Turn Persian audio
sisään text you can trust.

A different approach to Persian transcription.

Persian transcription is the process of using speech recognition and natural language processing to turn Farsi and Dari audio and video into accurate, searchable text: what was said, by whom, and what it means. Researchers, journalists, legal teams, and language educators have used it for years to make Persian-language recordings usable instead of sitting untouched on a hard drive.

Why Persian transcription breaks down

For most teams, transcribing Persian by hand is the bottleneck. A bilingual staffer relistens to an interview twice to catch a phrase, a general-purpose transcription tool garbles the script or drops the diacritics, and a recording that took forty minutes to capture takes hours to turn into usable text. Dialect, code-switching between Farsi and English, and inconsistent romanization make off-the-shelf tools worse, not better, the moment a conversation gets technical or emotional.

Reading the recording, not just the words

Speak AI treats every Persian recording the way a bilingual research assistant would, at machine speed. Each file is transcribed in your language, with 100+ supported, and then the recording itself is analyzed: the tone and energy of the speaker, the hesitation and emphasis, and the emotion underneath a translated quote that plain text alone would miss. Names, terms, and speaker turns are extracted into structured fields your systems can use.

Then the questions start. Ask across your entire Persian recording history with AI chat, using the same high-quality prompt workflows teams once stitched together manually, now running natively over your recordings with ChatGPT, Claude, and Gemini built in.

What teams ask their Persian recordings

• “Transcribe this Farsi-language interview and translate the key quotes into English.”
• “Which of our Persian focus group recordings mention pricing concerns?”
• “Export this Persian transcript as SRT captions for our video team.”
• “Summarize sentiment across all our Farsi customer calls this month.”
• “Find every mention of a competitor across our Persian podcast episodes.”

From a stack of tape to a searchable archive

The result is a Persian archive that answers back instead of sitting untouched. Quotes come back translated and sourced to the exact timestamp. Themes across dozens of interviews become a report instead of a week of note-taking, and dashboards you can customize and white-label track sentiment and theme frequency over time, so this quarter’s fieldwork is measured against last quarter’s. One multilingual education program built with Interpreting.com scaled assessment across dozens of languages using embedded recorders, Persian included.

And because Persian recordings rarely live alone, the same engine scores calls, meetings, and interviews on the same criteria, connecting the archive to call scoring and to any assistant that can query it directly through the MCP server.