Turn Yoruba audio
into text you can trust.

A different approach to Yoruba to English translation.

Yoruba is a tonal language spoken by more than 40 million people, most of them in Nigeria, with speaker communities across Benin, Togo, and the wider Yoruba diaspora. It carries a deep tradition of oral literature and proverbs, and its tone system changes the meaning of a word depending on pitch, not just spelling. English, by contrast, is the working language of international business, research, and media. Translating between the two is not a word swap. It is the difference between a transcript that reads correctly and one that reads like what the speaker actually meant.

Why text-only translation loses the message

Most Yoruba to English translation still starts with a dictionary tool built for typed text: paste a sentence, get a sentence back. That works for a single phrase. It breaks down on a real recording, where a subject-verb-object sentence carries tone, hesitation, and emphasis that a flat translation cannot capture. A business call gets flattened into stiff English. An interview loses the warmth or urgency in the speaker's voice. A family recording turns into words without a face.

Reading the tone, not just running text through a dictionary

Speak AI translates the audio itself, not a transcript typed out by hand. Every file is transcribed in your language, with 100+ supported, speaker names and timestamps kept in the translated version if you want them, and the tone and energy of the delivery carried into the output alongside the words. Then you can ask questions across your entire library with AI chat, using ChatGPT, Claude, and Gemini built in, the same way a bilingual colleague would answer if you asked them to summarize a stack of Yoruba recordings.

• “What did the speaker in this Yoruba interview actually promise, and by when?”
• “Translate every mention of pricing across these supplier calls.”
• “Which of these recordings mention a specific person, place, or company?”
• “Summarize the common themes across all these Yoruba interviews this quarter.”

From a folder of Yoruba files to English you can act on

The result is a translation workflow that scales past one file at a time. Translate a single recording, or select an entire folder and translate every file in it in a few clicks, with the option to keep speaker labels and timestamps so nothing gets lost in the handoff. Trends across dozens of translated interviews become a report instead of a pile of documents, and dashboards you can customize and white-label track themes and mentions over time. One multilingual assessment provider used this same embedded-recorder and AI workflow to scale language assessment across dozens of languages, Yoruba included, without adding headcount.

And because your Yoruba recordings rarely live in isolation, the same knowledge base connects to every conversation your team captures, queryable from Claude, ChatGPT, and Cursor through the MCP server.