68 lines
2.0 KiB
JSON
68 lines
2.0 KiB
JSON
{
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"name": "genre electronic",
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"type": "multi-class classifier",
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"link": "https://essentia.upf.edu/models/classifiers/genre_electronic/genre_electronic-musicnn-msd-2.pb",
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"version": "1",
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"description": "classification of electronic music by subgenres",
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"author": "Pablo Alonso",
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"email": "pablo.alonso@upf.edu",
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"release_date": "2020-07-07",
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"framework": "tensorflow",
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"framework_version": "1.15.0",
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"classes": ["ambient", "drum and bass", "house", "techno", "trance"],
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"model_types": ["frozen_model"],
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"dataset": {
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"name": "In-house MTG collection",
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"size": "250 track excerpts, 50 per genre",
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"metrics": {
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"5-fold_cross_validation_normalized_accuracy": 0.95
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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": [187, 96]
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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": [1, 5],
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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_2/BiasAdd",
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"type": "float",
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"shape": [1, 5],
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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_1/BiasAdd",
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"type": "float",
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"shape": [1, 100],
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"op": "fully connected",
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"description": "penultimate layer",
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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": [1, 200],
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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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