Autoencoders

# Autoencoders At a high level, autoencoders are models constructed of two parts: an *encoder*, and a *decoder*. The *encoder* takes in an sequence (such as mono or stereo audio) and outputs a compressed representation of that sequence as a d-channel "latent sequence", usually heavily downsampled by a constant factor. The *decoder* takes in a d-channel latent sequence and upsamples it back to the original input sequence length, reversing the compression of the encoder. Autoencoders are trained with a combination of reconstruction and adversarial losses in order to create a compact and invertible representation of raw audio data that allows downstream models to work in a data-compressed "latent space", with various desirable and controllable properties such as reduced sequence length, noise resistance, and discretization. The autoencoder architectures defined in `stable-audio-tools` are largely fully-convolutional, which allows autoencoders trained on small lengths to be applied to arbitrary-length sequences. For example, an autoencoder trained on 1-second samples could be used to encode 45-second inputs to a latent diffusion model.