flattening” is similar, but instead of sampling first codebook 1 for all steps, it interleaves them with
the parallel sampling of codebooks 2, 3, and 4. Finally, the “flattening” pattern consists in flattening
all the codebooks, similar to MusicLM [Agostinelli et al., 2023]. We report objective and subjective
evaluations in Table 4. While flattening improves generation, it comes at a high computational cost
and similar performance can be reached for a fraction of this cost using a simple delay approach.
The effect of model size. In Table 5 we report results for different model sizes, namely 300M, 1.5B,
and 3.3B parameter models. As expected, scaling the model size results in better scores, however this
comes at the expense of longer training and inference time. Regarding subjective evaluations, the
overall quality is optimal at 1.5B, but a larger model can better understand the text prompt.
Memorization experiment. Following [Agostinelli et al., 2023], we analyze the memorization
Simple and Controllable Music Generation