  Yor\`ub\'a is a widely spoken West African language with a writing system
rich in orthographic and tonal diacritics. They provide morphological
information, are crucial for lexical disambiguation, pronunciation and are
vital for any computational Speech or Natural Language Processing tasks.
However diacritic marks are commonly excluded from electronic texts due to
limited device and application support as well as general education on proper
usage. We report on recent efforts at dataset cultivation. By aggregating and
improving disparate texts from the web and various personal libraries, we were
able to significantly grow our clean Yor\`ub\'a dataset from a majority
Bibilical text corpora with three sources to millions of tokens from over a
dozen sources. We evaluate updated diacritic restoration models on a new,
general purpose, public-domain Yor\`ub\'a evaluation dataset of modern
journalistic news text, selected to be multi-purpose and reflecting
contemporary usage. All pre-trained models, datasets and source-code have been
released as an open-source project to advance efforts on Yor\`ub\'a language
technology.
