# crank: An Open-Source Software for Nonparallel Voice Conversion Based on Vector-Quantized Variational Autoencoder

arXiv: https://arxiv.org/abs/2103.02858 · HTML: https://mlsanity.com/paper/2103.02858/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2021.

## Built on by (most cited first, 6 of 6)

- [Towards Identity Preserving Normal to Dysarthric Voice Conversion](https://mlsanity.com/paper/2110.08213/) (2021)
- [Configurable Privacy-Preserving Automatic Speech Recognition](https://mlsanity.com/paper/2104.00766/) (2021)
- [A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion](https://mlsanity.com/paper/2106.01415/) (2021)
- [Read the Room: Adapting a Robot's Voice to Ambient and Social Contexts](https://mlsanity.com/paper/2205.04952/) (2022)
- [Non-Parallel Voice Conversion for ASR Augmentation](https://mlsanity.com/paper/2209.06987/) (2022)
- [Converting Anyone's Voice: End-to-End Expressive Voice Conversion with a Conditional Diffusion Model](https://mlsanity.com/paper/2405.01730/) (2024)

## Abstract (arXiv)

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.