Downloading the database

JTD is now integrated within the latest version of mirdata, and this is the recommended way to work with the database annotations moving forwards. To install mirdata, run the following code (best inside a virtualenv):

pip install git+https://github.com/mir-dataset-loaders/mirdata.git

Now, you can download the dataset and access the annotations simply by running the following lines of Python code:

import mirdata

jtd = mirdata.initialize('jtd')
jtd.download()

Next, we show how the metadata for a single performance can be loaded:

# Choose a random multitrack
multi_track = jtd.choice_multitrack()

# Access properties of the performance
name = multi_track.name
tempo = multi_track.tempo
time_signature = multi_track.time_signature
print(name, tempo, time_signature)

The final example loads MIDI from a single piano track into the pretty-midi library (a common Python package for working with MIDI data) and plots a “piano roll” representation.

import seaborn as sns
from pretty_midi import PrettyMIDI

# Access the piano MIDI
piano = multi_track.piano    # alternatively, .bass, .drums
midi = piano.midi

# Unpack the MIDI attributes from the mirdata representation
pitches = midi.pitches.astype(int)
starts, ends = midi.intervals[:, 0], midi.intervals[:, 1]
velocities = midi.confidence.astype(int)
all_data = zip(starts, ends, velocities, pitches)

# Convert to pretty_midi format
pm_notes = [
    Note(start=s, end=e, pitch=p, velocity=v) 
    for (s, e, p, v) in all_data
]
instrument = Instrument(program=0)
instrument.notes = pm_notes

# Compute the piano roll with the default frames-per-second and display
piano_roll = instrument.get_piano_roll(fs=100)
sns.heatmap(piano_roll)

Further examples can be found in the mirdata documentation.

Although it is not recommended, to download the raw database annotations without mirdata, navigate to Releases and download the most recent .zip file.