Contrastive Predictive Coding (CPC), based on predicting future segments of\nspeech based on past segments is emerging as a powerful algorithm for\nrepresentation learning of speech signal. However, it still under-performs\nother methods on unsupervised evaluation benchmarks. Here, we introduce\nWavAugment, a time-domain data augmentation library and find that applying\naugmentation in the past is generally more efficient and yields better\nperformances than other methods. We find that a combination of pitch\nmodification, additive noise and reverberation substantially increase the\nperformance of CPC (relative improvement of 18-22%), beating the reference\nLibri-light results with 600 times less data. Using an out-of-domain dataset,\ntime-domain data augmentation can push CPC to be on par with the state of the\nart on the Zero Speech Benchmark 2017. We also show that time-domain data\naugmentation consistently improves downstream limited-supervision phoneme\nclassification tasks by a factor of 12-15% relative.\n