Machine Translation · اردو
Urdu Data for Machine Translation
Training and evaluating translation between English and Indian languages, and between Indian languages. In Urdu, the binding constraint is usually dialect coverage and code-mixing, not raw hours.
- Language
- Urdu
- Primary metric
- Human adequacy and fluency scores
- Typical volume
- 250-1,000 hours
Data profile required
- Sentence-aligned parallel corpora
- Register-matched to your product
- Enforced terminology glossary
What Urdu adds to the requirement
- Shares most phonology with Hindi but adds Perso-Arabic phonemes (/q/, /x/, /ɣ/, /z/, /f/) that many speakers merge
- Dakhini differs substantially from north Indian Urdu in lexicon, morphology and intonation
- Dialects to cover: Dakhini (Hyderabad), Lucknawi, Dehlvi, Bihari Urdu
- Spoken Urdu and spoken Hindi are largely mutually intelligible; the distinction is mainly lexical and orthographic. Decide up front whether transcription is in Nastaliq, Devanagari, or both.
Metrics to track
- Human adequacy and fluency scores
- Terminology compliance rate
- Back-translation divergence
Failure modes
- Pivoting everything through English
- Post-edited machine output passed off as human translation
For Urdu specifically: Indian Urdu specifically, and Dakhini in particular, are absent from public data dominated by Pakistani Urdu broadcast speech.
Recommended cohort
Fix the script decision before fielding; retro-transcribing a Nastaliq dataset into Devanagari after delivery costs as much as the original transcription pass.
| Dimension | Typical split | Why it matters for Urdu |
|---|---|---|
| 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 Urdu forms that younger urban speakers have lost |
| Region | Uttar Pradesh / Telangana / Bihar 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 |
Suggested programme shape
Start with an evaluation set of 100 speakers spread across every Urdu dialect in scope, collected before training data. Then field 250-1,000 hours of training data from disjoint speakers.
This ordering is what makes the improvement measurable rather than assumed.
Frequently asked
Is there usable public Urdu data for machine translation?
Indian Urdu specifically, and Dakhini in particular, are absent from public data dominated by Pakistani Urdu broadcast speech.
How many Urdu speakers do we need?
500-1,500 speakers for a training corpus, plus a disjoint evaluation cohort covering each dialect. Speaker count matters more than hours for generalisation.
Can you run this across multiple languages at once?
Yes. Multi-language programmes run to one master specification so per-language results stay comparable.
Scope Urdu data for machine translation
Send the target metric and the languages in scope.