Turn Tamil audio
dans texte digne de confiance.

A different approach to Tamil transcription.

Tamil is spoken by more than 78 million people, with official status in Tamil Nadu, Sri Lanka, and Singapore, and large communities across Malaysia and the diaspora. It is one of the oldest continuously used languages in the world, with a script and literary tradition that stretches back over two thousand years. That history is exactly what generic transcription tools get wrong: they treat Tamil audio like any other input and hope for the best.

Why Tamil transcription breaks down

For most teams, Tamil audio piles up in a folder nobody has time to work through by hand. A researcher replays an interview three times to catch a name. A media team scrubs back and forth through a broadcast clip looking for one quote. The tools that claim to handle Tamil often stumble on regional dialect variation between Tamil Nadu and Sri Lanka, on code-switching into English mid-sentence, and on the gap between formal written Tamil and how people actually speak.

Lire le message, pas seulement les mots

Speak AI treats every Tamil recording as more than a wall of text. Each file is transcribed in your language, with 100+ supported, and then the recording itself is analyzed: three layers at once, the words, the tone and energy of the speaker, and, for video, what is on screen. Speaker names, timestamps, and terminology are extracted into structured fields your team can search and export. You can run the same transcript through Claude, ChatGPT, or Gemini, whichever model handles your Tamil content best for the task at hand.

Then the questions start. Ask across your entire Tamil recording history with AI chat, the same way a bilingual research assistant would answer, now running natively over your files.

What teams ask their Tamil recordings

• “Which interviews mention this topic, and what exactly did the speaker say in Tamil and in translation?”
• “Show me every clip where the speaker switches from Tamil into English.”
• “Summarize the themes across all the field interviews from this quarter.”
• “Which recordings still need a speaker name confirmed before we publish?”
• “Pull every mention of this term across the Tamil archive, with timestamps.”

From a backlog of recordings to a searchable archive

The result is a Tamil archive that works the way your team actually searches, instead of a folder of files nobody opens twice. Captions and subtitles export straight from the transcript in SRT and VTT. Terminology and themes across hundreds of recordings become a report instead of a re-listen, and des tableaux de bord que vous pouvez personnaliser et mettre en marque blanche track coverage and themes over time, so this quarter’s interviews are measured against last quarter’s. One multilingual education program put its assessment recordings through this workflow, scaled it across languages including Tamil, and you can read how in the Interpreting.com multilingual case study.

And because Tamil recordings rarely live alone, the same engine handles every other language your team works in, all searchable from the Serveur MCP straight inside Claude, ChatGPT, or Cursor.