Hush
Open-source noise suppression that cleans live voice AI calls on CPU.
Hush is a solid, free, open-source noise suppression model that effectively cleans up noisy call audio for voice AI pipelines, cutting ASR errors and improving agent clarity. Its CPU-only operation and 8MB footprint make it easy to deploy anywhere. If you're comfortable with self-hosting and integrating an ML model, it's a strong choice. Teams without ML Ops experience might prefer a managed solution like Deepgram's noise cancellation, but for developers who want control, Hush is a compelling option.
Verified 6d ago · liveness 69/100 · cite: rightaichoice.com/tools/hush
- Voice AI developers in noisy environments
- BFSI teams deploying call center voice bots
- Customer support AI in field operations
- Startups needing free low-latency noise suppression
- Non-developers seeking plug-and-play audio filter
- Users needing real-time noise suppression for music
- Teams demanding GPU-accelerated quality
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Skip Hush if you're not comfortable with ML model integration and self-hosting, or if you need a fully managed noise cancellation service with dedicated support and enterprise SLAs.
Weya AI platform charges $0.12+ per minute, which adds up at high call volumes—self-hosting the model avoids this but requires your own infrastructure
Hush's open-source model is free to self-host. For managed usage, Weya AI's Pay-as-you-go at $0.12+/minute is competitive for startups but more expensive than self-hosting for high volumes. For enterprises, custom Enterprise pricing includes advanced features. Compare to Deepgram's noise cancellation, which is priced per audio hour but may offer simpler integration.
In short
Hush — Open-source noise suppression that cleans live voice AI calls on CPU. Best for Voice AI developers in noisy environments, BFSI teams deploying call center voice bots, Customer support AI in field operations. Free to start; paid plans from $0.12.
What people actually say about Hush — is it worth it?
We scanned public community sources for Hush on Jul 2, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
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: September 2026
How we score →Key Features
- Real-time noise suppression on CPU
- Processes 10ms audio frames in under 1ms
- Open-source model (GitHub/Hugging Face)
- Trained on 10,000+ hours of noisy audio
- Isolates main speaker, suppresses background
- Handles traffic, office buzz, street, construction
- Softens sudden sounds (honks, bangs)
- Lightweight model (~8MB)
- Self-hosted or cloud deployment
- Integrates with Weya AI platform
- No GPU required
- Supports 40+ languages and accents
- Reduces ASR errors
- Works with inbound/outbound calls
About Hush
Hush is an open-source noise suppression model from Weya AI, designed to remove background noise and competing voices from real-time voice calls. It processes each 10ms audio frame in under 1ms on standard CPUs—no GPU required—making it ideal for voice AI developers and BFSI teams deploying conversational agents in noisy environments. Trained on over 10,000 hours of real-world noisy audio, Hush isolates the main speaker, suppresses competing voices, and handles traffic, office buzz, street noise, and sudden sounds like honks. The model is lightweight at ~8MB, deployable in your own cloud or data center, and available on GitHub and Hugging Face. At launch, it ranked #5 on Hugging Face's Audio-to-Audio leaderboard. Hush integrates with Weya AI's voice agent platform, but you can also self-host it as a preprocessing step in your own pipeline. It supports 40+ languages and accents, reduces ASR errors, and is built for developers who want control over their audio stack.
