Speaker-independent-emotional-voice-conversion-based-on-conditional-VAW-GAN-and-CWT

This is the implementation of the paper "Converting anyone's emotion: steps towards speaker-independent emotional voice conversion". Please kindly cite our paper if you are using our codes.

Getting Started

Prerequisites

• Ubuntu 16.04
• Python 3.6
  • Tensorflow-gpu 1.5.0
  • PyWorld
  • librosa
  • soundfile
  • numpy 1.14.0
  • sklearn
  • glob
  • sprocket-vc
  • pycwt
  • scipy

Usage

1. Activate your virtual enviroment.

source activate [your env]

2. Train VAW-GAN for prosody.

./train_f0.sh

3. Train VAW-GAN for spectrum.

./train_sp.sh

4. Generate the converted emotional speech.

./convert.sh

Note: The codes are based on VAW-GAN Voice Conversion: https://github.com/JeremyCCHsu/vae-npvc/tree/vawgan