Hush is live · pricing from $0.12/min · last verified 24 Oct 2026 · viability healthy
Hush
Hush is Weya AI's open-source noise suppression model that cleans noisy voice AI calls in under 1 ms per 10 ms frame on CPU.
If your ASR failures trace back to bad call audio, Hush is one of the few noise suppression models shipping concrete numbers instead of marketing: ~8 MB, ~0.9 ms per 10 ms frame, CPU-only, 10,000+ hours of noisy training audio, #5 on Hugging Face's Audio-to-Audio leaderboard at launch. Self-hosting means you own scaling and monitoring, so budget for that. If you would rather not run a model, Weya's hosted platform bundles noise cancellation at $0.12+/minute for voice agents with $10 in free
Verdict generatedVerified 6d ago · liveness 71/100 · cite: rightaichoice.com/tools/hush
- Voice AI developers whose ASR fails on noisy real-world call audio
- BFSI and lending teams deploying call center voice bots in noisy environments
- Customer support AI running in field operations, streets, or shared offices
- Teams that need CPU-only, self-hosted noise suppression in their own cloud
- Non-developers wanting a plug-and-play audio filter with no setup
- Anyone denoising music, ambience, or non-voice audio
- Teams without ML or media-engineering capacity to run a self-hosted model
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Skip Hush if you want a managed denoiser you never operate yourself, or if your audio is music rather than speech.
Hosted platform pricing is per minute of voice agent usage ($0.12+/minute), so noise cancellation is only economical if you are also buying Weya's agent platform
Weya's hosted pay-as-you-go is $0.12+/minute for voice agents and $0.062+/Session for chat agents, with $10 free credits and 3 free concurrent calls, which suits small teams piloting one workflow. Enterprise, discounted for volumes over 50,000 min/mo, is aimed at large call operations. If you only want noise suppression and not an agent platform, self-hosting the open-source model avoids per-minute fees entirely.
In short
Hush — Hush is Weya AI's open-source noise suppression model that cleans noisy voice AI calls in under 1 ms per 10 ms frame on CPU. Best for Voice AI developers whose ASR fails on noisy real-world call audio, BFSI and lending teams deploying call center voice bots in noisy environments, Customer support AI running in field operations, streets, or shared offices. Plans from $0.12/min.
What is Hush?
Hush — Hush is Weya AI's open-source noise suppression model that cleans noisy voice AI calls in under 1 ms per 10 ms frame on CPU. Best for Voice AI developers whose ASR fails on noisy real-world call audio, BFSI and lending teams deploying call center voice bots in noisy environments, Customer support AI running in field operations, streets, or shared offices. Plans from $0.12/min.
Hush is Weya AI's in-house, open-source noise suppression model, built to fix the audio layer of voice AI rather than the model layer. It strips background noise and competing voices from real-world phone calls so your ASR and voice agents hear the main caller, not the cafe, the traffic, or the TV. The pitch from Weya is blunt: most voice AI failures come from bad audio, not bad models. Hush fixes the call signal at the source. The operational numbers are the reason to look. Each 10 ms audio frame is cleaned in about 0.9 ms on a standard CPU with no GPU required, and the model is roughly 8 MB, small enough to drop into your own cloud or data center. Weya trained it on 10,000+ hours of real-world noisy audio covering overlapping speakers, traffic, office buzz, fans, construction and street noise, plus sudden sounds like honks and announcements. At launch it landed #5 on Hugging Face's Audio-to-Audio leaderboard. Distribution runs through GitHub and Hugging Face, so you can bolt it onto an existing real-time voice pipeline as a preprocessing step. If you would rather not run the model yourself, Weya's own platform bundles noise cancellation into the pay-as-you-go and Enterprise plans, alongside 40+ languages and accents and inbound plus outbound call support. Cleaner input means fewer "sorry, can you repeat that?" loops before you ever touch your ASR vendor. The trade-off is who does the work. Hush is a model, not a managed audio service, and self-hosting pushes deployment, scaling and monitoring onto your team. Teams that want a managed audio stack instead should compare against Deepgram's noise cancellation before committing.
