  Machine translation (MT) is a rapidly expanding field that has experienced
significant advancements in recent years with the development of models capable
of translating multiple languages with remarkable accuracy. However, the
representation of African languages in this field still needs to improve due to
linguistic complexities and limited resources. This applies to the Zarma
language, a dialect of Songhay (of the Nilo-Saharan language family) spoken by
over 5 million people across Niger and neighboring countries
\cite{lewis2016ethnologue}. This paper introduces Feriji, the first robust
French-Zarma parallel corpus and glossary designed for MT. The corpus,
containing 61,085 sentences in Zarma and 42,789 in French, and a glossary of
4,062 words represent a significant step in addressing the need for more
resources for Zarma. We fine-tune three large language models on our dataset,
obtaining a BLEU score of 30.06 on the best-performing model. We further
evaluate the models on human judgments of fluency, comprehension, and
readability and the importance and impact of the corpus and models. Our
contributions help to bridge a significant language gap and promote an
essential and overlooked indigenous African language.
