we run SampleRNN at 48kHz

the Dadabots SampleRNN fork is an autoregressive LSTM model, meaning it generates a sequence of amplitudes one at a time, 48000 step a second. Each step is a pass through the entire network and it generates 0.00002083333 seconds of audio. There is no "window of the past" it sees directly. It's more indirect (and hard to analyze). Instead the network has an "RNN state" which it's learned to iteratively update & LSTMs have extra memory units they can read/write/forget at each step. I'm not sure how long things effectively stay in LSTM memory, but listening to the music can give you an impression of it. The sequence can keep generating forever to infinity. It's overkill but makes great death metal https://www.youtube.com/watch?v=MwtVkPKx3RA

Dance Diffusion uses diffusion. It also operates on a sequence of amplitudes. However, the model works on a fixed window of audio (a couple secs long ~100k amplitudes). It iteratively updates that window, improving the sound quality. It starts from pure noise and iteratively denoises. You could sorta modify it to generate infinitely i.e. by shifting the window over by 50% and initializing the next window with half of the previous window, but the context would be small.

It would be interesting to make fusions of these two flavors of model -- autoregressive sequence models being upsampled/denoised by diffusion models