Status: Archive (code is provided as-is, no updates expected)
Code for "Jukebox: A Generative Model for Music"
Install the conda package manager from https://docs.conda.io/en/latest/miniconda.html
# Required: Sampling
conda create --name jukebox python=3.7.5
conda activate jukebox
conda install mpi4py=3.0.3 # if this fails, try: pip install 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" ./apex
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.
To use multiple GPU's, launch the above scripts as mpiexec -n {ngpus} python jukebox/sample.py ... so they use {ngpus}
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.
A summary of all sampling data including zs, x, labels and sampling_kwargs is stored in {name}/level_{level}/data.pth.tar.
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.
To continue sampling from already generated codes for a longer duration, you can run
python jukebox/sample.py --model=5b_lyrics --name=sample_5b --levels=3 --mode=continue \
--codes_file=sample_5b/level_0/data.pth.tar --sample_length_in_seconds=40 --total_sample_length_in_seconds=180 \
--sr=44100 --n_samples=6 --hop_fraction=0.5,0.5,0.125
Here, we take the 20 seconds samples saved from the first sampling run at sample_5b/level_0/data.pth.tar and continue by adding 20 more seconds.
You could also continue directly from the level 2 saved outputs, just pass --codes_file=sample_5b/level_2/data.pth.tar.
Note this will upsample the full 40 seconds song at the end.
If you stopped sampling at only the first level and want to upsample the saved codes, you can run
python jukebox/sample.py --model=5b_lyrics --name=sample_5b --levels=3 --mode=upsample \
--codes_file=sample_5b/level_2/data.pth.tar --sample_length_in_seconds=20 --total_sample_length_in_seconds=180 \
--sr=44100 --n_samples=6 --hop_fraction=0.5,0.5,0.125
Here, we take the 20 seconds samples saved from the first sampling run at sample_5b/level_2/data.pth.tar and upsample the lower two levels.
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.
To train a small vqvae, run
mpiexec -n {ngpus} python jukebox/train.py --hps=small_vqvae --name=small_vqvae --sample_length=262144 --bs=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
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 --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 --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.
To get the best sample quality anneal the learning rate to 0 near the end of training. To do so, continue training from the latest checkpoint and run with
--restore_prior="path/to/checkpoint" --lr_use_linear_decay --lr_start_linear_decay={already_trained_steps} --lr_decay={decay_steps_as_needed}
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 train 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 --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
Training the small_prior with a batch size of 2, 4, and 8 requires 6.7 GB, 9.3 GB, and 15.8 GB of GPU memory, respectively. A few days to a week of training typically yields reasonable samples when the dataset is homogeneous (e.g. all piano pieces, songs of the same style, etc).
Near the end of training, follow this to anneal the learning rate to 0
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=("vqvae", "upsampler_level_0", "upsampler_level_1", "small_prior")inMODELSinmake_models.py. - Update the
small_priordictionary inhparams.pyto includerestore_prior='path/to/checkpoint'. If you you changed any hps directly in the command line script (eg:heads), make sure to update them in the dictionary too so thatmake_modelsrestores our checkpoint correctly. - Run sample.py as outlined in the sampling section, but now with
--model=my_model
For example, let's say we trained small_vqvae, small_prior, and small_upsampler under /path/to/jukebox/logs. In make_models.py, we are going to declare a tuple of the new models as my_model.
MODELS = {
'5b': ("vqvae", "upsampler_level_0", "upsampler_level_1", "prior_5b"),
'5b_lyrics': ("vqvae", "upsampler_level_0", "upsampler_level_1", "prior_5b_lyrics"),
'1b_lyrics': ("vqvae", "upsampler_level_0", "upsampler_level_1", "prior_1b_lyrics"),
'my_model': ("my_small_vqvae", "my_small_upsampler", "my_small_prior"),
}
Next, in hparams.py, we add them to the registry with the corresponding restore_paths and any other command line options used during training. Another important note is that for top-level priors with lyric conditioning, we have to locate a self-attention layer that shows alignment between the lyric and music tokens. Look for layers where prior.prior.transformer._attn_mods[layer].attn_func is either 6 or 7. If your model is starting to sing along lyrics, it means some layer, head pair has learned alignment. Congrats!
my_small_vqvae = Hyperparams(
restore_vqvae='/path/to/jukebox/logs/small_vqvae/checkpoint_some_step.pth.tar',
)
my_small_vqvae.update(small_vqvae)
HPARAMS_REGISTRY["my_small_vqvae"] = my_small_vqvae
my_small_prior = Hyperparams(
restore_prior='/path/to/jukebox/logs/small_prior/checkpoint_latest.pth.tar',
level=1,
labels=False,
# TODO For the two lines below, if `--labels` was used and the model is
# trained with lyrics, find and enter the layer, head pair that has learned
# alignment.
alignment_layer=47,
alignment_head=0,
)
my_small_prior.update(small_prior)
HPARAMS_REGISTRY["my_small_prior"] = my_small_prior
my_small_upsampler = Hyperparams(
restore_prior='/path/to/jukebox/logs/small_upsampler/checkpoint_latest.pth.tar',
level=0,
labels=False,
)
my_small_upsampler.update(small_upsampler)
HPARAMS_REGISTRY["my_small_upsampler"] = my_small_upsampler
To train with you own metadata for your audio files, implement get_metadata in data/files_dataset.py to return the
artist, genre and lyrics for a given audio file. For now, you can pass '' for lyrics to not use any lyrics.
