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48 results in AI Resources
N-ATLaS
Nigeria's first government-backed multilingual LLM (Sep 2025): a Llama-3 8B fine-tuned on 400M+ tokens across 4 Nigerian languages. Produced by NCAIR/NITDA and Awarri.
Prembly FraudLens
Open-source fraud-intelligence dataset for Nigerian fintechs (Mar 2026), the first of its kind. Relevant to payment fraud, KYC fraud and account-takeover detection.
SERENGETI
A massively multilingual masked language model covering 517 African languages and varieties across five scripts, achieving state-of-the-art results on the AfroNLU benchmark. Developed by the UBC Deep Learning and NLP Lab as an Afrocentric resource.
Spitch
Spitch is a Lagos-based voice-AI company (founded Oct 2024) providing ASR (speech-to-text), TTS (text-to-speech) and translation APIs/SDKs for Nigerian languages (Yoruba, Igbo, Hausa, Nigerian-accented English, plus Amharic) so teams can add local-language voice to call centres, media and learning tools.
Sunbird AI SALT
SALT (Sunbird African Language Technology) is a multi-way parallel text-and-speech corpus of English plus Luganda, Acholi, Lugbara, Ateso, Runyankole and Swahili supporting ASR, TTS and translation, with companion Whisper and MMS models. Built with Makerere University AI Lab.
Toucan
An Afrocentric many-to-many machine translation model (1.2B params, mT5) fine-tuned from Cheetah to support 156 African language pairs, evaluated on the AfroLingu-MT benchmark. Developed by the UBC Deep Learning and NLP Lab and published at ACL 2024.
UlizaLlama (Jacaranda Health)
UlizaLlama is a 7B-parameter Swahili-and-English LLM fine-tuned from Meta's Llama 2 (continually pretrained on ~321M Swahili tokens) by Jacaranda Health in Kenya, built to power Swahili maternal-health SMS support for low-income expectant mothers in East Africa.
Vulavula
Vulavula is Lelapa AI's API platform for speech-to-text transcription and machine translation across South African and African languages (isiZulu, Sesotho, Sepedi, Setswana, isiXhosa, Afrikaans, English, Swahili, Hausa, Yoruba, French), purpose-built for telco and financial-services contact centres.
Zindi
Africa's largest data science and AI competition platform where organizations host real-world ML challenges and a community of builders competes to solve them. Offers competitions, learning courses, jobs and leaderboards, with partners including Microsoft, Google, AWS and Google DeepMind.
AfriCOMET
AfriCOMET is a COMET-based machine translation evaluation model for African languages, scoring translation quality from source, hypothesis and reference triplets. The STL-1.1 version uses the afro-xlmr-large-76L encoder and was validated in the WMT 2024 Metrics Shared Task across 13 African-centric language pairs. It is released by the Masakhane community with reference-based and quality-estimation variants.
AfriHuBERT
AfriHuBERT is a compact self-supervised speech representation model based on mHuBERT-147, continually pretrained via multilingual adaptive finetuning on over 10,000 hours of speech spanning more than 1,200 African languages and varieties. It improves spoken language identification and ASR over its base model and acts as an encoder for downstream African speech tasks. Its training data was aggregated from sources including BibleTTS, Kallaama, NaijaVoices and NCHLT.
AfriMT5
AfriMT5 (afri-mt5-base) is an mT5-based machine translation model from the Masakhane community fine-tuned to translate across 16 African languages using multilingual adaptive fine-tuning. It targets news-domain translation for low-resource African languages, several of which were not previously covered by existing benchmarks. It is distributed as open weights on HuggingFace.
Bambara-ASR-v2
Bambara-ASR-v2 is an automatic speech recognition model for Bambara (Bamanankan) fine-tuned from OpenAI's Whisper-large-v2 using parameter-efficient tuning, reaching about 25 percent word error rate. It handles natural Bambara-French code-switching common in Mali's multilingual context. It is released under Apache 2.0 as part of the MALIBA-AI community initiative.
EthioLLM
EthioLLM is a family of multilingual language models (XLM-RoBERTa and mT5 based) for five Ethiopian languages: Amharic, Ge'ez, Afaan Oromoo, Somali and Tigrinya, plus English. The large variant EthioLLM-l-70K is a fine-tuned XLM-RoBERTa-Large used for masked language modeling and downstream tasks like classification, NER and sentiment. It was released by the EthioNLP collective alongside the Ethiobenchmark evaluation suite.
