Quantizes audio into discrete tokens through several rounds of vector lookup, each round encoding the previous round's error.
Residual vector quantization turns continuous audio features into discrete tokens a language model can predict: the first codebook approximates the vector, the second encodes the leftover error, and so on. More codebooks mean higher fidelity at more tokens per second. Neural audio codecs and most modern TTS/music models are built on this.
Open any of these on hfviewer to find this block in the interactive architecture graph.
hfviewer renders the full architecture of 2,300+ Hugging Face models as interactive graphs - hover any block to see what it does, with this model's real numbers.
Browse all model graphs →