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Jukebox for MIR transfer learning

This repository contains code for our paper Codified audio language modeling learns useful representations for music information retrieval (Castellon et al. 2021), which demonstrates that OpenAI's Jukebox (Dhariwal et al. 2020) provides rich representations for music transfer learning.

This README is divided into three standalone sections. The first section, providing a Colab notebook that allows you to perform inference on the full Jukebox model for free, is optimized for simplicity and ease-of-use. The second section requires some setting up, but provides an end-to-end example of genre detection using representations from Jukebox. The third section is optimized for reproducibility and provides step-by-step instructions for reproducing the results from our paper.

In addition, we provide a Python library for extracting representations from Jukebox in just a couple lines of code. Check it out here!

Simple example of inference with Jukebox in Colab

Despite Jukebox's large size, it can be made to run on a single Tesla T4 GPU on Colab for free. Check out the notebook here. In the notebook, you should be able to use the API to extract representations in a couple lines of code:

audio = load_audio_from_file(fname, offset=0.0, duration=25)

representations = get_acts_from_audio(audio=audio,
                                      layers=[36],
                                      meanpool=True)

print(f"Got representations {representations}")
print(f"Its shape is {representations[36].shape}")

Simply pass the audio, layer numbers you're interested in, whether you want to mean-pool, and you'll get representations from Jukebox out. We accomplish these memory savings by initializing the model with the meta device, letting you perform end-to-end inference on any commodity GPU. All in all, the notebook provides certain benefits over the jukemir codebase, including:

a clean API for extracting representations in a customizable manner
compute savings when performing inference on short audio clips
memory-efficient model initialization, enabling extracting representations from later layers in one step

Simple example of using Jukebox for transfer learning

NOTE: the original hosting link for GTZAN appears to be dead, but you can try downloading it from The Internet Archive instead.

This section provides a quick demonstration of using Jukebox for transfer learning on the GTZAN genre detection dataset (Tzanetakis and Cook 2002).

Our codebase uses Docker to simplify the process of extracting representations from Jukebox for new audio files. If you do not already have Docker on your machine, please follow these instructions to install it.

Once docker is installed, run the following to download and extract the dataset:

wget http://opihi.cs.uvic.ca/sound/genres.tar.gz
tar xvfz genres.tar.gz

Next, we will extract representations from Jukebox. This will require a system with at least 30GB of RAM and a GPU with at least 12GB VRAM. Feature extraction will take a few hours (though it can be parallelized). If your system does not meet these requirements, you can alternatively download the pre-computed features. If your system does meet the requirements, run:

for GENRE in blues classical country disco hiphop jazz metal pop reggae rock
do
	echo $GENRE
	docker run \
		-it \
		--rm \
		-v $(pwd)/genres/$GENRE:/input \
		-v $(pwd)/features:/output \
		jukemir/representations_jukebox
done

This pre-built Docker container (jukemir/representations_jukebox) automatically extracts representations for all files in its input folder (/input) and saves a standard numpy array for each file in its output folder (/output). You can use Docker's -v directive to link input and output folders on your host machine to these directories (see above for an example).

Once you've extracted representations from Jukebox, you can train simple classifiers on top of them for different MIR tasks. For example, the following Python script trains and evaluates a simple SVM classifier on GTZAN:

import glob
import os
import random

import numpy as np
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

# Find numpy paths (and randomize to remove label ordering)
npy_paths = sorted(glob.glob('features/*.npy'))
assert len(npy_paths) == 1000
random.seed(0)
random.shuffle(npy_paths)

# Load data
X = np.array([np.load(p) for p in npy_paths])
y = np.array([os.path.split(p)[1].split('.')[0] for p in npy_paths])

# Run cross-validation
clf = make_pipeline(StandardScaler(), SVC())
scores = cross_val_score(clf, X, y, cv=10)
print('{:.1f} +- {:.1f}'.format(np.mean(scores) * 100, np.std(scores) * 100))

This should print out 92.5 +- 2.9, though results may differ slightly due to minor numerical differences. Note that, for simplicity, the above code performs the traditional evaluation for GTZAN which uses 10-fold cross validation. In our paper, we evaluate on the "fault-filtered" GTZAN split from Kereliuk et al. 2015.

Reproducing results from our paper

This section provides step-by-step instructions for reproducing all results from our paper. All code is executed within pre-built Docker containers to increase reproducibility.