GalsenAI Wolof TTS (xTTS-v2-wolof)
xTTS-v2-wolof is a text-to-speech model for Wolof built by GalsenAI by fine-tuning Coqui's xTTS v2 on the cleaned Anta Women Wolof TTS dataset. It synthesizes Wolof speech and can clone a voice from a reference clip as short as 6 seconds. It was developed by the GalsenAI community in Senegal.
IrokoBench
IrokoBench is a human-translated evaluation benchmark for 16 typologically diverse African languages covering three tasks: natural language inference (AfriXNLI), knowledge QA (AfriMMLU) and mathematical reasoning (AfriMGSM). It is widely used to measure the performance gap between English and African languages in large language models. It was released by the Masakhane community and published at NAACL 2025.
Lugha-Llama
Lugha-Llama is a Llama-3.1-8B model continually pretrained on the WURA African-language corpus to lift performance on low-resource African languages. It ships in three variants (wura, wura_edu, wura_math) and reaches leading results among similarly sized models on the IrokoBench and AfriQA African-language benchmarks. It was built by researchers at Princeton University.
MALIBA-AI Bambara TTS
MALIBA-AI Bambara TTS is a neural text-to-speech model for Bambara (Bamanankan), the most widely spoken language in Mali, built on the Spark-TTS framework with a Qwen2.5-based backbone of around 500M parameters. It supports 10 authentic Bambara speaker voices and outputs 16kHz mono audio without a separate vocoder. It is released under a non-commercial MALIBA-AI research license.
Mbaza Whisper-Small-Kinyarwanda
Whisper-Small-Kinyarwanda is an automatic speech recognition model fine-tuned from OpenAI's Whisper-small on the Common Voice Kinyarwanda dataset, achieving about 24 percent word error rate. It transcribes Kinyarwanda audio into text for speech applications. It was built by Mbaza NLP, part of the Digital Umuganda voice-technology ecosystem in Rwanda.
NaijaVoices Dataset
NaijaVoices is a large-scale speech dataset of about 1,800 hours from over 5,000 speakers with expert-curated transcripts in Igbo, Hausa and Yoruba, roughly 600 hours per language. It is designed for building ASR and speech AI for Nigerian languages and improves Whisper and MMS fine-tuning performance. It is available on HuggingFace behind a free registration and powers models like AfriHuBERT and SBPN.
NileChat-3B
NileChat-3B is a 3B-parameter LLM built on Qwen2.5-3B and adapted for Egyptian and Moroccan communities, handling Egyptian and Moroccan dialectal Arabic in both Arabic script and Arabizi alongside Modern Standard Arabic, French and English. It was trained with controlled synthetic data and translated corpora and outperforms its Qwen2.5-3B baseline on Arabic benchmarks. It was released by the UBC Deep Learning and NLP Lab and accepted at EMNLP 2025.
SabiYarn-125M
SabiYarn-125M is a 125M-parameter decoder-only foundation model pretrained on Nigerian-language text, the first in the SabiYarn series. It supports English, Yoruba, Hausa, Igbo and Nigerian Pidgin plus Fulfulde, Efik and Urhobo, with fine-tuned variants for translation, NER, sentiment and diacritization. It was built by Aletheia.ai Research Lab and presented at the AfricaNLP 2025 workshop.
Sunflower-14B
Sunflower-14B is a Qwen3-14B based multilingual LLM by Sunbird AI supporting translation and text generation across 31 Ugandan languages plus English. It achieves the highest translation accuracy in 24 of 31 evaluated language pairs and targets healthcare, agriculture, education and government use cases. It is released under Apache 2.0 with quantized GGUF and W8A8 variants available.
Zabantu-XLM-Roberta
Zabantu is a family of XLM-RoBERTa masked language models (roughly 80M to 250M params) trained from scratch on South African Bantu languages including Tshivenda, Zulu, Xhosa, Swati, Northern and Southern Sotho, Setswana and Xitsonga. It serves as a benchmark for low-resource Bantu language NLP. It was built by the Data Science for Social Impact group at the University of Pretoria.