Key features
- Open-source noise suppression model on GitHub and Hugging Face
- Real-time audio cleaning on standard CPU with no GPU required
- ~0.9 ms typical processing per 10 ms audio frame
- ~8 MB model size, deployable in your own cloud or data center
- Trained on 10,000+ hours of real-world noisy audio
- Isolates the main caller and suppresses competing voices and TV noise
- Handles traffic, office buzz, fans, construction and street noise
- Softens sudden sounds such as honks, bangs and announcements
- Cleaner audio reduces ASR errors and repeat requests
- Supports 40+ languages and accents
- Works for inbound and outbound calls
- Self-hosted or cloud deployment options
- Noise cancellation bundled in Weya AI pay-as-you-go and Enterprise plans
- #5 on Hugging Face Audio-to-Audio leaderboard at launch
- 2-week pilot: implement and measure lift on one workflow before full rollout
Behind the verdict
Reach for Hush when your voice pipeline is already built and the failure mode is upstream: callers drowning in street noise, side conversations bleeding into the transcript, ASR dropping the first syllable of every sentence. Weya's framing is the right one — you don't need a bigger model, you need a cleaner signal. Cleaning 10 ms of audio in under 1 ms on CPU means this slots into a real-time path without a GPU line item, and 8 MB is easy to argue for in a deployment review. Where it bites is the same place every self-hosted model bites. You get a model, not an on-call rotation. Latency budgets, autoscaling, audio format plumbing, and monitoring for drift all land on your team. If your company doesn't have media-engineering or ML ops capacity, the model sitting on GitHub is not the win it looks like on paper. The closest alternative most buyers will weigh is Deepgram's noise cancellation, and the split is simple: Deepgram sells you a managed audio stack, Weya hands you a model you can own. Pick Hush if data residency, cost-per-minute at volume, or control over preprocessing matters. Pick Deepgram if you'd rather pay someone to keep the audio layer running. There's also the bundled route. If you're already evaluating Weya's voice agent platform, noise cancellation is included in both the pay-as-you-go and Enterprise plans, so the "do we run Hush ourselves" question may already be answered. For volume buyers above 50,000 minutes a month, Enterprise pricing is discounted against volumes, which changes the math versus per-minute managed alternatives. On the free path: Weya's pay-as-you-go signup comes with $10 in credits and 3 free concurrent calls, and the model itself is open-source on GitHub and Hugging Face. That's about as low-risk a pilot as it gets — the harder
Models under the hood
as of 2026-10-24
Limitations
- Hush is an open-source noise suppression model, so self-hosting means you own deployment, scaling and monitoring; it is also positioned as the audio layer of Weya AI's voice-agent platform, where noise cancellation is bundled into plans priced per minute or per session.
- The live pages state CPU processing of under 1 ms per 10 ms frame (~0.9 ms typical) and an ~8 MB model size, but do not document accuracy, latency or quality on GPU or outside that frame budget.
- The homepage and pricing pages link to GitHub and Hugging Face, yet the scraped evidence does not document an API, developer docs or SDK, so API access should be verified directly before committing.
as of 2026-10-05
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Is Hush still active?
Hush is live · pricing from $0.12/min · last verified 24 Oct 2026 · viability healthy.
Viability score
How well maintained and how widely used is Hush? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What do users say about Hush?
What people actually say about Hush — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
ResearchedDiscussed by users across 6 sources (Hacker News, YouTube, Product Hunt, App Store, Stack Overflow, Lemmy) · researched Oct 19, 2026.
Weighted by the 91 posts each of 6 sources contributed.
