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AI Data Companies · ASR Model Training

ASR Model Training Data for AI Data Companies

Data vendors and labelling platforms that win Indian-language work and need a delivery partner on the ground who works to their spec and under their brand. Building or fine-tuning speech recognition for Indian languages from scratch or from a multilingual base model.

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Audio waveforms being prepared as ASR training data — ASR Model Training Data for AI Data Companies
Buyer
AI Data Companies
Use case
ASR Model Training
Metric
Word error rate overall and per dialect
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Where the two meet

Indian-language capacity is hard to build remotely, especially outside metros 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
AI Data Companies · ASR Model TrainingWhat goes wrongWhat they check before signingIndian-language capacity is hard to build… remotely, especially outside metros…Client QA standards must be met by a subc…ontractor without loss of control…Margins disappear when re-work is needed …after delivery…Will the partner work to our specificatio…n and schema exactly?…Is the partner willing to work white-labe…l under our client relationship?…Is the QA report detailed enough to hand …to our client unchanged?…We quote against the right-hand column, not the pitch.
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Your evaluation criteria

  • Will the partner work to our specification and schema exactly?
  • Is the partner willing to work white-label under our client relationship?
  • Is the QA report detailed enough to hand to our client unchanged?
Diverse Indian speakers waiting for multilingual data collection sessions — supporting asr model training data for ai data companies
Diverse Indian speakers waiting for multilingual data collection sessions
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Metrics

  • Word error rate overall and per dialect
  • Entity error rate on names and numbers
  • Code-switch token accuracy
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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
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Contract points

  • White-label and non-solicitation terms
  • Your schema, your QA thresholds
  • Predictable per-unit pricing

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.

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