LLM human data · ગુજરાતી
Gujarati 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 Gujarati, where murmured (breathy-voiced) vowels are phonemic in gujarati and are absent from most shared indic acoustic models.
- Language
- Gujarati (gu-IN)
- Dialects covered
- 5
- Typical programme
- 250-1,000 hours
- Cities
- Ahmedabad, Surat, Vadodara
What changes when the language is Gujarati
The service specification stays constant across languages; the linguistics do not. For Gujarati, three things drive the design of a llm human data programme.
- Murmured (breathy-voiced) vowels are phonemic in Gujarati and are absent from most shared Indic acoustic models
- Dialect spread: Standard (Amdavadi), Surti, Kathiyawadi, Kachchhi-influenced
- Business and trade vocabulary is heavily English; Gujarati diaspora speech adds further English structure. Specify whether diaspora speakers are in or out of scope.
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 |
Gujarati cohort design
Surat and Rajkot recruitment is essential for dialect coverage; Ahmedabad-only cohorts sound uniform.
| Dimension | Typical split | Why it matters for Gujarati |
|---|---|---|
| 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 Gujarati forms that younger urban speakers have lost |
| Region | Gujarat / Daman & Diu / Dadra & Nagar Haveli and others | Dialect spread across 5 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
Gujarati-specific quality rules
- Breathy vowels have no consistent orthographic marking
- Kathiyawadi lexical items replaced with standard equivalents
- Numerals and currency in trade speech written inconsistently
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 Gujarati llm human data engagement: 250 hours from 500 speakers, 50/50 gender, ages 18-45, spread across Ahmedabad, Surat, Vadodara, 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
Very little spontaneous Gujarati audio exists publicly; nearly all of it is Ahmedabad read speech.
Frequently asked
How much does Gujarati llm human data cost?
Priced per delivered hour or unit against a written spec. The cost drivers for Gujarati are dialect spread, demographic narrowness and recording condition, in that order.
Which Gujarati dialects are included?
By default Standard (Amdavadi), Surti, Kathiyawadi, Kachchhi-influenced and others, tagged per speaker. You can also commission a single-dialect corpus if you are targeting one region.
Can you deliver Gujarati 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 Gujarati programme take?
2-6 weeks depending on task complexity and contributor screening depth.
Request a Gujarati llm human data quote
Hours, speakers, dialects, deadline. Send what you have.