🇳🇬 Nigeria AI Challenge 2026 · Featured N-ATLaS application

Cassava post-harvest guidance for Umuahia

Explore an experimental, cassava-focused adaptation of the NCAIR1/N-ATLaS 8B base model, paired with a QLoRA fine-tuned adapter. Ask about harvest, storage, transport, and processing across Umuahia-area locations and get up to three practical actions.

Built on N-ATLaS. Loss figures are supplied planning estimates; generated advice has not been independently validated.

â—‰ N-ATLaS Voice Studio
Voice data collection · English · Yorùbá · Hausa · Igbo

Build speech tools with local voices.

Contributors submit consented recordings for independent human review. Approval creates an eligible dataset record; model training and WAXAL/FLEURS workflows are separate steps.

Storage and privacy: This static page does not store, transmit, approve, or export recordings by itself. Configure a secured API before using it for real submissions. The API must enforce identity, consent, independent review, rights, retention, and access controls.

Submit a recording

Record in your browser, listen back, then submit only when your API is configured.

Your recording remains in this browser until the secured API is configured and you submit. Playback is local.

After review

  1. Reviewer approves samples and verifies the exact training rights.
  2. Export a language-specific dataset ZIP through the secured service.
  3. Run the existing ASR training recipe in an approved GPU environment; FLEURS stays held out for evaluation.
  4. Evaluate the checkpoint before separately approving deployment to an AI product.

WAXAL is not imported automatically. Its specific data and derivative terms need review before use.