What should llm companies know before buying Indian speech data?

The problems that recur

Foundation and applied LLM teams that need Indian-language human data with provable provenance, covering languages their web crawl barely touched.

• Web-scraped Indian-language text is thin, noisy and heavily transliterated
• Code-mixed Hinglish is nearly absent from any structured training source
• Provenance and consent for human-generated data must survive external audit

How to evaluate a data partner

Ask for a paid pilot delivered in your ingest format before volume. A vendor who cannot produce 10 hours to spec will not produce 1,000 to spec, and the pilot cost is trivial against the cost of discovering the mismatch late.

• Is every item traceable to a screened, consenting contributor?
• Can contributors be screened by domain expertise, not just language?
• Is there an adjudication process for disagreement on subjective tasks?

Contract terms to insist on

Consent language must explicitly cover commercial AI model training and the term of use. Generic recording releases do not, and a corpus with defective consent is unusable regardless of its audio quality.

• Auditable provenance records
• Contributor consent for model training and distribution
• No third-party or scraped content in deliverables

A typical engagement

Instruction-response pairs, preference rankings and evaluation sets across 6-12 Indian languages.

Scope is fixed in writing, priced fixed against that scope, piloted, then scaled with rolling batch delivery and weekly reporting so training is not blocked on a single final handover.

Services this segment usually buys

Most programmes in this segment combine human data collection for llm, translation and localisation, multilingual data collection. Collection alone rarely solves the problem, because the annotation layer is what makes the audio trainable.