What teams use this data for
Most conversations fall into three buckets: pretraining, speech, and evals.
Foundation model teams
Add Bengali to your pretraining mix
Problem: Your model speaks English and maybe Hindi. Bengali, Odia, Assamese, and Maithili are usually thin.
Dataset fit: Licensed Bengali-led text in volumes large enough to move the needle, with dialect labels and clear license scope.
Outcome: More Indian languages handled well, without legal panic about source risk.
Proof artifact: Coverage sheet + Bengali sample data card
Speech + audio teams
Make your ASR actually work in Sylhet
Problem: ASR trained on Standard Bengali often breaks on Sylheti, Chittagonian, or Rangpuri speech.
Dataset fit: Speech datasets with dialect labels at segment level, transcripts, speaker metadata, and review notes.
Outcome: Lower failure rates on dialects that matter to real users.
Proof artifact: QA scorecard + Bengali speech release summary
Eval + research teams
Build benchmarks you can trust over time
Problem: If eval sets shift silently, model comparisons become hard to defend.
Dataset fit: Versioned benchmark slices, release history, change notes, and manifest hashes.
Outcome: Cleaner comparisons across model versions and less explanation later.
Proof artifact: Version log + compliance summary
Who usually asks for what
Different teams care about different parts of the catalog.
Foundation model teams
Bengali-led text and speech, license paperwork attached, ready to drop into training pipelines.
Speech and ASR teams
Dialect-labelled audio, transcripts, and speaker metadata for Sylheti, Chittagonian, Kamrupi, and more.
Procurement and legal
Coverage sheet, compliance one-pager, signed contracts, and source evidence.
Eval and research teams
Versioned benchmark slices, change notes, and sample sets behind the work-email gate.
Tell us what you're training. We'll scope the package.
Tell us your goal and timeline. We'll map it to languages, format, and license tier.