Turn Sinhala audio
sisään text you can trust.

A different approach to Sinhala transcription.

Sinhala transcription is the process of turning spoken Sinhala, recorded on the island or across the diaspora, into accurate text: the words, who said them, and increasingly the tone and intent behind them. Close to 17 million people speak Sinhala in Sri Lanka, one of the country’s two official languages, with established communities in Canada, the U.K., Australia, and the Gulf. Media newsrooms, university researchers, NGOs running field surveys, and schools running multilingual assessments have all needed a reliable way to get Sinhala audio into text, and for years that meant a person with headphones and a lot of patience.

Why Sinhala transcription breaks down

For most teams, that person is still doing it by hand. Sinhala’s script is a rounded, diacritic-heavy descendant of Brahmi, and generic transcription tools built around English and a handful of major world languages either render it poorly or flatten it to a rough phonetic guess. Some confuse Sinhala with Tamil, Sri Lanka’s other official language, since both are spoken on the same island but come from unrelated language families. Interviews get replayed twice to catch a name, quotes get retyped by hand, and a week of fieldwork turns into another week of typing before it can go in a report.

Reading the script, not approximating it

Speak AI transcribes Sinhala in the native script, with 100+ other languages supported alongside it, and routes each file through the speech engine best suited to the accent and audio quality, layering in Claude, ChatGPT, or Gemini for the analysis on top. Sinhala is a lower-resource language for automatic speech recognition, meaning far less training audio exists for it than for English or Mandarin, so accuracy is strong and improving rather than flawless out of the box. A project glossary and a quick review pass close the gap for transcripts where every word matters.

Beyond the words, Speak AI reads the delivery itself: tone, energy, and hesitation in the recording, alongside the transcript. Then the questions start. Ask across an entire archive of Sinhala interviews and recordings with AI chat, the same way a research assistant would, now running natively over the audio instead of a stack of translated notes.

What teams ask their Sinhala recordings

• “What did participants in this field study say about access to clean water, in their own words?”
• “Which broadcast clips this month mention the upcoming election, and what was actually said?”
• “Summarize every parent interview from this term’s multilingual assessment, grouped by school.”
• “Show me every recording where the speaker sounds frustrated or uncertain.”
• “Pull every mention of a partner organization or program name, across the whole archive.”

From raw audio to a searchable archive

The result is an archive instead of a backlog: field interviews, broadcast clips, and classroom recordings land as searchable, native-script transcripts with speakers, sentiment, and key terms already tagged. Trends across hundreds of recordings become dashboards you can customize and white-label, tracked over time instead of re-read from scratch every quarter. Interpreting.com, an education pioneer scaling multilingual assessment programs, used this workflow with embedded recorders to standardize multilingual data collection across schools, without hiring a dedicated transcription team. And because Sinhala recordings rarely live alone, the same engine scores calls and coaches conversations on the same criteria, connecting the archive to call scoring and to any assistant through the MCP server.