{
"name": "AudioClassifier",
"subgraph_metadata": [
{
"input_tensor_metadata": [
{
"name": "audio_clip",
"content": {
"content_properties_type": "AudioProperties",
"content_properties": {
"sample_rate": 2,
"channels": 1
}
},
"stats": {
}
}
],
"output_tensor_metadata": [
{
"name": "head2",
"content": {
"content_properties_type": "FeatureProperties",
"content_properties": {
}
},
"process_units": [
{
"options_type": "ScoreCalibrationOptions",
"options": {
"score_transformation": "LOG",
"default_score": 0.2
}
}
],
"stats": {
},
"associated_files": [
{
"name": "labels_en_2.txt",
"description": "Labels for categories that the model can recognize.",
"type": "TENSOR_AXIS_LABELS"
},
{
"name": "labels_cn_2.txt",
"description": "Labels for categories that the model can recognize.",
"type": "TENSOR_AXIS_LABELS"
},
{
"name": "score_cali_2.txt",
"description": "Contains sigmoid-based score calibration parameters. The main purposes of score calibration is to make scores across classes comparable, so that a common threshold can be used for all output classes.",
"type": "TENSOR_AXIS_SCORE_CALIBRATION"
}
]
},
{
"name": "head1",
"content": {
"content_properties_type": "FeatureProperties",
"content_properties": {
}
},
"process_units": [
{
"options_type": "ScoreCalibrationOptions",
"options": {
"score_transformation": "LOG",
"default_score": 0.2
}
}
],
"stats": {
},
"associated_files": [
{
"name": "labels_en_1.txt",
"description": "Labels for categories that the model can recognize.",
"type": "TENSOR_AXIS_LABELS"
},
{
"name": "labels_cn_1.txt",
"description": "Labels for categories that the model can recognize.",
"type": "TENSOR_AXIS_LABELS"
},
{
"name": "score_cali_1.txt",
"description": "Contains sigmoid-based score calibration parameters. The main purposes of score calibration is to make scores across classes comparable, so that a common threshold can be used for all output classes.",
"type": "TENSOR_AXIS_SCORE_CALIBRATION"
}
]
}
]
}
]
}