Speech recognition technology faces significant challenges in real-world use cases, particularly in African contexts such as informal retail. Code-switching, the seamless transition between languages or dialects within conversations, poses a major obstacle to accurate transcription. Additionally, limited access to diverse and labeled speech datasets, compounded by factors like background noise and accent variability, hinders model training and performance. Innovative approaches leveraging techniques such as data augmentation and crowdsourced dataset curation are essential to address these challenges and enable the widespread adoption of speech recognition technology in Africa.