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README.md
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---
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dataset_info:
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features:
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- name: filename
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dtype: string
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- name: 'true'
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sequence: float32
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length: 20
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- name: mask
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sequence: int32
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length: 20
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- name: mp3_bytes
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dtype: binary
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splits:
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- name: train
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num_bytes: 1790991884
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num_examples: 14915
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- name: test
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num_bytes: 611455142
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num_examples: 5085
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download_size: 0
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dataset_size: 2402447026
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/shard_train_*
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- split: test
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path: data/shard_test_*
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---
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# CPJKU/openmic
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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.
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This dataset is preprocessed and compressed to 32khz mp3 files. The bytes of the mp3 files are embedded.
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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).
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Take a look at the original dataset for more information.
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The original dataset contains the following:
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10 second snippets of audio, in a directory format like 'audio/{0:3}/{0}.ogg'.format(sample_key)
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VGGish features as JSON objects, in a directory format like 'vggish/{0:3}/{0}.json'.format(sample_key)
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MD5 checksums for each OGG and JSON file
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Anonymized individual responses, in 'openmic-2018-individual-responses.csv'
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Aggregated labels, in 'openmic-2018-aggregated-labels.csv'
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Track metadata, with licenses for each audio recording, in 'openmic-2018-metadata.csv'
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A Python-friendly NPZ file of features and labels, 'openmic-2018.npz'
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Sample partitions for train and test, in 'partitions/*.txt'
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## Homepage
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https://zenodo.org/records/1432913
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## Citation
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```
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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.
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```
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## License
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CC BY 4.0
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