Speech AI that understands Nigerian English

Built on our own data, recorded across Nigeria.

Backed by the TY Danjuma Family Office

Nobody else has this.

We didn't scrape it off the internet. We went out and recorded it. Every hour is consented, paid for, and checked by somebody who actually speaks the language.

Anybody can build a model. Nobody can go back and record the last three years.

1,400 hrs

Recorded speech

Studio and field, across the country.

3,200

Distinct speakers

No speaker recorded twice under two names.

24

States covered

Out of 36, plus the FCT.

18

Accents and varieties

Tagged per clip, not per session.

The languages we cover, and the ones we are going after

Collected

Nigerian English

The language of the courts, the classroom and the news. It has its own vowels and its own rhythm, and no model trained on London or Los Angeles has heard them.

60–100M

Speakers

Almost all second language. Estimates vary by how you count fluency.

37

States and the FCT

Official everywhere. What changes by state is accent, not presence.

820 hrs

Recorded so far

Consented, paid for, and checked by a person.

Collected

Nigerian Pidgin

Naijá

Spoken everywhere, native here. Pidgin learned first sounds nothing like Pidgin spoken over Yorùbá or Hausa, and the difference shows up in the audio.

75–120M

Speakers

The widest spread of any language here, and the widest disagreement on the number.

6

States in the heartland

Where it is a first language rather than a second one.

580 hrs

Recorded so far

Weighted toward first language speakers on purpose.

Next

Yorùbá

Èdè Yorùbá

Tone carries meaning, not mood. Get the marks wrong and you have written a different word. Most speech pipelines drop them entirely.

40–45M

Speakers

Plus millions more across Kwara and Kogi in the North Central.

6

States in the zone

Lagos, Ogun, Oyo, Osun, Ondo, Ekiti.

Planned

Collection

Not started. We would rather say that than pad a number.

Next

Hausa

Count second language speakers and it is the biggest language in the country. It runs trade and radio across the whole north.

50–55M

First language speakers

Over 80M once you count it as a second language.

7

States in the zone

Kano, Katsina, Jigawa, Kaduna, Sokoto, Zamfara, Kebbi.

Planned

Collection

Not started.

Next

Igbo

Asụsụ Igbo

Tonal, heavily dialectal, and written with marks most speech tools quietly throw away. Anambra and Abia do not sound alike.

25–35M

Speakers

Plus Anioma, Ika and Ukwuani Igbo across the Niger in Delta.

5

States in the zone

Anambra, Imo, Enugu, Abia, Ebonyi.

Planned

Collection

Not started.

Nigerian English, Every zone

What we build with it

Getting the recordings is the hard part. These are the things they make possible.

A waveform resolving into lines of transcript
A grid of audio clips, some marked as checked
Concentric rings radiating from a speaking point
Recording sites scattered across a region and joined into a network

People don't speak the way they read

Most speech data is somebody reading prompts out loud. Ours is people talking. That difference is the whole product.

  1. A prompt sheet above a waveform of identical, evenly spaced bars

    Most speech data is read aloud

    Someone sits with a script and reads prompts into a mic. Clean, fast, and nothing like the way people actually talk.

  2. A flat, even waveform above an irregular one, with the distance between them marked

    People don't talk like they read

    Reading evens out the pace, drops the fillers, and never switches from English to Pidgin halfway through a sentence.

  3. Two speakers' waveforms taking turns and overlapping, over the texture of a room

    We record real conversations

    People talking to each other, in the places they would be talking anyway, with the noise that comes with it.

  4. Clusters of conversation points across a region, joined into a network

    So it sounds like your users

    Everyday African speech, in the languages and the registers it actually happens in.

The models we're building

Three of them, all trained on the same corpus. Pick one to see what it does.

  • Transcription you can build on. Subtitles, meeting records, anything that has to be searchable after the fact.

  • Generated audio in a voice your customers already recognise, rather than one imported from somewhere else.

  • End to end conversation. Somebody speaks, the thing answers, and nothing has to be written down in between.

Speech to text

Transcription you can build on. Subtitles, meeting records, anything that has to be searchable after the fact.

ASR, Speech to text. Transcription you can build on. Subtitles, meeting records, anything that has to be searchable after the fact.

FAQ

The ones that come up on nearly every call. If yours is not here, ask us directly.

  • Nigerian English and Nigerian Pidgin are collected and in use. Yorùbá, Hausa and Igbo are next, in that order. If you need one that is not on the list, that is a conversation worth having.

  • We record it ourselves, across Nigeria. Every hour is consented, paid for, and checked by somebody who speaks the language. Nothing here was scraped.

  • Yes. Licensed hours, labelled clip by clip rather than batch by batch, with provenance you can put in front of a regulator.

  • That is a service we run. Your vocabulary, your customers' accents, recorded to a brief you write with us.

  • We scope it, run a pilot on your own domain data, then deploy. You see the numbers on your traffic before anybody signs anything long term.

  • Yes, and you get paid for it. Bring your voice, your accent and an hour of your time.