groove

• Description:

The Groove MIDI Dataset (GMD) is composed of 13.6 hours of aligned MIDI and (synthesized) audio of human-performed, tempo-aligned expressive drumming captured on a Roland TD-11 V-Drum electronic drum kit.

• Homepage: https://g.co/magenta/groove-dataset

• Source code: tfds.audio.Groove

• Versions:

  • 2.0.1 (default): No release notes.

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Citation:

@inproceedings{groove2019,
    Author = {Jon Gillick and Adam Roberts and Jesse Engel and Douglas Eck and David Bamman},
    Title = {Learning to Groove with Inverse Sequence Transformations},
    Booktitle   = {International Conference on Machine Learning (ICML)}
    Year = {2019},
}

groove/full-midionly (default config)

• Config description: Groove dataset without audio, unsplit.

• Download size: 3.11 MiB

• Dataset size: 5.22 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'test'	129
'train'	897
'validation'	124

• Features:

FeaturesDict({
    'bpm': tf.int32,
    'drummer': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),
    'id': tf.string,
    'midi': tf.string,
    'style': FeaturesDict({
        'primary': ClassLabel(shape=(), dtype=tf.int64, num_classes=18),
        'secondary': tf.string,
    }),
    'time_signature': ClassLabel(shape=(), dtype=tf.int64, num_classes=5),
    'type': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
})

• Examples (tfds.as_dataframe):

groove/full-16000hz

• Config description: Groove dataset with audio, unsplit.

• Download size: 4.76 GiB

• Dataset size: 2.33 GiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'test'	124
'train'	846
'validation'	120

• Features:

FeaturesDict({
    'audio': Audio(shape=(None,), dtype=tf.float32),
    'bpm': tf.int32,
    'drummer': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),
    'id': tf.string,
    'midi': tf.string,
    'style': FeaturesDict({
        'primary': ClassLabel(shape=(), dtype=tf.int64, num_classes=18),
        'secondary': tf.string,
    }),
    'time_signature': ClassLabel(shape=(), dtype=tf.int64, num_classes=5),
    'type': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
})

• Examples (tfds.as_dataframe):

groove/2bar-midionly

• Config description: Groove dataset without audio, split into 2-bar chunks.

• Download size: 3.11 MiB

• Dataset size: 19.59 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'test'	2,204
'train'	18,163
'validation'	2,252

• Features:

FeaturesDict({
    'bpm': tf.int32,
    'drummer': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),
    'id': tf.string,
    'midi': tf.string,
    'style': FeaturesDict({
        'primary': ClassLabel(shape=(), dtype=tf.int64, num_classes=18),
        'secondary': tf.string,
    }),
    'time_signature': ClassLabel(shape=(), dtype=tf.int64, num_classes=5),
    'type': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
})

• Examples (tfds.as_dataframe):

groove/2bar-16000hz

• Config description: Groove dataset with audio, split into 2-bar chunks.

• Download size: 4.76 GiB

• Dataset size: 4.61 GiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'test'	1,873
'train'	14,390
'validation'	2,034

• Features:

FeaturesDict({
    'audio': Audio(shape=(None,), dtype=tf.float32),
    'bpm': tf.int32,
    'drummer': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),
    'id': tf.string,
    'midi': tf.string,
    'style': FeaturesDict({
        'primary': ClassLabel(shape=(), dtype=tf.int64, num_classes=18),
        'secondary': tf.string,
    }),
    'time_signature': ClassLabel(shape=(), dtype=tf.int64, num_classes=5),
    'type': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
})

• Examples (tfds.as_dataframe):

groove/4bar-midionly

• Config description: Groove dataset without audio, split into 4-bar chunks.

• Download size: 3.11 MiB

• Dataset size: 27.32 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'test'	2,033
'train'	17,261
'validation'	2,121

• Features:

FeaturesDict({
    'bpm': tf.int32,
    'drummer': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),
    'id': tf.string,
    'midi': tf.string,
    'style': FeaturesDict({
        'primary': ClassLabel(shape=(), dtype=tf.int64, num_classes=18),
        'secondary': tf.string,
    }),
    'time_signature': ClassLabel(shape=(), dtype=tf.int64, num_classes=5),
    'type': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
})

• Examples (tfds.as_dataframe):