- +CPU-only inference at roughly 0.9 ms per 10 ms frame keeps real-time latency low without GPUs
- +Tiny ~8 MB model footprint makes it easy to bundle into existing cloud or data-center deployments
- +Trained on 10,000+ hours of real noisy audio including overlapping speakers and street noise
- +Open-source distribution via GitHub and Hugging Face means no vendor lock-in for the model itself
- +Targets the audio layer specifically, which is where many voice AI failures actually originate
- −Zero genuine user reviews in the scraped data — nothing corroborates the performance claims
- −Self-hosted deployment pushes scaling, monitoring and ops work onto the buyer's team
- −No managed audio service; teams wanting turnkey noise cancellation must look at Deepgram instead
- −Name collisions with unrelated products make authentic community feedback nearly impossible to find
- −No third-party benchmark data in the scrape to verify the sub-millisecond latency claim
- • Self-hosting means you pay for compute, ops staff, and monitoring that a managed service would bundle
- • Weya's pay-as-you-go and Enterprise pricing is not disclosed in the data, requiring a sales call
- • Two-week pilot still consumes engineering time to integrate and measure before any full rollout
How we verified this
Every listed fact about Hush— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 24 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Hush 12 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed SSL certificate
- — changed integrations, who should skip it, description, features, summary, who it suits, pricing tiers, our verdict
- — changed limitations, pricing page
- — changed pricing model
- — changed setup time, who it suits, description, our verdict, features, hidden costs, limitations, switching in, switching out, who should skip it, pricing tiers, pricing fit, summary, use cases
- — changed limitations
Showing the 6 most recent of 12 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Hush cost in 2026?
Hush: Plans from $0.12/min. Pricing last verified 24 Oct 2026.
Plans compared
Every published Hush tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Pay as you go | From $0.12/minVendor: “$0.12+/minute for AI Voice Agents; $0.062+/Session for AI” | usage | Small teams piloting a single voice workflow who want noise cancellation bundled without an upfront commitmentAdds: Starting tier: $0.12+/minute for voice agents, $10 free credits, 3 concurrent calls, 1 workflow, 40+ languages and accents |
| Enterprise | Price not publishedVendor: “Discounted pricing based on volumes (over 50,000 min/mo)” | — | Large call operations running over 50,000 min/mo that need high concurrency and onboarding supportAdds: Adds 100+ concurrent calls (vs 3), realtime CRM sync, multiple RAG knowledge bases, workflow builder, omnichannel memory layer, agent A/B testing and white-glove onboarding |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Hush's pricing actually pencils out — and where peers do it cheaper.
Weya's hosted pay-as-you-go is $0.12+/minute for voice agents and $0.062+/Session for chat agents, with $10 free credits and 3 free concurrent calls, which suits small teams piloting one workflow. Enterprise, discounted for volumes over 50,000 min/mo, is aimed at large call operations. If you only want noise suppression and not an agent platform, self-hosting the open-source model avoids per-minute fees entirely.
Has Hush's pricing changed?
No plan price for Hush has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Hush?
Pick Hush if you are Voice AI developers whose ASR fails on noisy real-world call audio, BFSI and lending teams deploying call center voice bots in noisy environments, Customer support AI running in field operations, streets, or shared offices, Teams that need CPU-only, self-hosted noise suppression in their own cloud.
Look elsewhere if you are Non-developers wanting a plug-and-play audio filter with no setup, Anyone denoising music, ambience, or non-voice audio, Teams without ML or media-engineering capacity to run a self-hosted model, Buyers who want a fully managed audio service end to end.
Skip it if Skip Hush if you want a managed denoiser you never operate yourself, or if your audio is music rather than speech.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Hush actually fits — and what changes day-one when you adopt it.
You run a debt collection bot whose transcripts are full of missed words because callers dial in from busy streets. You pull Hush from Hugging Face, run it as a CPU preprocessing step in front of your ASR on the same media server, and keep your GPU pool for the LLM.
Outcome: Cleaner call audio reaches ASR, the caller's voice is isolated from background conversation, and your agent repeats itself less often on live calls.
Your loan sanction calls drop when borrowers are on the move. You take the two-week pilot with one workflow on Weya's hosted platform, where noise cancellation is bundled, and measure completion lift before rolling out.
Outcome: You get measured lift on one workflow before committing to a broader rollout, with $10 in free credits and 3 free concurrent calls to start.
Field agents on headsets pick up traffic, wind and office buzz. You deploy the ~8 MB Hush model in your own cloud to clean inbound and outbound call audio ahead of compliance recording.
