Turn Urdu audio
~ 안으로 Nastaliq you can read.

A different approach to Urdu transcription.

Urdu transcription is the process of turning spoken Urdu, in Nastaliq script or transliterated Roman Urdu, into accurate, searchable text: what was said, who said it, and in what order. Urdu is the national language of Pakistan, one of India’s 22 scheduled languages, and a language spoken by large diaspora communities across the UK, US, Canada, and the Gulf. Researchers, journalists, NGOs, and educators have relied on manual Urdu transcription for years to build interview records, broadcast archives, and course materials.

Why Urdu transcription breaks down

For most teams, Urdu transcription stayed manual because the tools built for it were not. A recording gets replayed three or four times to catch a name or a place, a research assistant types the Nastaliq script by hand, and a generic transcription tool built for English stumbles on the script, the dialect, and the code-switching into English or Punjabi that is common in everyday Urdu speech. What comes back is a document nobody trusts enough to skip the manual check.

Reading Urdu the way a fluent editor would

Speak AI transcribes Urdu audio and video with speech engines routed for the language, and custom vocabulary for the names, places, and technical terms your team actually uses, so a recurring interviewee’s name or a project’s local term is spelled the same way every time. Output is delivered in Nastaliq script, transcribed in your language, with 100+ languages supported for translation in and out. Speak also reads the three layers of a recording: the words, the tone and energy in the delivery, and, on video, the visuals, so a transcript carries more than the words alone ever could.

Then the questions start. Ask across your entire Urdu transcript archive with AI chat, using the same high-quality prompt workflows teams once stitched together by hand, now running natively over your recordings with ChatGPT, Claude, and Gemini built in.

What teams ask their Urdu transcripts

• “Summarize every field interview from this quarter that mentions flooding or displacement.”
• “Which lecture recordings this term cover contract law, and what did students ask?”
• “Show me every clip where a speaker code-switches into English mid-sentence.”
• “Which broadcast segments this month mention a competitor or a named public figure?”
• “Generate captions for this Urdu interview, with the interviewee’s name spelled consistently.”

From raw recordings to research-ready archives

The result is an Urdu transcript archive a team can actually search, not a folder of files nobody reopens. Trends across hundreds of interviews or broadcast hours become a report instead of a hunch, and dashboards you can customize and white-label track themes and sentiment over time, so this quarter’s field interviews are measured against last quarter’s. An education-focused organization put its multilingual assessment recordings, including Urdu, through embedded recorders on this same workflow to scale multilingual assessment across languages without adding headcount. And because Urdu recordings rarely live alone, the same engine scores interviews and training sessions on the same criteria, connecting the transcript archive to call scoring 그리고 coaching for teams that also run calls and sessions in Urdu.