Duplicate from CPJKU/openmic

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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
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+# Image files - uncompressed
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README.md

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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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