اردو · Perso-Arabic (Nastaliq) · ur-IN
Urdu AI Training Data Collection
We collect Urdu speech and language data for AI training across Uttar Pradesh, Telangana, Bihar and beyond, covering 5 dialect varieties rather than a single prestige standard.
- Speakers
- 51M
- Script
- Perso-Arabic (Nastaliq)
- Studio cities
- 5
- Typical programme
- 250-1,000 hours
Why Urdu breaks generic speech models
Urdu is not a variant of a language your model already handles. The specific properties below are the ones that show up as errors in production.
- 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
- Register shifts between colloquial Hindustani and formal Urdu change the vocabulary distribution sharply
Dialects we cover
Urdu has 5 varieties that matter for data collection. Collecting only the standard variety produces a model that works in the capital and fails everywhere else.
- Dakhini (Hyderabad)
- Lucknawi
- Dehlvi
- Bihari Urdu
- Mumbai Urdu
Code-mixing reality
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.
Specify your expected English-token ratio in the brief. It is cheaper to collect the right mix than to filter the wrong one afterwards.
What is missing from public data
Indian Urdu specifically, and Dakhini in particular, are absent from public data dominated by Pakistani Urdu broadcast speech.
This is the practical reason to commission collection rather than assemble open corpora: the gap in the public data is exactly the part your users occupy.
Transcription rules that decide whether the data is usable
These are the Urdu-specific decisions that go into the style guide before any transcriber starts work.
- Right-to-left Nastaliq tooling errors and diacritic loss
- Merged phonemes transcribed by sound rather than by etymology, or vice versa, inconsistently
- Dakhini forms replaced with standard Urdu
Recording types available
- Dakhini spontaneous conversation from Hyderabad
- Dual-script transcription sets (Nastaliq plus Devanagari)
- Formal and colloquial register pairs
Recommended cohort structure
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 |
Where we record
Urdu sessions run in Hyderabad, Lucknow, Delhi, Bhopal, Patna. Multi-city fielding is the default because single-city cohorts carry an audible accent signature.
The numbers we hold ourselves to
- 100% of delivered files pass automated technical QA for SNR, clipping, duration and silence
- 5-25% of files pass a second native-speaker content review, stratified by city, dialect and transcriber, and escalating to 100% on any batch that fails the agreed threshold
- Accepted yield runs 85-90% for scripted speech, 60-70% for spontaneous, 55-65% for conversational and 50-60% for telephony
- Default cohort quotas: 50/50 gender, with age bands at 30% (18-25), 40% (26-40) and 30% (41-60)
- 48 kHz / 24-bit capture, delivered as 16-bit PCM WAV, with studio sessions held below a -50 dBFS noise floor
- First response within one working day; a scoped, fixed quote within two to three
These are the figures a delivery is measured against, not aspirations. A batch that misses them is re-recorded at our cost rather than repaired.
Frequently asked
How many Urdu speakers can you field?
500-1,500 speakers for a standard programme, fielded across 5 cities. Fix the script decision before fielding; retro-transcribing a Nastaliq dataset into Devanagari after delivery costs as much as the original transcription pass.
Do you transcribe Urdu in Perso-Arabic (Nastaliq)?
Yes, and we can deliver romanised or dual-script transcripts alongside. Right-to-left Nastaliq tooling errors and diacritic loss is one of the rules fixed in the style guide before work begins.
Can you collect dialect-specific Urdu data?
Yes. Every speaker is tagged with region and dialect, so you can evaluate per variety after delivery instead of discovering the imbalance in production.
What about Urdu-English code-mixing?
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.
Request a Urdu dataset quote
Tell us the hours, speaker count and dialect spread you need. You get a scope, a timeline and a price.