python -m data.train_test_data_extraction.extract_data_for_training --base_dataset_path <base_path> --speaker <speaker_name> -np <number of processes> --speaker <speaker name>`

once done you should see the following directories structure:
(notice train.csv and a train folder within the relevant speaker)

Gestures
├── frames.csv
├── train.csv
├── almaram
    ├── frames
    ├── videos
    ├── keypoints_all
    ├── keypoints_simple
    ├── videos
    └── train
...
└── shelly
    ├── frames
    ├── videos
    ├── keypoints_all
    ├── keypoints_simple
    ├── videos
    └── train

audio_fn - path to audio filename associated with training sample
dataset - train/dev/test
start - start time in the video
end - end time in the video
pose_fn - path to .npz file containing training sample
speaker - name of a speaker in the dataset
video_fn - name of the video file

python -m audio_to_multiple_pose_gan.train --gans 1 --name test_run --arch_g audio_to_pose_gans --arch_d pose_D --speaker oliver --output_path /tmp

python -m audio_to_multiple_pose_gan.predict_to_videos --train_csv <path/to/train.csv>--seq_len 64 --output_path </tmp/my_output_folder> --checkpoint <model checkpoint path> --speaker <speaker_name> -ag audio_to_pose_gans --gans 1

python -m audio_to_multiple_pose_gan.predict_audio --audio_path <path_to_file.wav> --output_path </tmp/my_output_folder> --checkpoint <model checkpoint path> --speaker <speaker_name> -ag audio_to_pose_gans --gans 1

@InProceedings{ginosar2019gestures,
  author={S. Ginosar and A. Bar and G. Kohavi and C. Chan and A. Owens and J. Malik},
  title = {Learning Individual Styles of Conversational Gesture},
  booktitle = {Computer Vision and Pattern Recognition (CVPR)}
  publisher = {IEEE},
  year={2019},
  month=jun
}