PyTorch implementation of the method described in the A Universal Music Translation Network.

We present a method for translating music across musical instruments and styles. This method is based on unsupervised training of a multi-domain wavenet autoencoder, with a shared encoder and a domain-independent latent space that is trained end-to-end on waveforms.

This repository includes fast-inference kernels written by nvidia. The kernels were modified to match our architecture as detailed in the paper.

• Samples
• Setup
• Training
• Inference

Requirements:

• Linux
• Python 3.7
• PyTorch v1.0.1
• Install python packages

  git clone https://github.com/facebookresearch/music-translation.git
  cd music-translation
  pip install -r requirements.txt
  

• Install fast inference wavenet kernels (requires Tesla V100 GPU):

  cd src/nv-wavenet
  python setup.py install
  

1. Download MusicNet dataset from here into musicnet folder.
2. Extract specific domains from MusicNet dataset:

   python src/parse_musicnet.py -i musicnet -o musicnet/parsed
   

3. Split into train/test/val

   for d in musicnet/parsed/*/ ; do python src/split_dir.py -i $d -o musicnet/split/$(basename "$d"); done
   

4. Preprocess audio for training

   python src/preprocess.py -i musicnet/split -o musicnet/preprocessed
   

Pretrained models are available here.

We provide training instructions for both single node and multi-node training. Our results were achieved following 3 days of training on 6 nodes, each with 8 gpus.

Execute the train script:

./train.sh

Trained models will be saved in the checkpoints/musicnet directory.

To train in Multi-Node training, use the follow the instructions below. NOTE: Multi-Node training requires a single node per dataset.

Execute the train script (on each node):

train_dist.sh <nnodes> <node_rank> <master_address>

The parameters are as follows:

<nnodes>            - Number of nodes participating in the training.
<node_rank>         - The rank of the current node.
<master_address>    - Address of the master node.

We provide both interactive/offline generation scripts based on nv-wavenet CUDA kernels:

1. notebooks/Generate.ipynb - interactive generation.
2. sample.sh <checkpiont name> "<decoders ids>" - samples short segments of audio from each dataset and converts to the domains the method was trained on. Running the script will create a result\DATE directory, with subfolder for each decoder. See src/run_on_files.py for more details.

In addition, we provide python-based generation:

1. sample_py.sh <checkpiont name> "<decoders ids>" - same as 2. above.

You may find out more about the license here.

@inproceedings{musictranslation,
    title     = {A Universal Music Translation Network},
    author    = {Noam Mor, Lior Wold, Adam Polyak and Yaniv Taigman},
    booktitle = {International Conference on Learning Representations (ICLR)},
    year      = {2019},
}