Your agent is only
as good as what it hears.

Voice Isolation 2.5 separates the person speaking from everything around them, so your STT gets one clean voice.

Voice Isolation 2.5

Same STT engine with and without Voice Isolation

See the full benchmark

Raw microphone

As you see, those words Kumquat. keep on popping up in the Juxtapose. background. And we don't see them over here. Zucchini.

3 interfering words

After Krisp Voice Isolation

As you see, those words keep on popping up in the background. And we don't see them over here.

0 interfering words

3 interfering words removed

46%
fewer word errors
70%
on background voices
15 ms
latency, runs on CPU
Organizations worldwide trust us
Discord
Twilio
RingCentral
oVice
Daily
Dyte
Aircall
PhoneBurner
Vonage
Symphony
CarrierX
Zoho Voice
Altea Healthcare
Phound
Gather
Interact
Roam
LiveKit
Vapi
Telnyx
Tavus
Notch
Discord
Twilio
RingCentral
oVice
Daily
Dyte
Aircall
PhoneBurner
Vonage
Symphony
CarrierX
Zoho Voice
Altea Healthcare
Phound
Gather
Interact
Roam
LiveKit
Vapi
Telnyx
Tavus
Notch

Why agents break

Demos work. Real calls don't.

Three things go wrong, and all three start in the audio, not in your LLM.

A second voice

Modern speech to text handles noise well. Another person talking is a different problem. The engine transcribes both voices and hands your agent the mixture.

The wrong moment

Agents wait for silence to decide the turn is over. People pause mid thought. The result is dead air on one side and talk over on the other.

Every "mhm" is a stop

To an agent, a listener saying mhm sounds like an interruption. So it stops talking when nobody asked it to.

Integration

It sits underneath the conversation

VIVA runs first, in front of your speech-to-text. Everything after it inherits a clean signal.

Input Real audio
Krisp VIVA 15 ms
Transcribe STT
Reason LLM
Speak TTS

Runs where you run

What's in VIVA SDK

Five models. One integration.

Each model does one job well. Run one, or run them together.

Voice Isolation

Separates the person speaking from everything around them: competing voices, background chatter, room echo. Your STT gets one clean voice, shaped so the engine reads it the way it was trained to.

  • 46% lower WER
  • Runs on CPU
  • lite: 3.5x less compute
  • 15 ms
  • 16 kHz

New · Voice Isolation 2.5

Voice isolation your STT
can actually read.

We measured this the way a skeptic would. 1,685 real-world recordings, three conditions, ten STT engines from seven vendors, nothing tuned to any single engine.

46%
fewer word errors
15.3% → 8.2%
70%
on background voices
35.9% → 10.9%
2.15%
on harm on clean audio
vs 2.10% no processing
77.4%
on the best single engine
22.5% → 5.1%

Voice Isolation comes
in four different flavors

Voice Isolation has one job: keep speech-to-text working when the audio is bad.

Request SDK Access

The main model

Default

Strongest at removing noise and other speakers. Goes fully silent when only a bystander is talking.

Lighter processing

Balanced

Removes noise and background voices with lighter processing while the main speaker is talking, so the voice sounds more natural.

A third of the compute

Lite

Comparable quality on average at about a third of the compute. For edge, embedded and high-concurrency deployments.

32 kHz

HD

Built for 32 kHz high-fidelity streams such as WebRTC or WebSocket, rather than narrowband telephony codecs.

Two sizes

Isolation on every stream,
without the CPU bill

Most teams cap isolation to the calls they think need it. Now both models are light enough to leave on.

New: 70% less compute

Voice Isolation 2.5 lite

Matches the default on typical calls, with a gap only in extreme conditions. Built for high volume server fleets, edge, mobile and embedded.

Choose it when

CPU is the constraint

With FP16*

209 → 358 live calls

The default

Voice Isolation 2.5

Strongest in the hardest conditions: extreme noise, dense background voices, badly degraded streams.

Choose it when

Worst-case quality matters most

With FP16*

76 → 138 live calls

Both models
Latency 15 ms Compute Runs on CPU Bandwidth 16 kHz
How to read the lite numbers
Lite is measured on the same 1,685 recordings as the default model. On typical calls the two are within noise of each other; the gap opens only in the hardest conditions, where the full model keeps more of the target voice intact.

New: CPU optimization

Run 2× the voice agents on the same CPU

One SDK flag runs Voice Isolation in 16-bit math on CPUs built for it. Same model, same quality, about twice the live calls per server.

AWS Graviton3 · Arm NEON Intel Sapphire Rapids · AVX-512

On Ice Lake (c6i, m6i, r6i)? Move to Graviton3 (c7g) or Sapphire Rapids (c7i) and turn the flag on.

