Facebook Research actually have another toolkit called wav2vec that's based on the same principle as word2vec (self-supervised discriminative pretraining). wor

Facebook Research actually have another toolkit called wav2vec that's based on the same principle as word2vec (self-supervised discriminative pretraining). word2vec learns good feature representations for words by looking at a context window and adjusting weights for the ngram that fits within that window, simultaneously doing so in the opposite direction for randomly sampled ngrams. wav2vec does something similar, learning good feature representations of audio by distinguishing a "real" sample frame from a randomly sampled frame. I can't remember whether wav2vec operates on the raw waveform, the Fourier transform or MFCCs, but the underlying principle is the same. These learned feature vectors have been shown to dramatically improve downstream tasks.