README.md

---
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:
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    num_bytes: 18697466628.48
    num_examples: 13248
  download_size: 18637787206
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- config_name: intervals
  features:
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    dtype:
      audio:
        sampling_rate: 44100
        mono: false
  - name: root_note_name
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  - name: root_note_pitch_class
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  - 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
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  - name: midi_program_name
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  - name: midi_category
    dtype: string
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  features:
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    dtype:
      audio:
        sampling_rate: 44100
        mono: false
  - name: root_note_name
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  - 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
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  - name: midi_program_name
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  - name: midi_category
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- config_name: scales
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        mono: false
  - name: root_note_name
    dtype: string
  - name: mode
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  - name: play_style
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  - name: play_style_name
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  - name: midi_program_num
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  - name: midi_program_name
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  - name: midi_category
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- 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
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  - name: chord_progression
    dtype: string
  - name: midi_program_num
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  - name: midi_program_name
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  - name: midi_category
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  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:
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    num_examples: 4025
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- 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:
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    num_examples: 1200
  download_size: 692431621
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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-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
  - [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [Do Music Generation Models Encode Music Theory?](https://brown-palm.github.io/music-theory/)
- **Repository:** [SynTheory](https://github.com/brown-palm/syntheory)
- **Paper:** [Do Music Generation Models Encode Music Theory?](https://arxiv.org/abs/2410.00872)

### 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"):
```python
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.
```python
from datasets import load_dataset

notes = load_dataset("meganwei/syntheory", "notes", streaming=True)

print(next(iter(notes)))
```

*Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).

Local:

```python
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:

```python
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](https://huggingface.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
```bibtext
@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            |