Setting up Docker and cache

If you do not already have Docker on your machine, please follow these instructions to install it.

To initialize the default cache directory, run mkdir ~/.jukemir. If you would prefer to use a different directory, set the appropriate environment variable: export JUKEMIR_CACHE_DIR=<your desired directory>.

To launch our pre-built Docker container in the background, navigate to the reproduce folder and run ./0_docker.sh. You may kill and remove this container at any time by typing docker kill jukemir.

Download pre-computed representations (skipping steps 1-3)

Unless you are probing a new dataset or comparing a new representation on an existing dataset, you can skip steps 1-3 by downloading the pre-computed representations via ./123_precomputed.sh. If you need to selectively download particular pre-computed representations, see jukemir/assets/precomputed.json for relevant URLs.

(Step 1) Downloading the datasets

Once Docker is running, run ./1_download.sh to download all of the raw dataset assets from their respective sources. Note that this downloads about 8GB of files and may take quite a long time depending on the speed of your network connection.

The resultant files will be downloaded to the datasets subdirectory of your cache directory (~/.jukemir/cache/datasets by default).

(Step 2) Processing the datasets

Once the raw assets have been downloaded, run ./2_process.sh to process them into a standard format (parses all metadata and decodes MP3s to 16-bit PCM WAV files). Note that this script will also take several hours to run and will produce about 50GB of WAV files.

The resultant files will be saved to the processed subdirectory of your cache directory (~/.jukemir/cache/processed by default).

(Step 3) Extracting representations

Next, we need to extract representations for each WAV file in the processed datasets. Note that this process is resource-intensive (especially for certain representations). Alternatively, you can download pre-computed representations.

Each representation from our paper (chroma, mfcc, choi, musicnn, clmr, jukebox) has been packaged into a pre-built Docker container with a common API. The basic "type signature" of each container takes a folder of WAV files as input and returns a folder of Numpy arrays containing the corresponding representations. For example, if you have a folder in your current working directory called mywavs/, you can extract representations from Jukebox via the following command, which will create a folder of Numpy arrays called mywavs_jukebox/:

docker run \
	-it \
	--rm \
	-v $(pwd)/mywavs:/input \
	-v $(pwd)/mywavs_jukebox:/output \
	jukemir/representations_jukebox

Note that each container also takes two optional arguments as input, --batch_size and --batch_idx, which can be used to compute representations for a subset (batch) of the input WAV file directory. This is useful for parallelizing computation across several workers.

Because choi uses batchnorm during inference, its representations will differ if --batch_size is changed from its default value of 256 (not recommended). All other representations are invariant to batch size. Note that musicnn, clmr, and jukebox should be run on a machine with a GPU.

A Python script at reproduce/3_extract.py will generate all of the Docker commands needed to re-extract all representations for all datasets (see reproduce/3_extract.sh for output). We highly recommend executing these commands in parallel in your own computing environment, as running them one at a time will take a long time.

(Steps 4/5) Configuring and running probing experiments

Individual probing experiments are defined using a jukemir.probe.ProbeExperimentConfig and executed via jukemir.probe.execute_probe_experiment. For example, assuming you've followed the previous steps, you can train a linear probe on gtzan_ff using features from jukebox via the following script:

from jukemir.probe import ProbeExperimentConfig, execute_probe_experiment

cfg = ProbeExperimentConfig(dataset="gtzan_ff", representation="jukebox")
execute_probe_experiment(cfg)

To generate config files for the grid searches described in our paper, run ./4_grid_config.sh. The resultant files will be saved to the probes subdirectory of your cache directory (~/.jukemir/cache/probes by default).

To run all probing experiments one after another, run ./5_grid_train_serial.sh. This will take several days to run to completion. The resultant files will be saved to the probes subdirectory of your cache directory (~/.jukemir/cache/probes by default).

We highly recommend parallelizing this computation in your own environment. For example, you can run this computation in parallel on the Codalab platform by pip-installing the codalab and running ./5_grid_train_codalab.sh.

(Step 6) Evaluating test performance

Once all the probes are done training, run ./6_evaluate.sh to find the runs with the best validation scores for each dataset/representation and compute test performance.

Citation

If you use this codebase in your work, please consider citing our paper:

@inproceedings{castellon2021calm,
  title={Codified audio language modeling learns useful representations for music information retrieval},
  author={Castellon, Rodrigo and Donahue, Chris and Liang, Percy},
  booktitle={ISMIR},
  year={2021}
}