Update README.md

README.md

@@ -236,4 +236,192 @@ tags:
 - 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)
+
+### 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 9,900 non-silent samples. The 36 silent samples at extreme registers are unvoiceable with our soundfont. With a more complete soundfont, all 9,936 configurations are realizable to audio.
+
+## 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            |