SaaS Metrics
Product Positioning & Context
Hush removes competing voices, background noise, and audio interference from real-time calls so your voice AI agents always hear what matters.
Related Ecosystem & Alternatives
Discover adjacent products, open-source repositories, and developer tools sharing similar technical architecture.
Deep-Dive FAQs
What is Hush?
Hush is a digital product or tool described as: Open-source noise suppression for voice AI agents
Where did Hush originate?
Data for Hush was aggregated directly from the Product Hunt community ecosystem, representing raw developer and early-adopter sentiment.
When was Hush publicly launched?
The initial public indexing or launch date for Hush within our tracked developer communities was recorded on June 23, 2026.
How popular is Hush?
Hush has achieved measurable traction, logging over 171 traction score and facilitating 23 recorded discussions or engagements.
Which technical categories define Hush?
Based on metadata extraction, Hush is categorized under topics such as: Open Source, Developer Tools, Artificial Intelligence.
Is Hush recognized by media or academic researchers?
Yes. It has been covered by media outlets like Wattsupwiththat.com. This indicates the concept has reached a level of mainstream or scientific viability beyond just developer forums.
What are some commercial alternatives to Hush?
Our semantic intelligence engine identifies potential commercial alternatives in the SaaS space, such as Hush, which offers overlapping value propositions.
How does the creator describe Hush?
The original author or development team describes the product as follows: "Hush removes competing voices, background noise, and audio interference from real-time calls so your voice AI agents always hear what matters."
Community Voice & Feedback
Real-time noise suppression always involves tradeoffs - curious what the actual pipeline latency looks like end-to-end, not just model inference. WebRTC jitter buffers, chunking, and resampling all add overhead on top of the model itself, and for voice AI phone agents that budget is already tight with STT + LLM + TTS in the chain. Also wondering how it handles overlapping speakers mid-sentence vs. steady-state noise - that's usually where suppression models fall apart. How does it compare to what Deepgram or Twilio already offer natively in their voice pipelines?
Looks good! Congrats
This seems pretty useful. We would love to give it a try!
This is exactly the kind of voice-agent infra where the test set matters more than the demo clip. I would love to see three numbers side by side: added latency per frame, word deletion rate for quiet primary speakers, and false retention when a second speaker is louder than the caller. The open-source angle is especially useful if teams can run the same stress clips before deploying it into live calls.
What’s the latency like in real time calls, and does it ever clip or distort the speaker’s voice?
Sub-ms matters because voice UX breaks when the audio path gets clever but slow. The edge case I would watch is the handoff between suppression and downstream turn detection; a clean stream is useful only if it preserves the timing signals.
Thanks everyone for the amazing support so far! We're excited to hear your thoughts and answer any questions you have. Your feedback will help shape the future of Hush.
Excited to test and use this in my ongoing peoject. The cpu only is a game changer. Thankyou for making this open source, I was searching, something like this!
Most noise suppression libraries are built for human listeners, where "good enough" means the person on the other end doesn't notice. For voice AI agents the bar is different because the model is doing ASR first, and artifacts that a human brain filters out can wreck transcription accuracy pretty badly. Curious whether Hush is tuned specifically for that ASR pipeline use case or whether it's general-purpose suppression you're applying upstream. Also wondering how it handles near-field keyboard noise and fan hum during long agent sessions, since those tend to be the consistent offenders in real deployments.
Hey everyone 👋 I'm the maker of Hush. Here's the story behind why we built it.We build Voice AI at Weya. AI agents that handle live phone calls for businesses. And the #1 issue that kept breaking our pipeline wasn't the LLM, wasn't the TTS. It was background speech.A caller phones in from a busy restaurant. Their colleague is talking next to them. A TV is blaring in the background. What happens? The background speaker's words get picked up, transcribed, and fed into the AI agent as if the caller said them. The entire conversation derails.We tried every open-source noise cancellation model out there: DeepFilterNet3, RNNoise, SEGAN, MetricGAN+, DNS Challenge entrants. They all do a great job suppressing stationary noise (fans, traffic, HVAC). But none of them treat a competing human voice as a first-class problem. When the interference is another person speaking, speech looks like speech in every feature these models have learned. They either let it leak through, or they suppress both speakers and destroy intelligibility.So we built Hush from scratch to fix exactly this.What it does: Hush removes both background noise AND background speech from live audio, isolating only the primary speaker. It's an 8 MB model that runs fully on CPU in real time (
Sub-1ms on CPU is the claim that matters most here and also the one I'd want stress-tested. What's the degradation curve? Does it hold at 1ms with a single stream, and what happens at 10 or 50 concurrent calls on commodity hardware? That's the production reality for anyone running voice agents at scale.The open-source angle is smart for adoption but the real question is where the commercial model sits. Apache 2.0 gets you into production stacks fast. What's the wedge that converts users to paying customers?
The CPU-only, sub-1ms-per-frame number is what jumped out at me. Most enhancement I've tried adds enough latency to break the natural turn-taking on a live call. We build voice AI that phones elderly parents at home, where the hard part is exactly what you describe: a TV going in the background, a spouse talking across the room, sometimes a hearing aid whistling. My question: when the primary speaker is quiet, slurred, or unsteady (pretty common with older users), does isolating them ever clip that softer speech? Planning to test Hush on some of our real call audio.
Hey Product Hunt! I'm @lordhasanali , CEO of weya AI.We watched great voice AI fail in production, over and over, not because of the model, but because of the audio. Noisy environments, competing voices, background hum. Nobody was solving this properly, so we did.Introducing Hush, our first in-house open-source speech enhancement model, which:• Isolates the primary speaker and removes everything else in real time• Runs entirely on CPU, under 1ms per frame - no GPU needed• Language-agnostic - works across all spoken languages out of the box• Apache 2.0 - free to use in production todayWe launched at #5 on HuggingFace's Audio-to-Audio leaderboard, and this is just the start.We'll be here all day answering questions. Try it, break it, and let us know what you think!
Discovery Source
Product Hunt Aggregated via automated community intelligence tracking.
Tech Stack Dependencies
No direct open-source NPM package mentions detected in the product documentation.
Media Tractions & Mentions
- POLITICO: ‘Climate hushing’ – ‘The sound of (climate) silence’ – ‘As Trump unravels U.S. climate policy, Dems & enviro groups have grown strangely quiet’ – Dems ‘may not aggressively pursue climate action if they win back power’
- ‘Climate Hushing’: Pragmatism Demoting Exaggeration (McKibben fools himself)
- The Dyson HushJet Compact Air Purifier is powerful, quiet, and proof that good things do come in small(ish) packages
- Malala's brother Khushal on fleeing the Taliban and facing the manosphere
Deep Research & Science
No direct peer-reviewed scientific literature matched with this product's architecture.