The article is talking about doing exactly that. The key question is how to convert an inherently continuous signal (speech/audio) into a discrete set of tokens. A single window of audio is usually somewhere between 10ms and 100ms. It's difficult to squish all that information down to a single "token" that represents the semantic and acoustic content for that window.
That's why residual vector quantization is a useful technique - using multiple dictionaries to quantize a single timeslice, each conditioned on the previous residual level. You can also quantize a signal at different frequencies.
There are samples towards the end of the post of their LLM trained on their Mimi audio codec.
That's why residual vector quantization is a useful technique - using multiple dictionaries to quantize a single timeslice, each conditioned on the previous residual level. You can also quantize a signal at different frequencies.
There are samples towards the end of the post of their LLM trained on their Mimi audio codec.