Free Urdu speech data sample

• Urdu audio from at least four speakers with a mixed gender and age spread
• Coverage of Dakhini (Hyderabad), Lucknawi, Dehlvi where your spec calls for dialect breadth
• Verbatim transcripts in Perso-Arabic (Nastaliq), with a romanised parallel transcript on request
• Per-file metadata: speaker ID, age band, gender, district, device, environment and SNR
• A short QA report showing how the files were checked before they were sent

The sample is only useful if it looks like the data you would actually buy. Send the spec you would put in a purchase order and we record against that.

• Run it through your existing ASR or TTS pipeline and read the error breakdown by speaker and dialect
• Check how Urdu code-mixing is transcribed: 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.
• Check the transcription convention against your tokeniser — Right-to-left Nastaliq tooling errors and diacritic loss is the usual failure point
• Confirm the metadata is complete enough to filter and stratify by

Urdu has roughly 51 million speakers across Uttar Pradesh, Telangana, Bihar, and models trained on generic multilingual data typically fail on the same things each time.

Indian Urdu specifically, and Dakhini in particular, are absent from public data dominated by Pakistani Urdu broadcast speech.

• Phonetics your acoustic model may not have seen: Shares most phonology with Hindi but adds Perso-Arabic phonemes (/q/, /x/, /ɣ/, /z/, /f/) that many speakers merge
• Utterance types worth sampling separately: Dakhini spontaneous conversation from Hyderabad, Dual-script transcription sets (Nastaliq plus Devanagari), Formal and colloquial register pairs
• Recording geography in the sample: Hyderabad, Lucknow, Delhi

Nothing automatic. If the sample works, we scope the full build against the same specification and quote a fixed price. If it does not, tell us what failed — that feedback is more useful to us than a polite no.

Fix the script decision before fielding; retro-transcribing a Nastaliq dataset into Devanagari after delivery costs as much as the original transcription pass.