  Machine learning based singing voice models require large datasets and
lengthy training times. In this work we present a lightweight architecture,
based on the Differentiable Digital Signal Processing (DDSP) library, that is
able to output song-like utterances conditioned only on pitch and amplitude,
after twelve hours of training using small datasets of unprocessed audio. The
results are promising, as both the melody and the singer's voice are
recognizable. In addition, we present two zero-configuration tools to train new
models and experiment with them. Currently we are exploring the latent space
representation, which is included in the DDSP library, but not in the original
DDSP examples. Our results indicate that the latent space improves both the
identification of the singer as well as the comprehension of the lyrics. Our
code is available at https://github.com/juanalonso/DDSP-singing-experiments
with links to the zero-configuration notebooks, and our sound examples are at
https://juanalonso.github.io/DDSP-singing-experiments/ .
