Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio Representations

This is the official implementation of the paper Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio Representations. You can find the demo webpage here, and the pretrained model here.

Dependency

• python 3.6+
• pytorch 0.4.0
• h5py 2.8
• tensorboardX We also use some preprocess script from Kyubyong/tacotron.

Preprocess

Our model is trained on CSTR VCTK Corpus.

Feature extraction

We use the code from Kyubyong/tacotron to extract feature. The default paprameters can be found at preprocess/tacotron/norm_utils.py.

The configuration for preprocess is at preprocess/vctk.config, where:

• data_root_dir: the path of VCTK Corpus (VCTK-Corpus).
• h5py_path: the path to store extracted features.
• index_path: the path to store sampled segments.
• traini_proportion: the proportion of training utterances. Default: 0.9.
• n_samples: the number of sampled samples. Default: 500000.
• seg_len: the length of sampled segments. Default: 128.
• speaker_used_path: the path of used speaker list. Our speakers set used in the paper is here.

Once you edited the config file, you can run preprocess.sh to preprocess the dataset.

Training

You can start training by running main.py. The arguments are listed below.

• --load_model: whether to load the model from checkpoint.
• -flag: flag of this training episode for tensorboard. Default: train.
• -hps_path: the path of hyper-parameters set. You can find the default setting at vctk.json.
• --load_model_path: If --load_model is on, it will load the model parameters from this path.
• -dataset_path: the path of processed features (.h5).
• -index_path: the path of sampled segment indexes (.json).
• -output_model_path: the path to store trained model.

Testing

You can inference by running python3 test.py. The arguments are listed below.

• -hps: the path of hyper-parameter set. Default: vctk.json
• -m: the path of model checkpoint to load.
• -s: the path of source .wav file.
• -t: the index of target speaker id (integer). Same order as the speaker list (en_speaker_used.txt).
• -o: output .wav path.
• -sr: sample rate of the output .wav file. Default: 16000.
• --use_gen: if the flag is on, inference will use generator. Default: True.

Reference

Please cite our paper if you find this repository useful.

@article{chou2018multi,
  title={Multi-target voice conversion without parallel data by adversarially learning disentangled audio representations},
  author={Chou, Ju-chieh and Yeh, Cheng-chieh and Lee, Hung-yi and Lee, Lin-shan},
  journal={arXiv preprint arXiv:1804.02812},
  year={2018}
}

Contact

If you have any question about the paper or the code, feel free to email me at jjery2243542@gmail.com.