license: mit
dataset_info:
- config_name: chords
features:
- name: audio
dtype:
audio:
sampling_rate: 44100
mono: false
- name: root_note_name
dtype: string
- name: chord_type
dtype: string
- name: inversion
dtype: int64
- name: root_note_is_accidental
dtype: bool
- name: root_note_pitch_class
dtype: int64
- name: midi_program_num
dtype: int64
- name: midi_program_name
dtype: string
- name: midi_category
dtype: string
splits:
- name: train
num_bytes: 18697466628.48
num_examples: 13248
download_size: 18637787206
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- config_name: intervals
features:
- name: audio
dtype:
audio:
sampling_rate: 44100
mono: false
- name: root_note_name
dtype: string
- name: root_note_pitch_class
dtype: int64
- name: interval
dtype: int64
- name: play_style
dtype: int64
- name: play_style_name
dtype: string
- name: midi_note_val
dtype: int64
- name: midi_program_num
dtype: int64
- name: midi_program_name
dtype: string
- name: midi_category
dtype: string
splits:
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num_examples: 39744
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- config_name: notes
features:
- name: audio
dtype:
audio:
sampling_rate: 44100
mono: false
- name: root_note_name
dtype: string
- name: root_note_pitch_class
dtype: int64
- name: octave
dtype: int64
- name: root_note_is_accidental
dtype: bool
- name: register
dtype: int64
- name: midi_note_val
dtype: int64
- name: midi_program_num
dtype: int64
- name: midi_program_name
dtype: string
- name: midi_category
dtype: string
splits:
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num_bytes: 14023184428.832
num_examples: 9936
download_size: 13804952340
dataset_size: 14023184428.832
- config_name: scales
features:
- name: audio
dtype:
audio:
sampling_rate: 44100
mono: false
- name: root_note_name
dtype: string
- name: mode
dtype: string
- name: play_style
dtype: int64
- name: play_style_name
dtype: string
- name: midi_program_num
dtype: int64
- name: midi_program_name
dtype: string
- name: midi_category
dtype: string
splits:
- name: train
num_bytes: 21813743576.416
num_examples: 15456
download_size: 21806379646
dataset_size: 21813743576.416
- config_name: simple_progressions
features:
- name: audio
dtype:
audio:
sampling_rate: 44100
mono: false
- name: key_note_name
dtype: string
- name: key_note_pitch_class
dtype: int64
- name: chord_progression
dtype: string
- name: midi_program_num
dtype: int64
- name: midi_program_name
dtype: string
- name: midi_category
dtype: string
splits:
- name: train
num_bytes: 29604485544.56
num_examples: 20976
download_size: 29509153369
dataset_size: 29604485544.56
- config_name: tempos
features:
- name: audio
dtype:
audio:
sampling_rate: 44100
mono: false
- name: bpm
dtype: int64
- name: click_config_name
dtype: string
- name: midi_program_num
dtype: int64
- name: offset_time
dtype: float64
splits:
- name: train
num_bytes: 2840527084
num_examples: 4025
download_size: 1323717012
dataset_size: 2840527084
- config_name: time_signatures
features:
- name: audio
dtype:
audio:
sampling_rate: 44100
mono: false
- name: time_signature
dtype: string
- name: time_signature_beats
dtype: int64
- name: time_signature_subdivision
dtype: int64
- name: is_compound
dtype: int64
- name: bpm
dtype: int64
- name: click_config_name
dtype: string
- name: midi_program_num
dtype: int64
- name: offset_time
dtype: float64
- name: reverb_level
dtype: int64
splits:
- name: train
num_bytes: 846915090
num_examples: 1200
download_size: 692431621
dataset_size: 846915090
configs:
- config_name: chords
data_files:
- split: train
path: chords/train-*
- config_name: intervals
data_files:
- split: train
path: intervals/train-*
- config_name: notes
data_files:
- split: train
path: notes/train-*
- config_name: scales
data_files:
- split: train
path: scales/train-*
- config_name: simple_progressions
data_files:
- split: train
path: simple_progressions/train-*
- config_name: tempos
data_files:
- split: train
path: tempos/train-*
- config_name: time_signatures
data_files:
- split: train
path: time_signatures/train-*
task_categories:
- audio-classification
- feature-extraction
language:
- en
tags:
- audio
- music
- music information retrieval
size_categories:
- 100K<n<1M
Dataset Card for SynTheory
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: Do Music Generation Models Encode Music Theory?
- Repository: SynTheory
- Paper: Do Music Generation Models Encode Music Theory?
