Retrieve results

After the processing is complete, you can retrieve the results.

🌐 cURL Example

curl --request GET \ 
     --url https://api.behavioralsignals.com/v5/detection/clients/<your-cid>/processes/<process-id>/results \
     --header 'X-Auth-Token: <your-api-token>' \
     --header 'accept: application/json'

🐍 Python SDK Example

Retrieve the results of a given process with:

from behavioralsignals import Client

client = Client(YOUR_CID, YOUR_API_KEY)

# submit for processing
response = client.deepfakes.upload_audio(file_path="audio.wav")

# after a while
output = client.deepfakes.get_result(pid=<pid-of-process>)

The output is an object of class ResultResponse, defined here

Response Example

{
  "pid": <your-pid>,
  "cid": <your-cid>,
  "code": 2,
  "message": "Processing Complete",
  "results": [
    {
      "id": "0",
      "startTime": "0.031",
      "endTime": "13.151",
      "task": "asr",
      "prediction": [
        {
          "label": " This is deep fake example of what is possible with powerful computer and editing. It took around seventy two hours to create this example from scratch using extremely powerful GPU. It could improve with more computing time, but ninety percent people cannot tell the difference",
          "posterior": null,
          "dominantInSegments": []
        }
      ],
      "finalLabel": " This is deep fake example of what is possible with powerful computer and editing. It took around seventy two hours to create this example from scratch using extremely powerful GPU. It could improve with more computing time, but ninety percent people cannot tell the difference",
      "level": "utterance",
      "embedding": null
    },
    {
      "id": "0",
      "startTime": "0.031",
      "endTime": "13.151",
      "task": "diarization",
      "prediction": [
        {
          "label": "SPEAKER_00",
          "posterior": null,
          "dominantInSegments": []
        }
      ],
      "finalLabel": "SPEAKER_00",
      "level": "utterance",
      "embedding": null
    },
    {
      "id": "0",
      "startTime": "0.031",
      "endTime": "13.151",
      "task": "language",
      "prediction": [
        {
          "label": "en",
          "posterior": "0.9990234375",
          "dominantInSegments": []
        },
        {
          "label": "es",
          "posterior": "7.838010787963867e-05",
          "dominantInSegments": []
        },
        {
          "label": "pt",
          "posterior": "7.361173629760742e-05",
          "dominantInSegments": []
        }
      ],
      "finalLabel": "en",
      "level": "utterance",
      "embedding": null
    },
    {
      "id": "0",
      "startTime": "0.031",
      "endTime": "13.151",
      "task": "deepfake",
      "prediction": [
        {
          "label": "spoofed",
          "posterior": "0.9997",
          "dominantInSegments": []
        },
        {
          "label": "bonafide",
          "posterior": "0.0003",
          "dominantInSegments": []
        }
      ],
      "finalLabel": "spoofed",
      "level": "utterance",
      "embedding": null
    }
  ]
}

The results object is a list of predictions - corresponding to each utterance detected in the audio file. The startTime and endTime mark the start and end of the utterance.
Each result item contains a set of tasks: asr, diarization, language and deepfake
The "deepfake" task consists of two classes: "bonafide" (authentic) and "spoofed" (deepfake).
The posterior denotes the probability of the corresponding class.
The finalLabel field always displays the dominant class.