LLM human data · বাংলা
Bengali 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 Bengali, where inherent vowel is realised as /ɔ/ or /o/, which breaks g2p rules copied from devanagari-based systems.
- Language
- Bengali (bn-IN)
- Dialects covered
- 5
- Typical programme
- 500-2,000 hours
- Cities
- Kolkata, Siliguri, Durgapur
What changes when the language is Bengali
The service specification stays constant across languages; the linguistics do not. For Bengali, three things drive the design of a llm human data programme.
- Inherent vowel is realised as /ɔ/ or /o/, which breaks G2P rules copied from Devanagari-based systems
- Dialect spread: Kolkata standard (Rarhi), Sylheti-influenced, Rangpuri / North Bengal, Medinipuri
- Kolkata professional speech mixes English heavily; rural West Bengal much less. A single 'Bengali' dataset without register tags conflates two very different acoustic and lexical distributions.
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 |
Bengali cohort design
Tag every speaker as Indian Bengali and record district of origin; mixing in Bangladeshi speech without tags is a common and costly dataset defect.
| Dimension | Typical split | Why it matters for Bengali |
|---|---|---|
| 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 Bengali forms that younger urban speakers have lost |
| Region | West Bengal / Tripura / Assam (Barak Valley) 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
Bengali-specific quality rules
- Three sibilant characters chosen inconsistently for the same sound
- Bangladeshi vs Indian Bengali orthographic conventions mixed within one dataset
- Verb conjugation register (cholit vs sadhu) normalised by transcribers
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 Bengali llm human data engagement: 500 hours from 1,000 speakers, 50/50 gender, ages 18-45, spread across Kolkata, Siliguri, Durgapur, 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
Indian Bengali is under-collected relative to Bangladeshi Bengali, and North Bengal and Tripura varieties are almost entirely missing.
Frequently asked
How much does Bengali llm human data cost?
Priced per delivered hour or unit against a written spec. The cost drivers for Bengali are dialect spread, demographic narrowness and recording condition, in that order.
Which Bengali dialects are included?
By default Kolkata standard (Rarhi), Sylheti-influenced, Rangpuri / North Bengal, Medinipuri and others, tagged per speaker. You can also commission a single-dialect corpus if you are targeting one region.
Can you deliver Bengali 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 Bengali programme take?
2-6 weeks depending on task complexity and contributor screening depth.
Request a Bengali llm human data quote
Hours, speakers, dialects, deadline. Send what you have.