  There are many use cases in singing synthesis where creating voices from
small amounts of data is desirable. In text-to-speech there have been several
promising results that apply voice cloning techniques to modern deep learning
based models. In this work, we adapt one such technique to the case of singing
synthesis. By leveraging data from many speakers to first create a multispeaker
model, small amounts of target data can then efficiently adapt the model to new
unseen voices. We evaluate the system using listening tests across a number of
different use cases, languages and kinds of data.
