size_categories:10K<n<100K format:parquet modality:text modality:timeseries library:datasets library:pandas library:mlcroissant library:polars region:us
---
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
