LLM human data · తెలుగు
Telugu 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 Telugu, where vowel-length contrasts are phonemic and short/long confusion changes meaning outright.
- Language
- Telugu (te-IN)
- Dialects covered
- 4
- Typical programme
- 500-2,000 hours
- Cities
- Hyderabad, Vijayawada, Visakhapatnam
What changes when the language is Telugu
The service specification stays constant across languages; the linguistics do not. For Telugu, three things drive the design of a llm human data programme.
- Vowel-length contrasts are phonemic and short/long confusion changes meaning outright
- Dialect spread: Telangana, Coastal Andhra (Godavari), Rayalaseema, Srikakulam
- Hyderabad speech mixes Telugu, Urdu/Deccani, Hindi and English. A Telugu dataset for Hyderabad deployment must include Urdu-origin vocabulary.
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 |
Telugu cohort design
Split cohorts explicitly between Telangana and Andhra Pradesh and tag every speaker; models trained without the tag cannot be evaluated per region.
| Dimension | Typical split | Why it matters for Telugu |
|---|---|---|
| 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 Telugu forms that younger urban speakers have lost |
| Region | Andhra Pradesh / Telangana / parts of Karnataka and Odisha and others | Dialect spread across 4 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
Telugu-specific quality rules
- Telangana forms normalised to Coastal Andhra standard
- Long/short vowel marking errors under time pressure
- Urdu loanwords rendered inconsistently in Telugu script
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 Telugu llm human data engagement: 500 hours from 1,000 speakers, 50/50 gender, ages 18-45, spread across Hyderabad, Vijayawada, Visakhapatnam, 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
Coastal Andhra read speech dominates. Telangana rural and Rayalaseema speech is thin, despite Hyderabad being the largest deployment market.
Frequently asked
How much does Telugu llm human data cost?
Priced per delivered hour or unit against a written spec. The cost drivers for Telugu are dialect spread, demographic narrowness and recording condition, in that order.
Which Telugu dialects are included?
By default Telangana, Coastal Andhra (Godavari), Rayalaseema, Srikakulam and others, tagged per speaker. You can also commission a single-dialect corpus if you are targeting one region.
Can you deliver Telugu 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 Telugu programme take?
2-6 weeks depending on task complexity and contributor screening depth.
Request a Telugu llm human data quote
Hours, speakers, dialects, deadline. Send what you have.