Behind the Verdict
Hush stands out because it addresses a specific pain point—noisy audio in voice AI calls—with a lightweight, CPU-friendly model that developers can integrate directly. Unlike many noise suppression tools that require GPU acceleration or heavy infrastructure, Hush processes each 10ms frame in under 1ms on standard CPUs, making it practical for real-time use. The open-source nature (available on GitHub and Hugging Face) means you can inspect, modify, and self-host the model, giving you full control over your data and latency. Strengths: Performance on CPU is impressive for a real-time model. The training data covers diverse noisy environments, which is critical for call centers and field operations. The model also isolates the main speaker, which is particularly useful when multiple people are talking. The 8MB size makes deployment easy, even on edge devices. Weaknesses: Using Hush requires ML engineering skills—there's no plug-and-play UI. You'll need to handle deployment, scaling, and monitoring yourself if you self-host. The provided performance stats (e.g., 0.9ms processing) are from the vendor, so your mileage may vary depending on hardware. Weya AI's platform pricing adds per-minute costs, and the free tier limits you to 3 concurrent calls, which may not be enough for production. Where it fits: Best for developers building voice AI agents who need a noise suppression layer that can run on any CPU. Also ideal for BFSI teams deploying bots in call centers or field operations where background noise is a constant issue. Where it doesn't: For non-technical users seeking a plug-and-play audio filter, Hush is not the right fit. Also, if you require noise suppression for music or other non-speech audio, Hush is designed for speech and may not meet your needs.
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Real-world workflow fit
Concrete scenarios for the personas Hush actually fits — and what changes day-one when you adopt it.
You're building an outbound loan sanction bot that calls customers in noisy environments. You self-host Hush on a CPU instance and integrate it into your pipeline to preprocess audio before sending to ASR.
Outcome: Clean audio improves ASR accuracy, reducing customer repetitions and increasing successful loan sanction completions.
You deploy Weya AI's voice agent platform with Hush noise cancellation enabled to handle calls from field agents in high-noise areas.
Outcome: Agent and AI hear customers clearly, reducing call escalations and improving satisfaction scores.
You need to clean live calls for compliance recording. You download Hush from Hugging Face, run it locally on a CPU, and feed the output to your existing recording system.
Outcome: You achieve clearer recordings for compliance without GPU costs, staying within your infrastructure budget.
Use Cases
- Clean up noisy customer calls for ASR transcription in debt collection bots
- Suppress background chatter for lead nurturing voice agents in open offices
- Reduce traffic 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 minimize agent repetition
Models Under the Hood
as of 2026-09-01
Limitations
- Hush is an open-source noise suppression model that requires engineering effort to integrate.
- It processes 10 ms audio frames in under 1 ms on CPU.
- Self-hosting means you handle deployment, scaling, and monitoring.
- Using Weya AI's platform includes costs per minute; the free tier supports only 3 concurrent calls.
as of 2026-09-08
Verification history
We have re-verified Hush 8 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.
- — re-checked, vendor evidence unchanged
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Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
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.
Plans compared
For each published Hush tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Pay as you go
$0.12+/minute
Ideal for
Startups and small teams testing voice AI with low call volumes, needing flexibility without upfront costs.
What this tier adds
Entry tier includes noise cancellation, 3 concurrent calls, 1 workflow, and email support.
Enterprise
Contact for pricing
Ideal for
Large companies with over 50,000 minutes per month requiring dedicated support and advanced features like multiple RAG and memory.
What this tier adds
Adds 100+ concurrent calls, unlimited CRM sync, multiple RAG, workflow builder, and white-glove onboarding.
Where the pricing makes sense
The company stage and team size where Hush's pricing actually pencils out — and where peers do it cheaper.
Hush's open-source model is free to self-host. For managed usage, Weya AI's Pay-as-you-go at $0.12+/minute is competitive for startups but more expensive than self-hosting for high volumes. For enterprises, custom Enterprise pricing includes advanced features. Compare to Deepgram's noise cancellation, which is priced per audio hour but may offer simpler integration.
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.
For a developer with ML integration experience, self-hosting Hush on a CPU server can take 1-2 hours. If you use Weya AI's platform, enabling noise cancellation is quick, but implementing a full voice AI workflow may take longer.
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 raw audio pipelines: Add Hush as a preprocessing step to clean audio before ASR, typically by calling the model via API or running it locally.
- ↗To managed noise cancellation: Export your noise suppression logic and switch to a service like Deepgram, but note you'll lose self-hosting control.
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
Featured 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.
Hush 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.
Hush 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.
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