Status: Archive (code is provided as-is, no updates expected)
Code for "Jukebox: A Generative Model for Music"
# Required: Sampling
conda create --name jukebox python=3.7.5
conda activate jukebox
conda install -c anaconda mpi4py=3.0.3
conda install pytorch=1.4 torchvision=0.5 cudatoolkit=10.0 -c pytorch
git clone https://github.com/openai/jukebox.git
cd jukebox
pip install -r requirements.txt
pip install -e .
# Required: Training
conda install av=7.0.01 -c conda-forge
pip install ./tensorboardX
# Optional: Apex for faster training with fused_adam
conda install pytorch=1.1 torchvision=0.3 cudatoolkit=10.0 -c pytorch
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./apexTODO: Code to be upgraded to latest Pytorch for installation with latest Python 3.x libraries. This doesn't currently work
# Required: Sampling
conda create --name jukebox python=3.7.5
conda activate jukebox
conda install -c intel mpi4py=3.0.3
conda install pytorch=1.4 torchvision=0.5 cudatoolkit=10.0 -c pytorch
git clone https://github.com/openai/jukebox.git
cd jukebox
pip install -r requirements.txt
pip install -e .
# Required: Training
conda install av=7.0.01 -c conda-forge
pip install ./tensorboardX
# Optional: Apex for faster training with fused_adam
conda install pytorch=1.1 torchvision=0.3 cudatoolkit=10.0 -c pytorch
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./apexMPI support is provided via mpi4py dependency.
https://mpi4py.readthedocs.io/en/stable/
Tensorflow has an install for mpiexec under /tensorflow/tools/ci_build/install/install_mpi.sh https://arxiv.org/abs/1603.02339
openmpi-bin
libopenmpi-dev
Microsoft MPI v10.0
https://www.microsoft.com/en-us/download/details.aspx?id=57467
Add C:\Program Files (x86)\Microsoft SDKs\MPI, C:\Program Files\Microsoft MPI\Bin (or appropriate directory on your machine) to System or User path and execute refreshenv in command line.
## Sampling
To sample normally, run the following command. Model can be `5b`, `5b_lyrics`, `1b_lyrics`
python jukebox/sample.py --model=5b_lyrics --name=sample_5b --levels=3 --sample_length_in_seconds=20 --total_sample_length_in_seconds=180 --sr=44100 --n_samples=6 --hop_fraction=0.5,0.5,0.125
python jukebox/sample.py --model=1b_lyrics --name=sample_1b --levels=3 --sample_length_in_seconds=20 --total_sample_length_in_seconds=180 --sr=44100 --n_samples=16 --hop_fraction=0.5,0.5,0.125
The above generates the first `sample_length_in_seconds` seconds of audio from a song of total length `total_sample_length_in_seconds`.
The samples decoded from each level are stored in `{name}/level_{level}`.
You can also view the samples as an html with the aligned lyrics under `{name}/level_{level}/index.html`. Run `python -m http.server` and open the html through the server to see the lyrics animate as the song plays.
The hps are for a V100 GPU with 16 GB GPU memory. The `1b_lyrics`, `5b`, and `5b_lyrics` top-level priors take up 3.8 GB, 10.3 GB, and 11.5 GB, respectively. The peak memory usage to store transformer key, value cache is about 400 MB for `1b_lyrics` and 1 GB for `5b_lyrics` per sample. If you are having trouble with CUDA OOM issues, try `1b_lyrics` or decrease `max_batch_size` in sample.py, and `--n_samples` in the script call.
On a V100, it takes about 3 hrs to fully sample 20 seconds of music. Since this is a long time, it is recommended to use `n_samples > 1` so you can generate as many samples as possible in parallel. The 1B lyrics and upsamplers can process 16 samples at a time, while 5B can fit only up to 3. Since the vast majority of time is spent on upsampling, we recommend using a multiple of 3 less than 16 like `--n_samples 15` for `5b_lyrics`. This will make the top-level generate samples in groups of three while upsampling is done in one pass.
