Topic Detection

topics boolean Default: false

Pre-recorded Streaming:Nova English (all available regions)

Deepgram’s Topic Detection feature identifies key topics within the transcript, returning a list of text segments and the topics found within each segment.

The list of topics that can be identified is not a fixed list; this TSLM powered feature is able to generate topics based on the context of the language content in the transcript. You may also choose to use the optional custom-topic parameter to provide a custom topic you want detected if present within your audio.

To enable Topic Detection, use the following parameter in the query string when you call Deepgram’s /listen endpoint:

topics=true

To transcribe audio from a file on your computer, run the following curl command in a terminal or your favorite API client.

curl \
  --request POST \
  --header 'Authorization: Token YOUR_DEEPGRAM_API_KEY' \
  --header 'Content-Type: audio/wav' \
  --data-binary @youraudio.wav \
  --url 'https://api.deepgram.com/v1/listen?topics=true'

When the file is finished processing, you’ll receive a JSON response that has the following basic structure:

{
  "metadata": {...},
  "results": {
    "channels": [
      {
        "alternatives": [...]
      }
    ],
    "topics": {
      "segments": [
        {
          "text": "Can I upgrade my phone?",
          "start_word": 13,
          "end_word": 17,
          "topics": [
            { "topic": "Phone upgrade", "confidence_score": 0.9661531 }
          ]
        }
      ]
    }
  }
}

The response object values for topics are:

• segments: The list of segments of text identified by the model as containing notable topics.
• topic: The name of the topic detected by the model.
• confidence_score: a floating point from 0 to 1 representing the models confidence in this prediction.

If you request Topic Detection with an unsupported language by specifying a language code such as topics=true&language=es or topics=true&detect_language=true where the detected language is unsupported, you will get the warning message below.

"warnings": [
  {
    "parameter": "topics",
    "type": "unsupported_language",
    "message": "Topics is only supported for English."
  }
]

Example Warning

Here is an example of the JSON structure of a request with warning object.

{
 "metadata": {
...
       },
     "warnings": [
       {
         "parameter": "topic",
         "type": "unsupported_language",
         "message": "Topics is only supported for English."
       }
     ]
   },
 "results": {
       "channels": [
            {
                "alternatives": [...]
            }
        ], 

    }
}

Some examples of uses for Topic Detection include:

• Customers who want to help their Quality Assurance team analyze conversations to identify trends and patterns based on discussed topics.
• Customers who need to extract meaningful and actionable insights from conversations and audio data based on discussed topics.
• Customers who want to enhance search capabilities by tagging conversations based on identified topics.