Turn Swahili audio
в text you can search.

A different approach to Swahili transcription.

Swahili, or Kiswahili, is spoken by more than 100 million people across Kenya, Tanzania, Uganda, Rwanda, Burundi, and the Democratic Republic of Congo, and it holds official status in the African Union. Its vocabulary carries centuries of contact between Bantu, Arabic, Persian, and later Portuguese and English speakers along the East African coast, and it remains one of the fastest-growing languages on the internet. Turning Swahili speech into usable text has usually meant a bilingual transcriber working line by line, or a generic tool that gets the easy words right and mangles the rest.

Where Swahili transcription usually breaks down

Most transcription engines are tuned for a small set of high-resource languages, and Swahili is rarely one of them. Loanwords from Arabic and English get flattened, pronunciation that shifts across Kenya, Tanzania, and Uganda gets misheard, and the tone behind a frustrated customer call or an urgent field report never survives into the transcript. Teams end up re-listening to the recording anyway, which cancels out most of the time transcription was supposed to save.

Reading the recording, not just the words

Speak AI transcribes Swahili audio and video with speaker labels and timestamps, then analyzes the recording itself: the tone and energy behind a customer complaint, the urgency in a field call, the confidence in a market research interview. Names, locations, and callback details are extracted into structured fields your team can search. Then you can ask across your entire Swahili archive with AI chat, using ChatGPT, Claude, and Gemini built directly into the workspace.

• Swahili audio and video, transcribed with speaker labels and translation into English or another language.
• Handles regional variation across Kenya, Tanzania, Uganda, and the wider East African coast, alongside standard written Swahili.
• Captions and subtitles (SRT, VTT) generated straight from the timestamped transcript.
• Export to Word, PDF, TXT, CSV, or JSON, depending on how your team needs to use it.

From a stack of recordings to a searchable archive

The result is a Swahili archive that behaves like a well-run English one. Customer calls, focus groups, and field interviews become searchable text instead of a folder of audio nobody has time to relisten to, and dashboards you can customize and white-label track sentiment and themes across your Swahili recordings over time. One multilingual education platform put its assessment recordings through this kind of workflow across embedded recorders spanning multiple languages, without hiring reviewers for each one.

And because Swahili recordings rarely live in isolation from the rest of your work, the same engine scores calls and coaches teams on the same criteria, connecting Swahili transcription to call scoring and the wider база знань your team already runs on.