127 lines
3.2 KiB
JSON
127 lines
3.2 KiB
JSON
{
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"name": "MSD MusiCNN",
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"type": "auto-tagging",
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"link": "https://essentia.upf.edu/models/feature-extractors/musicnn/msd-musicnn-1.pb",
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"version": "1",
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"description": "prediction of the top-50 tags in the dataset",
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"author": "Pablo Alonso",
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"email": "pablo.alonso@upf.edu",
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"release_date": "2020-03-31",
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"framework": "tensorflow",
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"framework_version": "1.15.0",
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"classes": [
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"rock",
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"pop",
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"alternative",
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"indie",
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"electronic",
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"female vocalists",
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"dance",
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"00s",
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"alternative rock",
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"jazz",
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"beautiful",
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"metal",
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"chillout",
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"male vocalists",
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"classic rock",
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"soul",
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"indie rock",
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"Mellow",
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"electronica",
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"80s",
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"folk",
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"90s",
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"chill",
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"instrumental",
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"punk",
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"oldies",
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"blues",
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"hard rock",
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"ambient",
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"acoustic",
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"experimental",
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"female vocalist",
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"guitar",
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"Hip-Hop",
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"70s",
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"party",
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"country",
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"easy listening",
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"sexy",
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"catchy",
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"funk",
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"electro",
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"heavy metal",
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"Progressive rock",
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"60s",
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"rnb",
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"indie pop",
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"sad",
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"House",
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"happy"
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],
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"model_types": [
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"frozen_model"
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],
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"dataset": {
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"name": "The Millon Song Dataset",
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"citation": "http://millionsongdataset.com/",
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"size": "200k up to two minutes audio previews",
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"metrics": {
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"ROC-AUC": 0.88,
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"PR-AUC": 0.29
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}
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},
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"schema": {
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"inputs": [
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{
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"name": "model/Placeholder",
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"type": "float",
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"shape": [
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187,
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96
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]
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}
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],
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"outputs": [
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{
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"name": "model/Sigmoid",
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"type": "float",
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"shape": [
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1,
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50
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],
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"op": "Sigmoid",
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"output_purpose": "predictions"
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},
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{
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"name": "model/dense_1/BiasAdd",
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"type": "float",
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"shape": [
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1,
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50
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],
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"op": "fully connected",
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"description": "logits",
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"output_purpose": ""
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},
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{
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"name": "model/dense/BiasAdd",
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"type": "float",
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"shape": [
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1,
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200
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],
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"op": "fully connected",
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"output_purpose": "embeddings"
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}
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]
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},
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"citation": "@inproceedings{alonso2020tensorflow,\n title={Tensorflow Audio Models in Essentia},\n author={Alonso-Jim{\\'e}nez, Pablo and Bogdanov, Dmitry and Pons, Jordi and Serra, Xavier},\n booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},\n year={2020}\n}",
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"inference": {
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"sample_rate": 16000,
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"algorithm": "TensorflowPredictMusiCNN"
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}
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}
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