LLM Companies · ASR Model Training
ASR Model Training Data for LLM Companies
Foundation and applied LLM teams that need Indian-language human data with provable provenance, covering languages their web crawl barely touched. Building or fine-tuning speech recognition for Indian languages from scratch or from a multilingual base model.
- Buyer
- LLM Companies
- Use case
- ASR Model Training
- Metric
- Word error rate overall and per dialect
Where the two meet
Web-scraped Indian-language text is thin, noisy and heavily transliterated That is a asr model training problem, and it is solved by data shaped like this:
- Hundreds to thousands of hours of verbatim-transcribed speech
- Wide speaker diversity: age, gender, region, education, recording condition
- Speaker-disjoint train/dev/test splits
Your evaluation criteria
- 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?
Metrics
- Word error rate overall and per dialect
- Entity error rate on names and numbers
- Code-switch token accuracy
Pitfalls
- Read-speech-only corpora that do not transfer to spontaneous audio
- Speaker leakage across splits inflating reported accuracy
- Normalised-only transcripts with the raw text discarded
Contract points
- Auditable provenance records
- Contributor consent for model training and distribution
- No third-party or scraped content in deliverables
Frequently asked
What does a first engagement look like?
Usually a scoped pilot: one language, an evaluation set plus a first training batch, delivered in three to five weeks, followed by the full programme.
Can you match our existing vendor's schema?
Yes. Working to your schema avoids a conversion pass and keeps deliveries comparable across vendors.
How is provenance documented?
Per-item contributor records and consent mapped to IDs in the manifest.
Send your requirement
Language, volume, metric, deadline.