LLM human data · हिन्दी
Hindi Human Data for LLM Projects
Human-generated text and speech for LLM training and evaluation in Indian languages: prompts, preference rankings, instruction-response pairs, red-teaming and cultural-fit review. This page covers how that works specifically for Hindi, where four-way stop contrast (voiced/voiceless x aspirated/unaspirated) that collapses in models trained on english-first acoustic units.
- Language
- Hindi (hi-IN)
- Dialects covered
- 7
- Typical programme
- 500-2,000 hours
- Cities
- Delhi, Lucknow, Jaipur
What changes when the language is Hindi
The service specification stays constant across languages; the linguistics do not. For Hindi, three things drive the design of a llm human data programme.
- Four-way stop contrast (voiced/voiceless x aspirated/unaspirated) that collapses in models trained on English-first acoustic units
- Dialect spread: 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.
Technical specification
| Parameter | Standard |
|---|---|
| Task types | Prompt writing, response ranking, instruction-response pairs, adversarial testing |
| Languages | Any language in the network, including code-mixed Hinglish |
| Contributors | Screened by domain, education band and language proficiency |
| Agreement | Overlapping assignments with adjudication |
| Provenance | Per-item contributor and time records |
Hindi cohort design
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 |
Process
- Task specification and rubric design
- Contributor screening against the rubric
- Calibration round with feedback
- Production with overlap and gold items
- Adjudication and delivery
Hindi-specific quality rules
- Inconsistent Devanagari vs romanised spelling for the same English loan word
- Nukta characters (क़ ख़ ग़ ज़ फ़) applied inconsistently by transcribers
- Numerals: whether to write digits, Devanagari numerals, or spelled-out words must be fixed in the style guide up front
- Honorific verb forms create long agreement chains that annotators shorten unless the guide forbids it
Every item is traceable to a screened contributor, which matters when a model vendor audits your data provenance.
Deliverables
- Task data in your schema
- Rubric and calibration results
- Contributor metadata (anonymised)
- Agreement statistics
Worked example
A representative Hindi llm human data engagement: 500 hours from 1,000 speakers, 50/50 gender, ages 18-45, spread across Delhi, Lucknow, Jaipur, recorded to the specification above and delivered in WAV with a per-utterance manifest.
Timeline: 2-6 weeks depending on task complexity and contributor screening depth.
Where this data is missing today
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.
Frequently asked
How much does Hindi llm human data cost?
Priced per delivered hour or unit against a written spec. The cost drivers for Hindi are dialect spread, demographic narrowness and recording condition, in that order.
Which Hindi dialects are included?
By default Khari Boli, Awadhi, Braj, Bhojpuri-influenced Hindi and others, tagged per speaker. You can also commission a single-dialect corpus if you are targeting one region.
Can you deliver Hindi data in our format?
Yes. Task data in your schema is the default, but naming, schema and directory structure follow your pipeline.
How long does a Hindi programme take?
2-6 weeks depending on task complexity and contributor screening depth.
Request a Hindi llm human data quote
Hours, speakers, dialects, deadline. Send what you have.