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---
dataset_info:
features:
- name: filename
dtype: string
- name: 'true'
sequence: float32
length: 20
- name: mask
sequence: int32
length: 20
- name: mp3_bytes
dtype: binary
splits:
- name: train
num_bytes: 1790991884
num_examples: 14915
- name: test
num_bytes: 611455142
num_examples: 5085
download_size: 0
dataset_size: 2402447026
configs:
- config_name: default
data_files:
- split: train
path: data/shard_train_*
- split: test
path: data/shard_test_*
---
# CPJKU/openmic
The dataset is made available by Spotify AB under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. The full terms of this license are included alongside this dataset.
This dataset is preprocessed and compressed to 32khz mp3 files. The bytes of the mp3 files are embedded.
The mp3 bytes can be decoded quickly using for [example](https://github.com/kkoutini/PaSST/blob/4519e4605989b8c2e62dccb5b928af9bf7bf8602/audioset/dataset.py#L55) or [minimp3](https://github.com/f0k/minimp3py).
Take a look at the original dataset for more information.
The original dataset contains the following:
10 second snippets of audio, in a directory format like 'audio/{0:3}/{0}.ogg'.format(sample_key)
VGGish features as JSON objects, in a directory format like 'vggish/{0:3}/{0}.json'.format(sample_key)
MD5 checksums for each OGG and JSON file
Anonymized individual responses, in 'openmic-2018-individual-responses.csv'
Aggregated labels, in 'openmic-2018-aggregated-labels.csv'
Track metadata, with licenses for each audio recording, in 'openmic-2018-metadata.csv'
A Python-friendly NPZ file of features and labels, 'openmic-2018.npz'
Sample partitions for train and test, in 'partitions/*.txt'
## Homepage
https://zenodo.org/records/1432913
## Citation
```
Humphrey, Eric J., Durand, Simon, and McFee, Brian. "OpenMIC-2018: An Open Dataset for Multiple Instrument Recognition." in Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), 2018.
```
## License
CC BY 4.0
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