SpecAugment

Why we included this project

Training speech recognition models often runs into the same wall: not enough audio, and the model overfits what little you have. SpecAugment, the data augmentation method Google Brain introduced in 2019, attacks that directly by altering the spectrogram itself. This repo packages that technique as a small library with two interchangeable implementations, one for TensorFlow and one for PyTorch, so you can drop it into either stack. It warps the spectrogram in time and masks random blocks of frequency channels and time steps, which mimics the kind of distortion real speech picks up. For anyone building ASR pipelines, that's a quick way to add a proven regularization step without reimplementing the paper's details.