B More Experimental Details

B.1 Hardware Requirements

We use limited computational resources as avail-
able in a university lab. Efficiency is a highlight
of our model, where we only need an inference
time equivalent to the audio length on a consumer
GPU, which is several minutes, while many other
text-to-audio models take many GPU hours (Dhari-
wal et al., 2020; Kreuk et al., 2022). Our model
is very friendly for research at university labs, as
each of our models can be trained on a single A100
GPU in 1 week of training using a batch size of
32; this is equivalent to around 1M steps for both
the diffusion autoencoder and latent generator. For
inference, as an example, a novel audio source of
∼43s can be synthesized in less than 50s using a
consumer GPU with a DDIM sampler and a high
step count (100 generation steps and 100 decoding
steps).

C More Evaluation Details

C.1 Annotation Details for the Genre

Identification Test
Moûsai