Turn Luganda audio
sisään text you can search.

A different approach to Luganda transcription.

Luganda transcription is the process of turning recorded speech in Luganda, one of Uganda’s most widely spoken languages, into accurate written text, then analyzing what is inside it: themes, sentiment, and the details a research team or media monitor actually needs. Teams working in and around Kampala have transcribed interviews, broadcasts, and field recordings by hand for years, but the process rarely scales past a handful of files a week.

Why Luganda recordings pile up untouched

Most teams working with Luganda audio hit the same wall. Trained Luganda transcribers are hard to find and expensive to keep on retainer, so recordings queue up until someone has time to listen through them by hand. Mainstream transcription tools, built and tuned on high-resource languages, either reject Luganda outright or return a rough pass full of errors on place names, program terms, and the code-switching between Luganda and English that shows up in nearly every real conversation.

How Speak AI reads Luganda

Speak AI routes Luganda audio across multiple speech engines rather than a single model, and lets you prime custom vocabulary, place names, organization names, program terms, before you start transcribing, so accuracy holds up on the recordings you actually have. We are honest about where the language sits: Luganda is a lower-resource language for AI, and accuracy will not match English or Spanish out of the box. What it can do is improve meaningfully with your vocabulary list and consistent audio, and get more useful with every file, since the platform reads more than the words. It listens for the energy and tone in the delivery alongside the words themselves and, where available, the visuals in a recorded session, so a frustrated respondent or an enthusiastic testimony reads differently from a flat status update, not just as a wall of translated text.

What teams ask their Luganda recordings

• “Which Luganda interviews mention transport, healthcare, or the borehole project by name?”
• “Summarize the main themes across this month’s field recordings.”
• “Which respondents sounded frustrated or urgent, and about what?”
• “Show every recording that mentions a location outside Kampala.”

From scattered recordings to a searchable archive

The result is a Luganda archive your team can actually query, instead of a folder of audio files nobody reopens. Themes across dozens or hundreds of recordings become a report instead of a guess, and dashboards you can customize and white-label track coverage, sentiment, and topic trends over time, so this quarter’s fieldwork is measured against last quarter’s. An education-focused customer used this same approach to scale multilingual assessment across embedded recorders in the field, without adding a translator to every session. And because a Luganda archive rarely stands alone, the same structured data is queryable straight from Claude, ChatGPT, and Cursor through the MCP server, alongside every other language and recording your team captures.