Baselines. We compare MUSICGEN to two baselines for text-to-music generation: Riffusion [Fors-
gren and Martiros] and Mousai [Schneider et al., 2023]. We use the open source Riffusion model
to run inference 3. For Mousai, we train a model using our dataset for a fair comparison, using the
open source implementation provided by the authors4. Additionally, when possible, we compare to
MusicLM [Agostinelli et al., 2023] and Noise2Music [Huang et al., 2023b].
Evaluation metrics. We evaluate the proposed method using objective and subjective metrics. For
the objective methods, we use three metrics: the Fréchet Audio Distance (FAD), the Kullback-Leiber
Divergence (KL) and the CLAP score (CLAP). We report the FAD [Kilgour et al., 2018] using the
official implementation in Tensorflow with the VGGish model 5. A low FAD score indicates the
generated audio is plausible. Following Kreuk et al. [2022], we use a state-of-the-art audio classifier
Simple and Controllable Music Generation