third_party/tflite_support/src/tensorflow_lite_support/metadata/python/tests/testdata/audio_classifier/daredevil_sound_recognizer_320ms.json

{
  "name": "AudioClassifier",
  "description": "Identify the most prominent type in the audio clip from a known set of categories.",
  "subgraph_metadata": [
    {
      "input_tensor_metadata": [
        {
          "name": "audio_clip",
          "description": "Input audio clip to be classified.",
          "content": {
            "content_properties_type": "AudioProperties",
            "content_properties": {
              "sample_rate": 2,
              "channels": 1
            }
          },
          "stats": {
          }
        }
      ],
      "output_tensor_metadata": [
        {
          "name": "probability",
          "description": "Scores of the labels respectively.",
          "content": {
            "content_properties_type": "FeatureProperties",
            "content_properties": {
            }
          },
          "process_units": [
            {
              "options_type": "ScoreCalibrationOptions",
              "options": {
                "score_transformation": "LOG",
                "default_score": 0.2
              }
            }
          ],
          "stats": {
            "max": [
              1.0
            ],
            "min": [
              0.0
            ]
          },
          "associated_files": [
            {
              "name": "labelmap.txt",
              "description": "Labels for categories that the model can recognize.",
              "type": "TENSOR_AXIS_LABELS"
            },
            {
              "name": "score_calibration.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"
            }
          ]
        }
      ]
    }
  ]
}