Turn Kannada video
dans captions viewers trust.

A different approach to Kannada captions.

Kannada is spoken by more than 40 million people, mostly across Karnataka, and it is written in its own script with a grammar structure that most captioning tools were never tuned for. Captions are not the same thing as a raw transcript: viewers need text that is timed to the video, broken into readable chunks, and accurate on names and technical terms, not a wall of text dumped under the frame. For a long time the only reliable way to get that in Kannada was a bilingual captioner working line by line.

Why generic captioning tools struggle with Kannada

Most auto-caption tools are tuned for a handful of high-resource languages, and Kannada rarely gets the same attention. Words get misheard, regional terms flatten into nonsense, and the timing drifts on longer files, which means someone on your team still has to relisten and correct every line. That defeats the reason to automate captioning in the first place.

Captions built from the recording, not just the audio

Speak AI transcribes Kannada audio and video with speaker labels and precise timestamps, then generates captions synced to the video timeline, ready to export as SRT or VTT. Names, terms, and callback details get pulled into structured fields your team can search across the whole library, and you can query it all with AI chat, using ChatGPT, Claude, and Gemini built directly into the workspace.

• Kannada audio and video, captioned with speaker labels and export to SRT or VTT.
• Upload MP3, WAV, M4A, MP4, MOV, and other common formats, or import by URL.
• Translation into English or another language alongside the original Kannada captions.
• Edit captions directly in the editor: fix timing, correct wording, update speaker names.

From one video to a captioned library

The result is a Kannada caption library that behaves like a well-run English one: searchable, exportable, and ready for whatever platform you publish to, with des tableaux de bord que vous pouvez personnaliser et mettre en marque blanche tracking volume and themes across your Kannada content over time. One multilingual education platform put its assessment recordings through this kind of workflow across embedded recorders spanning multiple languages, as detailed in the Interpreting.com multilingual case study, without hiring reviewers for each one.

And because Kannada captions rarely live in isolation from the rest of your work, the same engine scores calls and coaches teams on the same criteria, connecting Kannada captioning to évaluation des appels and the wider base de connaissances your team already runs on.