<translate> This need for culturally grounded voice applications emerges from the existence of diverse ethnic communities in the continent coupled with relatively high uptake of information and communication technologies. Whilst the need exists, the process of filling in the gap requires dedication of skills and resources not extensively available to many people or entities on the continent. Having recognised the general lack of diverse datasets to train machine learning models especially for low resource languages, Mozilla through its Common Voice platform has created voice datasets of locally spoken languages such as Swahili and Kinyrwanda. Leveraging on the power of communities to collect these data, Mozilla hosts open voice datasets on its Common Voice platform that can be used to feed speech recognition software. <swh>Hitaji hili la matumizi ya sauti yenye msingi wa kitamaduni linatokana na kuwepo kwa jumuiya za makabila mbalimbali barani pamoja na utumiaji wa juu wa teknolojia ya habari na mawasiliano. Ingawa hitaji lipo, mchakato wa kujaza pengo unahitaji kujitolea kwa ujuzi na rasilimali ambazo hazipatikani kwa wingi na watu au taasisi nyingi barani. Baada ya kutambua ukosefu wa namna nyingi wa seti mbalimbali za data za kufundisha miundo ya mashine ya kujifunza hasa kwa lugha zenye rasilimali chache, Mozilla kupitia jukwaa lake la Common Voice imeunda seti za data za lugha zinazozungumzwa kikanda kama vile Kiswahili na Kinyarwanda. Kwa kutumia uwezo wa jumuiya kukusanya data hizi, Mozilla huweka seti za data za sauti huria kwenye mfumo wake wa Common Voice ambao unaweza kutumika kuhimili programu ya utambuzi wa matamshi. 