If you want to prompt the model with your own creative piece or any other music, first save them as wave files and run
python jukebox/sample.py --model=5b_lyrics --name=sample_5b_prompted --levels=3 --mode=primed --audio_file=path/to/recording.wav,awesome-mix.wav,fav-song.wav,etc.wav --prompt_length_in_seconds=12 --sample_length_in_seconds=20 --total_sample_length_in_seconds=180 --sr=44100 --n_samples=6 --hop_fraction=0.5,0.5,0.125
This will load the four files, tile them to fill up to `n_samples` batch size, and prime the model with the first `prompt_length_in_seconds` seconds.
## Training
## VQVAE
To train a small vqvae, run
mpiexec -n {ngpus} python jukebox/train.py --hps=small_vqvae --name=small_vqvae --sample_length=262144 --bs=4 --nworkers=4 --audio_files_dir={audio_files_dir} --labels=False --train --aug_shift --aug_blend
Here, `{audio_files_dir}` is the directory in which you can put the audio files for your dataset, and `{ngpus}` is number of GPU's you want to use to train.
The above trains a two-level VQ-VAE with `downs_t = (5,3)`, and `strides_t = (2, 2)` meaning we downsample the audio by `2**5 = 32` to get the first level of codes, and `2**8 = 256` to get the second level codes.
Checkpoints are stored in the `logs` folder. You can monitor the training by running Tensorboard
tensorboard --logdir logs
## Prior
### Train prior or upsamplers
Once the VQ-VAE is trained, we can restore it from its saved checkpoint and train priors on the learnt codes.
To train the top-level prior, we can run
mpiexec -n {ngpus} python jukebox/train.py --hps=small_vqvae,small_prior,all_fp16,cpu_ema --name=small_prior --sample_length=2097152 --bs=4 --nworkers=4 --audio_files_dir={audio_files_dir} --labels=False --train --test --aug_shift --aug_blend --restore_vqvae=logs/small_vqvae/checkpoint_latest.pth.tar --prior --levels=2 --level=1 --weight_decay=0.01 --save_iters=1000
To train the upsampler, we can run
mpiexec -n {ngpus} python jukebox/train.py --hps=small_vqvae,small_upsampler,all_fp16,cpu_ema --name=small_upsampler --sample_length 262144 --bs 4 --nworkers 4 --audio_files_dir {audio_files_dir} --labels False --train --test --aug_shift --aug_blend --restore_vqvae logs/small_vqvae/checkpoint_latest.pth.tar --prior --levels 2 --level 0 --weight_decay 0.01 --save_iters 1000
We pass `sample_length = n_ctx * downsample_of_level` so that after downsampling the tokens match the n_ctx of the prior hps.
Here, `n_ctx = 8192` and `downsamples = (32, 256)`, giving `sample_lengths = (8192 * 32, 8192 * 256) = (65536, 2097152)` respectively for the bottom and top level.
### Reuse pre-trained VQ-VAE and retrain top level prior on new dataset.
Our pre-trained VQ-VAE can produce compressed codes for a wide variety of genres of music, and the pre-trained upsamplers can upsample them back to audio that sound very similar to the original audio.
To re-use these for a new dataset of your choice, you can retrain just the top-level
To retrain top-level on a new dataset, run
mpiexec -n {ngpus} python jukebox/train.py --hps=vqvae,small_prior,all_fp16,cpu_ema --name=pretrained_vqvae_small_prior --sample_length=1048576 --bs=4 --nworkers=4 --bs_sample=4 --aug_shift --aug_blend --audio_files_dir={audio_files_dir} --labels=False --train --test --prior --levels=3 --level=2 --weight_decay=0.01 --save_iters=1000
You can then run sample.py with the top-level of our models replaced by your new model. To do so, add an entry `my_model` in MODELs (in `make_models.py`) with the (vqvae hps, upsampler hps, top-level prior hps) of your new model, and run sample.py with `--model=my_model`.
## Citation
Please cite using the following bibtex entry:
@article{dhariwal2020jukebox, title={Jukebox: A Generative Model for Music}, author={Dhariwal, Prafulla and Jun, Heewoo and Payne, Christine and Kim, Jong Wook and Radford, Alec and Sutskever, Ilya}, journal={arXiv preprint arXiv:[TODO]}, year={2020} }