Outcome: Compliance recordings have clearer speaker isolation and agents stop asking customers to repeat themselves over background noise.
Use cases
- Clean noisy customer calls before ASR in debt collection bots
- Suppress open-office chatter so lead nurturing voice agents hear the caller
- Reduce traffic and street noise for field agent AI assistants
- Pre-process audio for compliance recording with clearer speaker isolation
- Improve completion rates in Indian NBFC loan sanction calls
- Drop into a real-time voice pipeline to cut agent repetition
- Isolate the main speaker on calls where background conversation overlaps
Setup time & first value
How long it actually takes to get something useful out of Hush — broken out by persona, not the marketing-page minute.
Self-hosting developers: minutes to pull the ~8 MB model from GitHub or Hugging Face, then an afternoon to wire it into your real-time audio pipeline and validate on your own noisy call samples. Teams using Weya's hosted platform: fast, since noise cancellation ships with the pay-as-you-go plan and $10 in free credits, with a two-week pilot offered to prove lift on one workflow.
Switching to or from Hush
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Deepgram noise cancellation: move to Hush if you want the model self-hosted on CPU instead of a managed audio API, and keep Deepgram for ASR
- →From an in-house denoiser: drop the ~8 MB Hush model into your existing pipeline as the preprocessing step and retire the custom model
- ↗To Deepgram noise cancellation: use it if you want a fully managed audio stack rather than operating the model yourself
- ↗To Weya's hosted platform: stop self-hosting and let noise cancellation run inside Weya's voice agent plans at $0.12+/minute
Integrations
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Hush”, and we withheld 6: 6 could not be judged, because “Hush” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Hush.
Official links
What are the best Hush alternatives?
The closest alternatives to Hush we list are Vision Agents, LiveKit and Dograh. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Vision Agents | Free | Free | — |
| LiveKit | Freemium | Pricing not published | — |
| Dograh | Contact Sales | Custom pricing | — |
Alternatives to Hush
View all
Vision Agents
Open-source Python framework for building real-time voice and video AI agents with any model.
Tools that pair well with Hush
Common stack mates teams adopt alongside Hush, with the specific reason each pairing earns its keep.
Vision Agents
Open-source Python framework for building real-time voice and video AI agents with any model.
LiveKit
Open-source framework plus developer cloud for building, testing, deploying and scaling real-time voice, video, and physical AI agents.
Dograh
Open-source voice AI platform for self-hosted speech-to-speech agents with BYOK model keys.
Hush vs its closest rival
Hush's closest head-to-head is with Spider Cloud — Spider Cloud and Hush serve completely different domains: Spider Cloud is a web scraping API optimized for AI agents and RAG, while Hush is an open-source noise suppression model for voice AI. Choose Spider Cloud if you need real-time web data extraction; choose Hush if you are building voice agents and need to clean audio on CPU. They are complementary, not competing. Verdict generated
Head-to-head comparisons
Hush vs Spider Cloud
Spider Cloud and Hush serve completely different domains: Spider Cloud is a web scraping API optimized for AI agents and RAG, while Hush is an open-source noise suppression model for voice AI. Choose Spider Cloud if you need real-time web data extraction; choose Hush if you are building voice agents and need to clean audio on CPU. They are complementary, not competing.
GeneratedHush vs Voyage Ai
Voyage AI and Hush serve completely different needs: Voyage AI is for enterprises optimizing RAG pipelines with domain-specific embeddings and rerankers (costly, custom), while Hush is a free, open-source noise suppression model for voice AI developers. Choose Voyage for high-accuracy retrieval on finance/legal data, Hush for real-time audio cleanup on a budget.
GeneratedHush vs Temporal Ai
Temporal AI and Hush solve completely different problems. Temporal is for teams building resilient, long-running workflows and AI agents that survive failures; it's powerful but overkill for simple tasks. Hush is a focused noise suppression tool that makes voice AI calls clearer, especially in chaotic environments. Choose based on your need: durable orchestration or audio preprocessing.
GeneratedFrequently asked questions
Best-of guides
Topics
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