Turn Yoruba audio
do text you can trust.

A different approach to Yoruba transcription.

Why generic transcription tools fail Yoruba

Yoruba is spoken by roughly 47 million people, mostly across southwestern Nigeria, and it carries a three-tone system where pitch changes a word’s meaning entirely: “igba” can mean garden, calabash, two hundred, or time depending on tone alone. Generic transcription tools built for English strip the subdots and tone marks (ẹ, ọ, ṣ) that carry that meaning, and they buckle on the dialectal range across Yorubaland, from Ijebu and Ekiti to Oyo and Egba. A transcript that drops the tone marks is not really a Yoruba transcript.

How Speak AI reads Yoruba audio

Speak AI routes your Yoruba recording across multiple speech engines and picks the one suited to your audio quality and terms, then reads the file the way a bilingual transcriber would: the words, the tone and pacing behind them, and the code-switching common in everyday Yorùbánglish. Speaker identification separates voices in interviews, sermons, and panel discussions, and AI summaries, keyword extraction, and sentiment analysis run on top of the transcript once it is clean. We would rather be upfront about the ceiling here: tonal accuracy still depends on recording quality and how strongly a speaker leans into a dialect, and we route to the best available engine rather than promise perfect tonal accuracy on every file.

What teams use Yoruba transcription for

• Nollywood and Yoruba-language film production, for subtitling and content indexing
• Nigerian market research firms running Yoruba focus groups and customer interviews
• University linguists documenting oral history and sociolinguistic research
• Churches and diaspora congregations transcribing Yoruba sermons for archives and translation
• NGOs running community health and development programs in Yoruba-speaking regions
• Diaspora cultural associations preserving Yoruba oral history for the next generation

From a raw recording to a transcript your team can act on

Once the transcript lands, ask it questions with AI chat, and track themes and sentiment on dashboards you can customize and white-label, across months of recordings instead of one file at a time. An interpreting and language-services platform used this same three-layer reading, the words, the delivery, and eventually the visuals of a session, to scale multilingual assessment across languages including Yoruba, without adding headcount: read how Interpreting.com scaled multilingual assessment with embedded recorders and AI. And because a Yoruba recording rarely lives alone, the same engine is queryable from Claude, ChatGPT, and Cursor through the MCP server, so your transcripts sit inside the tools your team already works in.