What does this equation mean?

The mechanism that does this at scale is a learned neural codec built around residual vector quantization: an encoder compresses the waveform into a sequence of continuous frames, and each frame is then quantized in successive stages, each stage encoding what the previous stage’s codebook missed. SoundStream is the reference architecture, described by its authors as an end-to-end neural audio codec built on a convolutional encoder-decoder pair with a residual vector quantizer, trained jointly and shown to outperform prior codecs at comparable bitrates [ 7 ] . The mechanism gives a concrete, computable token rate. For a signal sampled at $f_s$ hertz, encoded with hop length h samples per frame, and quantized with $n_q$ residual stages of codebook size K each, the bitrate is $R = \frac{f_s}{h} \cdot n_q \cdot \log_2 K \text{ bits per second.}$. Every added quantizer stage buys reconstruction fidelity at a fixed, computable token-rate cost — the audio-codec analogue of the video tokenizer’s frame-versus-resolution trade, and just as unavoidable.