This repository provides a PyTorch implementation of AutoPST, which enables unsupervised global prosody conversion without text transcriptions.

This is a short video that explains the main concepts of our work. If you find this work useful and use it in your research, please consider citing our paper.

@InProceedings{pmlr-v139-qian21b,
  title = 	 {Global Prosody Style Transfer Without Text Transcriptions},
  author =       {Qian, Kaizhi and Zhang, Yang and Chang, Shiyu and Xiong, Jinjun and Gan, Chuang and Cox, David and Hasegawa-Johnson, Mark},
  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {8650--8660},
  year = 	 {2021},
  editor = 	 {Meila, Marina and Zhang, Tong},
  volume = 	 {139},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {18--24 Jul},
  publisher =    {PMLR},
  url = 	 {http://proceedings.mlr.press/v139/qian21b.html}
}

The audio demo for AutoPST can be found here

• Python 3.6
• Numpy
• Scipy
• PyTorch == v1.6.0
• librosa
• pysptk
• soundfile
• wavenet_vocoder pip install wavenet_vocoder==0.1.1 for more information, please refer to https://github.com/r9y9/wavenet_vocoder

Download 580000-P.ckpt for predictor to assets

Download sea.ckpt for sea to assets

Download checkpoint_step001000000_ema.pth for model to assets

Download the same WaveNet vocoder model as in AutoVC to assets using pip install wavenet_vocoder==0.1.1

The fast and high-quality hifi-gan v1 (https://github.com/jik876/hifi-gan) pre-trained model is now available here.

Added systhesis.py and hparams.py to make the code workable.

Input files for the demo should be placed in assets/inputs/wave, and assets/outputs files will be generated in assets/output.

Run demo.ipynb

This project is part of an ongoing research. We hope this repo is useful for your research. If you need any help or have any suggestions on improving the framework, please raise an issue and we will do our best to get back to you as soon as possible.