Dataset Summary
SynTheory is a synthetic dataset of music theory concepts, specifically rhythmic (tempos and time signatures) and tonal (notes, intervals, scales, chords, and chord progressions).
Each of these 7 concepts has its own config.
tempos consist of 161 total integer tempos (bpm) ranging from 50 BPM to 210 BPM (inclusive), 5 percussive instrument types (click_config_name), and 5 random start time offsets (offset_time).
time_signatures consist of 8 time signatures (time_signature), 5 percussive instrument types (click_config_name), 10 random start time offsets (offset_time), and 3 reverb levels (reverb_level). The 8 time signatures are 2/2, 2/4, 3/4, 3/8, 4/4, 6/8, 9/8, and 12/8.
notes consist of 12 pitch classes (root_note_name), 9 octaves (octave), and 92 instrument types (midi_program_name). The 12 pitch classes are C, C#, D, D#, E, F, F#, G, G#, A, A# and B.
intervals consist of 12 interval sizes (interval), 12 root notes (root_note_name), 92 instrument types (midi_program_name), and 3 play styles (play_style_name). The 12 intervals are minor 2nd, Major 2nd, minor 3rd, Major 3rd, Perfect 4th, Tritone, Perfect 5th, minor 6th, Major 6th, minor 7th, Major 7th, and Perfect octave.
scales consist of 7 modes (mode), 12 root notes (root_note_name), 92 instrument types (midi_program_name), and 2 play styles (play_style_name). The 7 modes are Ionian, Dorian, Phrygian, Lydian, Mixolydian, Aeolian, and Locrian.
chords consist of 4 chord quality (chord_type), 3 inversions (inversion), 12 root notes (root_note_name), and 92 instrument types (midi_program_name). The 4 chord quality types are major, minor, augmented, and diminished. The 3 inversions are root position, first inversion, and second inversion.
simple_progressions consist of 19 chord progressions (chord_progression), 12 root notes (key_note_name), and 92 instrument types (midi_program_name). The 19 chord progressions consist of 10 chord progressions in major mode and 9 in natural minor mode. The major mode chord progressions are (I–IV–V–I), (I–IV–vi–V), (I–V–vi–IV), (I–vi–IV–V), (ii–V–I–Vi), (IV–I–V–Vi), (IV–V–iii–Vi), (V–IV–I–V), (V–vi–IV–I), and (vi–IV–I–V). The natural minor mode chord progressions are (i–ii◦–v–i), (i–III–iv–i), (i–iv–v–i), (i–VI–III–VII), (i–VI–VII–i), (i–VI–VII–III), (i–VII–VI–IV), (iv–VII–i–i), and (VII–vi–VII–i).
Supported Tasks and Leaderboards
audio-classification: This can be used towards music theory classification tasks.feature-extraction: Our samples can be fed into pretrained audio codecs to extract representations from the model, which can be further used for downstream MIR tasks.
How to use
The datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function.
For example, to download the notes config, simply specify the corresponding language config name (i.e., "notes"):
from datasets import load_dataset
notes = load_dataset("meganwei/syntheory", "notes")
Using the datasets library, you can also stream the dataset on-the-fly by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
from datasets import load_dataset
notes = load_dataset("meganwei/syntheory", "notes", streaming=True)
print(next(iter(notes)))
Bonus: create a PyTorch dataloader directly with your own datasets (local/streamed).
Local:
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
from torch.utils.data import DataLoader
notes = load_dataset("meganwei/syntheory", "notes")
batch_sampler = BatchSampler(RandomSampler(notes), batch_size=32, drop_last=False)
dataloader = DataLoader(notes, batch_sampler=batch_sampler)
Streaming:
from datasets import load_dataset
from torch.utils.data import DataLoader
notes = load_dataset("meganwei/syntheory", "notes", streaming=True)
dataloader = DataLoader(notes, batch_size=32)
To find out more about loading and preparing audio datasets, head over to hf.co/blog/audio-datasets.
Example scripts
[More Information Needed]
Dataset Structure
Data Fields
[More Information Needed]
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
For the notes music theory concept, there are 9,936 distinct note configurations. However, our dataset contains 9,848 non-silent samples. The 88 silent samples at extreme registers are unvoiceable with our soundfont. With a more complete soundfont, all 9,936 configurations are realizable to audio.
