Turn Farsi audio
naar binnen captions you can trust.

A different approach to Persian captions.

Persian, also called Farsi, is spoken by roughly 77 million people across Iran, Afghanistan, and Tajikistan, and captioning it well means more than getting the words down. It means keeping speaker names straight, matching the right dialect and terms, and getting a file your video platform or learning system will actually accept.

Why Persian captioning stalls in a spreadsheet

For most teams, captioning Farsi content is a one-off task handed to whoever happens to speak the language. Files pile up in different formats, MP3 and WAV audio next to MP4 and MOV video, plus the occasional YouTube link nobody has captioned yet. Someone replays each file twice to catch a name or a technical term, types it out by hand, and by the time the export is ready the deadline has usually slipped. It does not scale past a handful of files a month.

Reading Farsi the way a native speaker would

Speak AI treats Persian captioning as more than a transcript. Each file is transcribed in your language, with 100+ supported, and the recording itself is read the way a person would: the words, the tone and pacing of the speaker, and the visual context when video is involved. Speaker names, technical terms, and your glossary carry through consistently across every file, not just the one someone happened to proofread carefully.

Then the questions start. Ask across your entire Persian media library with AI chat, the same way teams once dug through folders of exported transcripts by hand, now running natively over your recordings with ChatGPT, Claude, and Gemini built in.

What teams ask their Persian caption library

• “Which Farsi webinars this quarter mention our new product name, and how was it captioned each time?”
• “Show me every Persian file where the speaker sounds uncertain or the audio quality is poor.”
• “Summarize the training modules we have captioned in Farsi so far, and flag any still missing captions.”
• “Which Persian course videos have the most watch time, and do their captions need a review pass?”

From a folder of files to a shareable caption library

The result is a Persian caption workflow that does not depend on one bilingual team member. Upload audio or video directly, or drop in a publicly available URL, including YouTube, select Persian from the language menu, and captioning starts automatically. Files export to SRT and VTT for your LMS or video player, or share the interactive media player with the transcript, captions, and analysis together. Dashboards you can customize and white-label track caption volume and turnaround across your whole Persian library over time, so this quarter is measured against last quarter instead of a guess. Interpreting.com, a multilingual assessment platform, used this same recorder-and-language layer to scale assessment across dozens of languages, Persian included, without hiring a translator for every one.

And because Persian content rarely lives alone, the same engine picks the right model, speech engine, and language for every file, whether that is Farsi, English, or one of 100+ languages in between, and the same transcripts feed call scoring en coaching if your Persian-language calls need it too.