  We present a preprocessed, ready-to-use automatic speech recognition corpus,
BembaSpeech, consisting over 24 hours of read speech in the Bemba language, a
written but low-resourced language spoken by over 30% of the population in
Zambia. To assess its usefulness for training and testing ASR systems for
Bemba, we train an end-to-end Bemba ASR system by fine-tuning a pre-trained
DeepSpeech English model on the training portion of the BembaSpeech corpus. Our
best model achieves a word error rate (WER) of 54.78%. The results show that
the corpus can be used for building ASR systems for Bemba. The corpus and
models are publicly released at https://github.com/csikasote/BembaSpeech.