For training with labels, we'll use small_labelled_prior in hparams.py, and we set labels=True,labels_v3=True.
We use 2 kinds of labels information:
- Artist/Genre:
- For each file, we return an artist_id and a list of genre_ids. The reason we have a list and not a single genre_id
is that in v2, we split genres like
blues_rockinto a bag of words[blues, rock], and we pass atmostmax_bow_genre_sizeof those, inv3we consider it as a single word and just setmax_bow_genre_size=1. - Update the
v3_artist_idsandv3_genre_idsto use ids from your new dataset. - In
small_labelled_prior, set the hpsy_bins = (number_of_genres, number_of_artists)andmax_bow_genre_size=1.
- For each file, we return an artist_id and a list of genre_ids. The reason we have a list and not a single genre_id
is that in v2, we split genres like
- Timing:
- For each chunk of audio, we return the
total_lengthof the song, theoffsetthe current audio chunk is at and thesample_lengthof the audio chunk. We have three timing embeddings: total_length, our current position, and our current position as a fraction of the total length, and we divide the range of these values intot_binsdiscrete bins. - In
small_labelled_prior, set the hpsmin_durationandmax_durationto be the shortest/longest duration of audio files you want for your dataset, andt_binsfor how many bins you want to discretize timing information into. Notemin_duration * srneeds to be at leastsample_lengthto have an audio chunk in it.
- For each chunk of audio, we return the
After these modifications, to train a top-level with labels, run
mpiexec -n {ngpus} python jukebox/train.py --hps=vqvae,small_labelled_prior,all_fp16,cpu_ema --name=pretrained_vqvae_small_prior_labels \
--sample_length=1048576 --bs=4 --aug_shift --aug_blend --audio_files_dir={audio_files_dir} \
--labels=True --train --test --prior --levels=3 --level=2 --weight_decay=0.01 --save_iters=1000
For sampling, follow same instructions as above but use small_labelled_prior instead of small_prior.
To train in addition with lyrics, update get_metadata in data/files_dataset.py to return lyrics too.
For training with lyrics, we'll use small_single_enc_dec_prior in hparams.py.
- Lyrics:
- For each file, we linearly align the lyric characters to the audio, find the position in lyric that corresponds to
the midpoint of our audio chunk, and pass a window of
n_tokenslyric characters centred around that. - In
small_single_enc_dec_prior, set the hpsuse_tokens=Trueandn_tokensto be the number of lyric characters to use for an audio chunk. Set it according to thesample_lengthyou're training on so that its large enough that the lyrics for an audio chunk are almost always found inside a window of that size. - If you use a non-English vocabulary, update
text_processor.pywith your new vocab and setn_vocab = number of characters in vocabularyaccordingly insmall_single_enc_dec_prior. In v2, we had an_vocab=80and in v3 we missed+and son_vocab=79of characters.
- For each file, we linearly align the lyric characters to the audio, find the position in lyric that corresponds to
the midpoint of our audio chunk, and pass a window of
After these modifications, to train a top-level with labels and lyrics, run
mpiexec -n {ngpus} python jukebox/train.py --hps=vqvae,small_single_enc_dec_prior,all_fp16,cpu_ema --name=pretrained_vqvae_small_single_enc_dec_prior_labels \
--sample_length=786432 --bs=4 --aug_shift --aug_blend --audio_files_dir={audio_files_dir} \
--labels=True --train --test --prior --levels=3 --level=2 --weight_decay=0.01 --save_iters=1000
To simplify hps choices, here we used a single_enc_dec model like the 1b_lyrics model that combines both encoder and
decoder of the transformer into a single model. We do so by merging the lyric vocab and vq-vae vocab into a single
larger vocab, and flattening the lyric tokens and the vq-vae codes into a single sequence of length n_ctx + n_tokens.
This uses attn_order=12 which includes prime_attention layers with keys/values from lyrics and queries from audio.
If you instead want to use a model with the usual encoder-decoder style transformer, use small_sep_enc_dec_prior.
For sampling, follow same instructions as above but use small_single_enc_dec_prior instead of
small_prior. To also get the alignment between lyrics and samples in the saved html, you'll need to set alignment_layer
and alignment_head in small_single_enc_dec_prior. To find which layer/head is best to use, run a forward pass on a training example,
save the attention weight tensors for all prime_attention layers, and pick the (layer, head) which has the best linear alignment
pattern between the lyrics keys and music queries.
Previously, we showed how to train a small top-level prior from scratch. Assuming you have a GPU with at least 15 GB of memory and support for fp16, you could fine-tune from our pre-trained 1B top-level prior. Here are the steps:
- Support
--labels=Trueby implementingget_metadatainjukebox/data/files_dataset.pyfor your dataset. - Add new entries in
jukebox/data/ids. We recommend replacing existing mappings (e.g. rename"unknown", etc with styles of your choice). This uses the pre-trained style vectors as initialization and could potentially save some compute.
After these modifications, run
mpiexec -n {ngpus} python jukebox/train.py --hps=vqvae,prior_1b_lyrics,all_fp16,cpu_ema --name=finetuned \
--sample_length=1048576 --bs=1 --aug_shift --aug_blend --audio_files_dir={audio_files_dir} \
--labels=True --train --test --prior --levels=3 --level=2 --weight_decay=0.01 --save_iters=1000
To get the best sample quality, it is recommended to anneal the learning rate in the end. Training the 5B top-level requires GPipe which is not supported in this release.
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:2005.00341},
year={2020}
}
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