Speaker Diarization
Assign speaker labels to each utterance and determine speaker count in conversations with our advanced Speech AI models, providing industry-leading speech-to-text accuracy.
Get started with less than 10 lines of code
Simply enable Speaker Diarization in our API, and receive a detailed transcript with a list of utterances.
Improve transcription quality and readability
Reduce speaker misattribution and transcription errors, enabling cleaner data for NLP tasks and enhancing user experience in speech-to-text applications.
Build confidently with accurate, multilingual speaker diarization
Maximize speaker count accuracy
Enhance conversation analysis and speaker-dependent AI models with industry-leading speaker count accuracy. Our models achieve a 2.9% error rate, outperforming competitors in identifying the number of speakers.
See benchmarks and latest improvements
Broaden your application's reach
Support speaker diarization in 16 languages, enabling multilingual audio analysis and expanding your product's global market potential.
View supported languages
Make every voice count
Improve the readability of your transcriptions
Unlock call center insights
Create a better search experience
Assess communication patterns
Optimize short-form content generation
Enhance automated dubbing precision
Implement intelligent camera focus
Determine talk time for sales teams
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import assemblyai as aai
transcriber = aai.Transcriber()
transcript = transcriber.transcribe(URL, config)
print(transcript)
{
"id": "6rlr37h8f4-e310-4e23-bbf3-ea5f347dc684",
"language_code": "en_us",
"status": "completed",
"text": "Runner's knee is a condition characterized by pain behind or around the kneecap...",
"confidence": 0.98122,
"audio_duration": 3200,
"words": [
{ "text": "Runner's", "start": 0, "end": 550, "speaker": "A", "confidence": 0.98113 },
{ "text": "knee", "start": 580, "end": 1130, "speaker": "A", "confidence": 0.95417 }
]
}