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Acting Workshops

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Ian Gonzalez
Ian Gonzalez

OpenAI Jukebox: A Revolutionary Tool for Music Lovers and Creators



If you stopped sampling at only the first level and want to upsample the saved codes, you can runpython 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=0.5,0.5,0.125Here, 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.


To train a small vqvae, runmpiexec -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_blendHere, 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 Tensorboardtensorboard --logdir logs




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To train top-level on a new dataset, runmpiexec -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=1000Training 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).


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!```mysmallvqvae = Hyperparams( restorevqvae='/path/to/jukebox/logs/smallvqvae/checkpointsomestep.pth.tar',)mysmallvqvae.update(smallvqvae)HPARAMSREGISTRY["mysmallvqvae"] = mysmallvqvae


mysmallprior = Hyperparams( restoreprior='/path/to/jukebox/logs/smallprior/checkpointlatest.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. alignmentlayer=47, alignmenthead=0,)mysmallprior.update(smallprior)HPARAMSREGISTRY["mysmallprior"] = mysmall_prior


After these modifications, to train a top-level with labels and lyrics, runmpiexec -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=1000To 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.


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=1000To 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.


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All endpoints have a .create method that supports a request_timeout param. This param takes a Union[float, Tuple[float, float]] and will raise an openai.error.Timeout error if the request exceeds that time in seconds (See: ).


Private companies, like AIVA and Soundful, are also offering AI music generation for licensing. Their user-friendly interfaces are built for social media content creators that want to license music at a lower cost. Users create an account, choose a genre, generate audio, and then download the original music for their projects.


A jukebox is an iconic piece of musical history that has been around for decades. It is a coin-operated machine that allows people to choose from a selection of self-contained media such as records or compact discs. It is a fun and nostalgic way to listen to music.


Thank you for considering downloading a music disc to your computer. Downloading music to your jukebox is easy with a few simple steps. First, select the music disc you would like to have from a music store, either online or in a physical store. Once you have selected the disc, right click on the jukebox to play the music. You should be able to find a link to listen to the music through your jukebox.


If you need some help getting started, there are plenty of helpful tutorials available online. Whether you are downloading music legally or illegally, it is important to be mindful of copyright laws and regulations. Please keep this in mind as you download and share music.


A jukebox is a music-playing device that is usually coin-operated. It allows users to select a specific song or album to play. Jukeboxes typically have buttons with letters and numbers that are used to select a particular song. Some jukeboxes may use compact discs instead of records.


The jukebox is a block that can play music discs. The sound from the jukebox travels roughly 65 blocks in all directions. It supports all available music discs in the game. In Bedrock Edition, hoppers and droppers can be used to insert a disc into a jukebox.


The standard location for the volume control knob is in the middle of the back of the jukebox against the wall. This is so that people can easily adjust the volume without having to reach around the back of the machine.


Jukeboxes were first introduced in the early 1900s and became very popular in the 1950s. They were often found in restaurants, bars and other public places. Today, jukeboxes are not as common as they once were, but can still be found in some establishments. They are also available as digital devices that can be connected to a computer or television.


To use the Openai Jukebox, first you need to download the software from the Openai website. Once you have downloaded the software, you need to unzip the file and then double-click on the openai-jukebox icon to launch the program.


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