encodec

Since Ballard's codec is "AI" based, can you add google's lyrav2 ( https://github.com/google/lyra ) and Facebook's/meta EnCodec ( https://github.com/facebookresearch/encodec ). Also I don't seem to be able to access your page, so there might be error. Finally, when doing opus comparison it's good now to denote if it is using Lace or NoLace decoder post processing filters that became available in opus 1.5 (note, this feature need to be enabled at compile time, and defying decode a new API call needs to be made to force higher complexity decoder) . See https://opus-codec.org/demo/opus-1.5/

It’s mostly there in https://github.com/lucidrains/audiolm-pytorch#hierarchical-t.... They just used FAIRs EnCodec (https://github.com/facebookresearch/encodec) instead of soundstream.

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.