Impressive results - on CD-quality audio too, not just speech as with many recent codecs. One thing I was not able to find, either in the blog post or the paper (https://arxiv.org/pdf/2210.13438.pdf), was the size of the model.
EDIT: I downloaded the model from the code (https://github.com/facebookresearch/encodec/blob/3837f0db2a3...). It looks to have 15M parameters in total (this is a CPU-only model, by the way). For inference, I assume we can get away with 16-bit floats, so this clocks in at about 30 MiB. Not particularly favorable compared with LAME on my system (164 KiB), but not at all unreasonable to use - these days apps take 30 MiB dependencies all the time.
EDIT: I downloaded the model from the code (https://github.com/facebookresearch/encodec/blob/3837f0db2a3...). It looks to have 15M parameters in total (this is a CPU-only model, by the way). For inference, I assume we can get away with 16-bit floats, so this clocks in at about 30 MiB. Not particularly favorable compared with LAME on my system (164 KiB), but not at all unreasonable to use - these days apps take 30 MiB dependencies all the time.