Turn Uyghur audio
naar binnen text you can trust.

A different approach to Uyghur transcription.

Uyghur is a Turkic language spoken by an estimated 10-15 million people, most in the Xinjiang Uyghur Autonomous Region of Northwest China, with communities across Kazakhstan, Kyrgyzstan, and Uzbekistan. It is written today in a modified Arabic script, though Cyrillic and Latin scripts have both been used at different points in its history, and centuries of contact along the Silk Road left it with loanwords from Arabic, Persian, and Russian. Few transcription tools support it at all.

Why Uyghur audio gets dropped by generic tools

Most transcription services either skip Uyghur entirely or bolt on a general-purpose speech model never tuned for its script, tone patterns, or dialect variation. Researchers and journalists end up transcribing by hand, sentence by sentence, or paying per-file rates to a language service and waiting days for a file that needed a same-day turnaround. Mixed-language recordings, where a speaker moves between Uyghur and Mandarin mid-sentence, break most tools outright.

Reading the recording, not just the script

Speak AI transcribes Uyghur audio and video into accurate text, with speaker identification and timestamps, then routes the recording across multiple speech engines chosen for the file, the accent, and the dialect. You can also translate the transcript in and out of Uyghur, and ask questions across your entire archive with AI chat, picking the model, Claude, ChatGPT, or Gemini, that fits the task.

• “What did this speaker say about the border crossing, in their own words?”
• “Which interviews in this project mention family members still in Xinjiang?”
• “Summarize the recurring themes across all 40 oral history interviews.”
• “Show me every recording where the speaker switches from Uyghur to Mandarin.”

From a folder of recordings to a searchable Uyghur archive

The result is an archive instead of a folder: every interview, testimony, or call transcribed, timestamped, and searchable in one place, with dashboards die u kunt aanpassen en white-label tracking themes and mentions over time, so this year’s interviews are measured against last year’s. One education program that needed to capture and analyze 350+ bilingual student submissions saved $4K+ and 120 hours running the same embedded-recorder and automated-transcription workflow, the kind of gain that matters even more when the language in question has almost no tooling built for it. And because a Uyghur transcript is rarely the only file in a project, the same engine handles every other language on your team’s list, connecting to the MCP server so your assistant of choice can query the whole archive directly.