Usage¶

This package should nicely integrate with your existing python code, thus makes it easy to participate in the MUSDB tasks. The core of this package is calling a user-provided function that separates the mixtures from the musdb into several estimated target sources.

Providing a compatible function¶

The core of this package consists of calling a user-provided function which separates the mixtures from the musdb into estimated target sources.

The function will take an musdb Track object which can be used from inside your algorithm.
Participants can access
Track.audio, representing the stereo mixture as an np.ndarray of shape=(nun_sampl, 2)
Track.rate, the sample rate
Track.path, the absolute path of the mixture which might be handy to process with external applications, so that participants don’t need to write out temporary wav files.
The function needs to return a python Dict which consists of target name (key) and the estimated target as audio arrays with same shape as the mixture (value).
It is the users choice which target sources they want to provide for a given mixture. Supported targets are ['vocals', 'accompaniment', 'drums', 'bass', 'other'].
Please make sure that the returned estimates do have the same sample rate as the mixture track.

Here is an example for such a function separating the mixture into a vocals and accompaniment track.

def my_function(track):

    # get the audio mixture as numpy array shape=(nun_sampl, 2)
    track.audio

    # compute voc_array, acc_array
    # ...

    return {
        'vocals': voc_array,
        'accompaniment': acc_array
    }

Create estimates for SiSEC evaluation¶

Setting up musdb¶

Simply import the musdb package in your main python function:

import musdb

mus = musdb.DB(
    root_dir='path/to/musdb/',
)

The root_dir is the path to the musdb dataset folder. Instead of root_dir it can also be set system-wide. Just export MUSDB_PATH=/path/to/musdb inside your terminal environment.

Test if your separation function generates valid output¶

Before you run the full 150 tracks, which might take very long, participants can test their separation function by running:

mus.test(my_function)

This test makes sure the user provided output is compatible to the musdb framework. The function returns True if the test succeeds.

Processing the full musdb¶

To process all 150 musdb tracks and saves the results to the estimates_dir:

mus.run(my_function, estimates_dir="path/to/estimates")

Processing training and testing subsets separately¶

Algorithms which make use of machine learning techniques can use the training subset and then apply the algorithm on the test data:

mus.run(my_training_function, subsets="train")
mus.run(my_test_function, subsets="test")

Access the reference signals / targets¶

For supervised learning you can use the provided reference sources by loading the track.targets dictionary. E.g. to access the vocal reference from a track:

track.targets['vocals'].audio

Use multiple cores¶

Python Multiprocessing¶

To speed up the processing, run can make use of multiple CPUs:

mus.run(my_function, parallel=True, cpus=4)

Note: We use the python builtin multiprocessing package, which sometimes is unable to parallelize the user provided function to PicklingError.