Table 2: Comparison of our Moûsai model with previous music/audio generation models. We compare the followings
aspects: (1) audio sample rate@the number of channels (Sample Rate↑, where the higher the better), (2) context
length of the generated music (Len.↑, where the higher the more capable the model is to generate structural music; ⋆
indicates variable length, where we assume that autoregressive methods are variable by default, with an upper-bound
imposed by attention), (3) input type (Input, where we feature using Text as the condition for the generation), (4)
type of the generate music (Music, where the more Diverse↑ genre, the better), (5) an example of the generated
music type (Example), (6) inference time (Infer. Time↓, where the shorter the better, and since the music length is
seconds or minutes, the inference time equivalent to the audio length is the shortest, and we use ⋆ to show models
Moûsai