studio / quiet room / field | Match the noise profile of your deployment |
Contract points
- Speaker-disjoint splits guaranteed contractually
- Right to publish benchmark results
- Re-record remedy for QA failures
Example requirement
"We need 2,000 hours of Hindi from 3,000 speakers, 50/50 male-female, ages 18-45, studio quality, scripted plus spontaneous, delivered in WAV with transcripts."
That sentence is enough to produce a quote and a timeline. Anything missing, we will ask about once.
Frequently asked
Do you have Hindi capacity available now?
Largest recruitment pool in the network. A 1,000-speaker Hindi cohort with balanced gender and 18-45 age bands is typically fielded across four cities to avoid a single-city accent bias. Fielding usually starts one to two weeks after the specification is signed.
Can you work white-label?
Yes, including QA reporting written so it can be passed to your end client unchanged.
What licensing applies to Hindi data?
Perpetual and transferable, with participant consent covering model training and downstream distribution. Speaker-disjoint splits guaranteed contractually is addressed in the master agreement.
Request a Hindi quote
Send the spec. You get scope, timeline and price.