AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss

This is an unofficial implementation of AutoVC based on the official one. The D-Vector and vocoder are from yistlin/dvector and yistLin/universal-vocoder respectively.

This implementation supports torch.jit, so the full model can be loaded with simply one line:

model = torch.jit.load(model_path)

Pre-trained models are available here.

Preprocessing

python preprocess.py <data_dir> <save_dir> <encoder_path> [--seg_len seg] [--n_workers workers]

• data_dir: The directory of speakers.
• save_dir: The directory to save the processed files.
• encoder_path: The path of pre-trained D-Vector.
• seg: The length of segments for training.
• workers: The number of workers for preprocessing.

Training

python train.py <config_path> <data_dir> <save_dir> [--n_steps steps] [--save_steps save] [--log_steps log] [--batch_size batch] [--seg_len seg]

• config_path: The config file of model hyperparameters.
• data_dir: The directory of preprocessed data.
• save_dir: The directory to save the model.
• steps: The number of training steps.
• save: To save the model every save steps.
• log: To record training information every log steps.
• batch: The batch size.
• seg: The length of segments for training.

Inference

python inference.py <model_path> <vocoder_path> <source> <target> <output>

• model_path: The path of the model file.
• vocoder_path: The path of the vocoder file.
• source: The utterance providing linguistic content.
• target: The utterance providing target speaker timbre.
• output: The converted utterance.

Reference

Please cite the paper if you find it useful.

@InProceedings{pmlr-v97-qian19c,
  title = {{A}uto{VC}: Zero-Shot Voice Style Transfer with Only Autoencoder Loss},
  author = {Qian, Kaizhi and Zhang, Yang and Chang, Shiyu and Yang, Xuesong and Hasegawa-Johnson, Mark},
  pages = {5210--5219},
  year = {2019},
  editor = {Kamalika Chaudhuri and Ruslan Salakhutdinov},
  volume = {97},
  series = {Proceedings of Machine Learning Research},
  address = {Long Beach, California, USA},
  month = {09--15 Jun},
  publisher = {PMLR},
  pdf = {http://proceedings.mlr.press/v97/qian19c/qian19c.pdf},
  url = {http://proceedings.mlr.press/v97/qian19c.html}
}