Discover our models that enable developers and product teams to easily access state-of-the-art speech AI everywhere it matters
Identify and differentiate speakers in the blink of an eye.
Our AI models reach the highest level of precision in the industry.
Deploy our models everywhere, on premise or through API.
STATE-OF-THE-ART
Explore our cutting edge models
Open
Open source
Discover the most famous opensource diarisation model
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Turbo
Optimized
Deploy a super fast version of the opensource model
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Precision
Optimized
Unleash the true potential of your audio with the most accurate AI model
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Enhanced diarization
World-class diarization is the foundation of our work, and we are always looking for ways to maximize the benefits of our models to better match our particular needs. Enhance diariazation with our features
Our world-class speaker diarization technology segments multi-speaker conversations into speech turns and assigns a unique tag per speaker. It really shines with noisy, dynamic, and overlapping speech.
Combined with our world-class speaker diarization technology and state-of-the-art voiceprints, speaker identification tracks recurring speakers across multiple podcast episodes or team meetings.
Overlapping speech happens all the time in spontaneous conversations (fast turn-taking, back-channeling, or interruption). Not only do we detect it, but we also attribute it to the right speakers.
Our world-class speaker diarization technology provides extremely accurate speaker change timestamps. Prevents cutting audio in the middle of a word, and improves speaker attribution.
Our world-class speaker diarization technology builds on top of state-of-the-art voice activity detection. Removes regions without speech, yet detects even the shortest words ("yes", "oh", "hmm").
Our world-class speaker diarization technology not only detects overlapping speech, it can also isolate overlapping speakers and return one separate audio stream per speaker.
Our world-class speaker diarization technology highlights the most complex parts of a conversation automatically. Use confidence scores to filter out the noisiest parts of your training data, or for human-in-the-loop use cases.