See the full benchmark

Live calls per server

Standard FP16 flag on
Voice Isolation 2.5 lite +71%
209
358
Voice Isolation 2.5 +82%
76
138

*FP16 figures are for these CPUs, with the SDK flag on. Same model and quality.

Integration

Drops into what you run

One call in front of your speech to text. Nothing downstream changes.

  1. 01 Connect once session = krisp.viva.connect(model="…v2.5")
  2. 02 Clean each frame, before your STT clean = session.process(frame)   # 15 ms, on CPU
  3. 03 Push it on, to any engine stt.push(clean)
Full specs and everything it runs with
Server-side, on-prem or in your own cloud. Python, Node.js, Go and iOS SDKs, with LiveKit, Pipecat and raw WebRTC pipelines supported out of the box. PCM S16LE, 16 kHz mono in and out.

Already on VIVA? It's a model swap.

Integration is unchanged. Point your pipeline at the new model.

Server SDK docs

Run it on your own calls.

Tell us your use case and we'll set you up with the SDK and a trial to start.

Customers

From voice AI teams

David Casem

“At scale, the biggest challenge in voice AI isn’t the model. It’s the quality of the signal going into it. Krisp addresses that at the source, which improves everything downstream from transcription to response.”

David Casem CEO of Telnyx
Kwindla Kramer

“The Krisp VIVA noise reduction, voice isolation, and turn taking models are a big unlock for production voice agents. Highly, highly recommended.”

Kwindla Kramer Co-Founder at Daily
Stanislav Vishnevskiy

“Our users expect world-class quality from all of their communication channels within Discord, and especially with their voice communications. Integrating Krisp’s AI-based Noise Cancellation technology into our desktop and mobile applications was seamless.”

Stanislav Vishnevskiy CTO & Co-Founder at Discord
Guarav Agarwal

“We are thrilled to partner with Krisp, a leader in Voice AI, to provide exceptional audio quality to the billions of conversations on the Twilio Video platform, allowing customers to build engaging virtual experiences across telehealth, education, remote work, and more.”

Guarav Agarwal VP of product at Twilio
Zach Koch

“Integrating Krisp eliminates one of the biggest challenges—unwanted interruptions—ensuring seamless, uninterrupted conversations.”

Zach Koch Founder and CEO at Ultravox
Dr. Navdeep Dhaliwal

“By integrating Krisp’s advanced AI Noise Cancellation technology, we’re enhancing physician dictation accuracy and transforming telemedicine communication, reinforcing Aarista’s position as a leader in healthcare communication and patient care.”

Dr. Navdeep Dhaliwal Director of Clinical Informatics at Aarista
Howard Lerman

“Roam Inventors are working around the clock to deliver the #1 Cloud HQ meeting experience. Our new partnership with Krisp means our members will get the best available noise cancellation on the planet.”

Howard Lerman Founder and CEO
Quinn Favret

“Krisp is a meaningful part of the equation to improve audio quality for us — and importantly, it just works. That’s exactly what we need from an infrastructure partner.”

Quinn Favret COO & Co-Founder at Tavus
Yukari Minai

“oVice customers can now enjoy more comfortable conversations in the business metaverse with access to the world's best AI noise-cancellation technology.”

Yukari Minai PR at oVice
Kumar Saurav

“To ensure exceptional customer experiences as we scale, we’re integrating Krisp’s best-in-class noise cancellation technology to empower Vodex to reach new heights in delivering clear and uninterrupted voice interactions.”

Kumar Saurav Co-founder & CTO at Vodex

Have questions?
We’ve got answers.

What's in VIVA 2.5?
Voice isolation, turn-taking and signal detectors, in one SDK. 2.5 is a big step on the isolation side, which is where the word error rate numbers come from.
Doesn't voice isolation make transcripts worse?
It can, and we measured exactly where. On the examples that were most problematic for Voice Isolation 2.1, it stripped too much and pushed word errors above leaving the audio alone. We published that. 2.5 closes most of that gap, and we published the part that remains, because it is not fully closed. On clean audio it now beats untouched, on average.
Which STT engines does it work with?
Any of them. It cleans the audio before your STT sees it. Benchmarked across ten streaming and non-streaming systems from seven vendors, including Deepgram, NVIDIA, Soniox, ElevenLabs and AssemblyAI.
Do the models need transcription or per-language setup?
No. They operate directly on the audio signal, are language agnostic, and need no per-language tuning.
How do we know the numbers are real?
Run it on your own audio. We will set up a trial on your calls. That is the only number that matters to you anyway.
We can't spare the footprint.
There's a lite model at 70% less compute, and on typical calls it matches the default. A gap may appear in extreme conditions, so if worst-case quality matters more to you than compute, take the default. If CPU is the constraint, take lite.

Run it on your own calls.

Tell us your use case and we'll set you up with the SDK and a trial on your audio.

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