The silent samples are the following audio files: 0_0_C_10_Music_Box.wav, 0_0_C_56_Trumpet.wav, 0_0_C_68_Oboe.wav, 1_0_C#_10_Music_Box.wav, 1_0_C#_56_Trumpet.wav, 1_0_C#_68_Oboe.wav, 2_0_D_10_Music_Box.wav, 2_0_D_56_Trumpet.wav, 2_0_D_68_Oboe.wav, 3_0_D#_10_Music_Box.wav, 3_0_D#_56_Trumpet.wav, 3_0_D#_68_Oboe.wav, 4_0_E_10_Music_Box.wav, 4_0_E_56_Trumpet.wav, 4_0_E_68_Oboe.wav, 5_0_F_10_Music_Box.wav, 5_0_F_56_Trumpet.wav, 5_0_F_68_Oboe.wav, 6_0_F#_10_Music_Box.wav, 6_0_F#_56_Trumpet.wav, 6_0_F#_68_Oboe.wav, 7_0_G_10_Music_Box.wav, 7_0_G_56_Trumpet.wav, 7_0_G_68_Oboe.wav, 8_0_G#_10_Music_Box.wav, 8_0_G#_56_Trumpet.wav, 8_0_G#_68_Oboe.wav, 9_0_A_10_Music_Box.wav, 9_0_A_56_Trumpet.wav, 9_0_A_68_Oboe.wav, 10_0_A#_10_Music_Box.wav, 10_0_A#_56_Trumpet.wav, 10_0_A#_68_Oboe.wav, 11_0_B_10_Music_Box.wav, 11_0_B_56_Trumpet.wav, 11_0_B_68_Oboe.wav, 12_0_C_68_Oboe.wav, 13_0_C#_68_Oboe.wav, 14_0_D_68_Oboe.wav, 15_0_D#_68_Oboe.wav, 16_0_E_68_Oboe.wav, 17_0_F_68_Oboe.wav, 18_0_F#_68_Oboe.wav, 19_0_G_68_Oboe.wav, 20_0_G#_68_Oboe.wav, 21_0_A_68_Oboe.wav, 22_0_A#_68_Oboe.wav, 23_0_B_68_Oboe.wav, 24_0_C_68_Oboe.wav, 25_0_C#_68_Oboe.wav, 26_0_D_68_Oboe.wav, 27_0_D#_68_Oboe.wav, 28_0_E_68_Oboe.wav, 29_0_F_68_Oboe.wav, 30_0_F#_68_Oboe.wav, 31_0_G_68_Oboe.wav, 32_0_G#_68_Oboe.wav, 33_0_A_68_Oboe.wav, 34_0_A#_68_Oboe.wav, 35_0_B_68_Oboe.wav, 80_2_G#_67_Baritone_Sax.wav, 81_2_A_67_Baritone_Sax.wav, 82_2_A#_67_Baritone_Sax.wav, 83_2_B_67_Baritone_Sax.wav, 84_2_C_67_Baritone_Sax.wav, 85_2_C#_67_Baritone_Sax.wav, 86_2_D_67_Baritone_Sax.wav, 87_2_D#_67_Baritone_Sax.wav, 88_2_E_67_Baritone_Sax.wav, 89_2_F_67_Baritone_Sax.wav, 90_2_F#_67_Baritone_Sax.wav, 91_2_G_67_Baritone_Sax.wav, 92_2_G#_67_Baritone_Sax.wav, 93_2_A_67_Baritone_Sax.wav, 94_2_A#_67_Baritone_Sax.wav, 95_2_B_67_Baritone_Sax.wav, 96_2_C_67_Baritone_Sax.wav, 97_2_C#_67_Baritone_Sax.wav, 98_2_D_67_Baritone_Sax.wav, 99_2_D#_67_Baritone_Sax.wav, 100_2_E_67_Baritone_Sax.wav, 101_2_F_67_Baritone_Sax.wav, 102_2_F#_67_Baritone_Sax.wav, 103_2_G_67_Baritone_Sax.wav, 104_2_G#_67_Baritone_Sax.wav, 105_2_A_67_Baritone_Sax.wav, 106_2_A#_67_Baritone_Sax.wav, and 107_2_B_67_Baritone_Sax.wav.
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
@inproceedings{Wei2024-music,
title={Do Music Generation Models Encode Music Theory?},
author={Wei, Megan and Freeman, Michael and Donahue, Chris and Sun, Chen},
booktitle={International Society for Music Information Retrieval},
year={2024}
}
Data Statistics
| Concept | Number of Samples |
|---|---|
| Tempo | 4,025 |
| Time Signatures | 1,200 |
| Notes | 9,936 |
| Intervals | 39,744 |
| Scales | 15,456 |
| Chords | 13,248 |
| Chord Progressions | 20,976 |