  In this paper, we present an open-source software for developing a
nonparallel voice conversion (VC) system named crank. Although we have released
an open-source VC software based on the Gaussian mixture model named sprocket
in the last VC Challenge, it is not straightforward to apply any speech corpus
because it is necessary to prepare parallel utterances of source and target
speakers to model a statistical conversion function. To address this issue, in
this study, we developed a new open-source VC software that enables users to
model the conversion function by using only a nonparallel speech corpus. For
implementing the VC software, we used a vector-quantized variational
autoencoder (VQVAE). To rapidly examine the effectiveness of recent
technologies developed in this research field, crank also supports several
representative works for autoencoder-based VC methods such as the use of
hierarchical architectures, cyclic architectures, generative adversarial
networks, speaker adversarial training, and neural vocoders. Moreover, it is
possible to automatically estimate objective measures such as mel-cepstrum
distortion and pseudo mean opinion score based on MOSNet. In this paper, we
describe representative functions developed in crank and make brief comparisons
by objective evaluations.
