Krisp Voice Translation API

Real-time speech-to-speech translation API

API

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The custom translation_dictionary that locks an exact term-to-term mapping is the detail I'd actually reach for — brand and product names are usually the first thing these pipelines mangle. On the free 60-min tier, is the dictionary per-request or stored server-side once you set it up?

OH my, I was looking for something like this for months at this point. Do you have plans to integrate this into your mobile app as a native functionality?

@asti_pili tangential question-- how does this work with speaker attribution when multiple people are in a room together? This is my biggest pet peeve with most transcription agents today. Granola's amazing if I use my phone, and has nothing when using my laptop. My kingdom for good attribution regardless of setting! What does Krisp do here?

Interesting breakdown of the latency tradeoffs. The language reordering problem is something many demos conveniently avoid discussing. I was wondering how you're handling workload spikes when multiple streams require larger context windows simultaneously. Do you dynamically allocate translation capacity per stream, or is there some form of queueing and prioritization to prevent latency from cascading across tenants?

Curious how deep the localization goes. Spanish in Mexico and Spanish in Spain can feel quite different in everyday use. Do you support regional variations like that?

Congrats on the launch!

Real-time speech-to-speech is the hard part - what's the round-trip latency at acceptable quality? Most translation APIs I've tested hit 800ms+ which is fine for async but breaks conversational flow completely.

How well does Custom Vocabulary / Dictionary work? How many terms can I add, and does it slow things down?

Congrats on the launch! 🚀 Real-time voice translation is impressive on its own, but production experience in healthcare and finance makes it even more compelling. Best of luck today!

Real-time speech-to-speech at API level means you've solved the three-stage pipeline problem: ASR accuracy, translation context, and TTS naturalness all simultaneously. We've built on streaming audio APIs and the hardest part is always mid-utterance interruptions breaking the translation context. What's your P99 latency for a 10-second utterance, and how do you handle speaker turn overlap?

Looking forward to seeing what the Product Hunt community builds with it. We'd love your feedback!

Impressive to see accuracy claims based on real contact center traffic instead of lab conditions. How does the API handle industry-specific terminology, like healthcare or financial services vocabulary, where a single mistranslation can create major issues?

The "works great in demos, then real users show up with background noise and accents" line is exactly the wall we hit building voice AI for older adults. Phone-quality audio and unfamiliar accents break most pipelines that benchmark beautifully. Training on a million real contact-center calls is a smart moat for that reason. One question on the speech-to-speech path: how much added latency does translation introduce over plain transcription, and is it low enough to keep a live call feeling like a natural back-and-forth?