Memorization experiment. Following [Agostinelli et al., 2023], we analyze the memorization
abilities of MUSICGEN. We only consider the first stream of codebooks from MUSICGEN as it
contains the coarser-grain information. We randomly select N = 20, 000 examples from our training
set and for each one we feed the model with a prompt of EnCodec codebooks corresponding to the
original audio and the conditioning information. We generate a continuation of 250 audio tokens (5
second-long audio) using greedy decoding. We report exact matches as the fraction of examples for
which the generated audio tokens and source audio tokens are identical over the whole sequence. In
addition, we report partial matches as the fraction of the training examples for which the generated
and source sequences have at least 80% of the audio tokens matching. We present the memorization
results for the different model sizes when varying the length of the audio prompt in Figure 2b.
Simple and Controllable Music Generation