  We introduce Noise2Music, where a series of diffusion models is trained to
generate high-quality 30-second music clips from text prompts. Two types of
diffusion models, a generator model, which generates an intermediate
representation conditioned on text, and a cascader model, which generates
high-fidelity audio conditioned on the intermediate representation and possibly
the text, are trained and utilized in succession to generate high-fidelity
music. We explore two options for the intermediate representation, one using a
spectrogram and the other using audio with lower fidelity. We find that the
generated audio is not only able to faithfully reflect key elements of the text
prompt such as genre, tempo, instruments, mood, and era, but goes beyond to
ground fine-grained semantics of the prompt. Pretrained large language models
play a key role in this story -- they are used to generate paired text for the
audio of the training set and to extract embeddings of the text prompts
ingested by the diffusion models.
  Generated examples: https://google-research.github.io/noise2music
