Perfect Diarization
Supported PlatformsPerfect diarization is currently supported for Zoom, Google Meet, and Microsoft Teams bots.
Perfect diarization is a feature designed to address the problem of inaccurate speaker attribution in meeting transcripts. Meeting platforms can sometimes attribute words to the wrong speaker, especially when multiple people are talking at once. This feature ensures that each speaker's words are accurately identified, even when participants are talking over each other.
Supported Meeting Platforms
| Platform | Supported |
|---|---|
| Zoom | ✅ |
| Microsoft Teams | ✅ |
| Google Meet | ✅ |
| Webex | ❌ |
| Slack Huddles (Beta) | ❌ |
| Go-To Meeting (Beta) | ❌ |
How It Works
Perfect diarization transcribes separate audio streams for each participant instead of using the combined audio stream for the entire meeting, significantly improving the accuracy of speaker attribution. This feature is compatible with Recall.ai transcription, as well as all AI transcription providers supported by Recall.ai. It can be used for real-time and async transcription.
Usage
Real-time transcription
To enable perfect diarization, add the use_separate_streams_when_available parameter to transcription_options when calling Create Bot:
{
...,
"transcription_options": {
"provider": "Your AI Transcription Provider",
"use_separate_streams_when_available": true
}
}Async transcription
To enable perfect diarization, add the use_separate_streams_when_available parameter to your request body when calling Analyze Bot Media:
{
...,
"use_separate_streams_when_available": true
}Cost Considerations
For real-time transcription with separate streams, we typically see ~1.8x the transcription credit usage compared to the normal transcription.
For async transcription with separate streams, we trim out sections of audio without speech to optimize your transcription costs. The result is that async transcription can use anywhere from 0.6x to 1.2x the transcription credit usage, with the average cost difference being 1x.
Updated about 21 hours ago