Universal-3 Pro Streaming is the most accurate real-time STT model for voice agents. With entity detection, speaker labels, and code switching, it's built for the hard stuff: disfluencies, alphanumerics, and noisy environments. One API. 99+ languages. Try it free.
Hey PH 👋 We just shipped the most accurate real-time STT model for voice agents.
Universal-3 Pro Streaming is a first-of-its-kind realtime Speech Language Model built for the hard stuff voice agents actually encounter (disfluencies, emails, URLs, names, account numbers, alphanumerics, and code-switching across languages). All in noisy conditions. All at super low latency.
Here's what we kept seeing: incorrect credit card numbers. Turn detection cutting off customers mid-sentence. Speaker labels scrambled in multi-party calls. Voice agent failures cluster around the edge cases that matter most to your users. Existing streaming models weren't solving them.
So we took every capability from Universal-3 Pro and brought it to real-time streaming. Plus two new capabilities that didn't exist in streaming before: real-time speaker diarization and global language support through the same API.
We'd love to see what you build with it! 🚀
good luck on the launch guys, using your api sometimes for voice to text and vice versa
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This is very cool! Looking forward to trying it out.
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Voice logging in FuelOS runs on streaming STT and the place it consistently fell apart was alphanumeric strings, things like "vitamin B12" or "omega-3" getting mangled mid-stream. How does Universal-3 Pro handle those in noisy kitchen environments specifically, where background noise compounds the problem?
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good luck on the launch guys, using your api sometimes for voice to text and vice versa
This is very cool! Looking forward to trying it out.
Voice logging in FuelOS runs on streaming STT and the place it consistently fell apart was alphanumeric strings, things like "vitamin B12" or "omega-3" getting mangled mid-stream. How does Universal-3 Pro handle those in noisy kitchen environments specifically, where background noise compounds the problem?