Early diagnosis of cardiac abnormalities from ECG recordings is crucial for predicting and preventing cardiovascular diseases and other morbidities. This laboursome task has not yet been fully automated and remains a heavy burden for cardiologists. As part of the 2020 PhysioNet/Computing in Cardi-ology Challenge, team Karma Backprops developed a convolutional neural network approach for classifying a wide range of cardiac abnormalities from multi lead ECGs. The algorithm is inspired by WaveNet, a system that was proposed origi-nally for the audio domain. This architecture contains various dilation fac-tors that allow its receptive field to grow exponentially with depth. In addi-tion, complex data augmentation mechanisms are utilized, allowing for in-creased generalizability and transportability of the algorithm to unseen envi-ronments. Preliminary evaluation has shown that the proposed approach is capable of correctly diagnosing 9 distinct cardiac events with performance of 0.464 on a hidden test set, where performance is measured by a geometric mean between an F-beta score and a generalization of the Jaccard measure. These encouraging results prove the potential of our method and call for further model development using data from more sources.