This is document is to present a wolof speech recognition dataset collected and prepared by BAAMTU Datamation (a senegalease company focused on using data to help companies to leverage AI and Big Data ). Motivation In a country(i.e Senegal), where about 50% of the population is illiterate, using existing technologies and applications that are designed to be used by people who can read is very difficult for those people. Our Goal here is to use ASR technique on WOLOF to help the illiterate persons to interact with apps with just with their voice in a language they can already speak (i.e WOLOF). We chose the urban transport use case for two reasons : ● Many urban transport users can’t read nor speak french, so they can’t interact with existing apps that help passengers to find a Bus for a given destination. ● There is already an existing app in SENEGAL (i.e WeeGo) which help passengers to get information about urban transport, so the goal here is to build an ASR model that will be plugged into the App so illiterate people will be able to use it(the resulting model is actually used by the WeeGo App). Wolof is the most used language in SENEGAL.