Endpointing

Deepgram’s Endpointing feature monitors incoming streaming audio and detects sufficiently long pauses that are likely to represent an endpoint in speech. When Deepgram detects an endpoint, it assumes that no additional data will improve its prediction, so it immediately finalizes its results for the processed time range and returns the transcript with a speech_final parameter set to true.

Use Cases

Some examples of use cases for Endpointing include:

• Returning finalized transcripts as soon as possible when a break in speech is detected.

Enable Feature

To enable endpointing, when you call Deepgram’s API, add an endpointing parameter set to true in the query string:

endpointing=true

For an example of audio streaming, see Getting Started with Streaming Audio.

Results

When enabled, the transcript for each received streaming response shows a key called speech_final.

{
  "channel_index":[
    0,
    1
  ],
  "duration":1.039875,
  "start":0.0,
  "is_final":false,
  "speech_final":false,
  "channel":{
    "alternatives":[
      {
        "transcript":"another big",
        "confidence":0.9600255,
        "words":[
          {
            "word":"another",
            "start":0.2971154,
            "end":0.7971154,
            "confidence":0.9588303
          },
          {
            "word":"big",
            "start":0.85173076,
            "end":1.039875,
            "confidence":0.9600255
          }
        ]
      }
    ]
  }
}
...

When speech_final is set to true, Deepgram has detected an endpoint and immediately finalized its results for the processed time range.