---
license: other
license_name: nvidia-one-way-noncommercial-and-mit-apache
license_link: LICENSE
base_model: nvidia/Real-time_RE-USE
pipeline_tag: audio-to-audio
library_name: hush-ai
tags:
  - speech-enhancement
  - noise-suppression
  - noise-cancellation
  - real-time
  - streaming
  - mamba
  - deepfilternet
  - rust
  - cpu
  - on-device
---

<p align="center">
  <img src="icon.png" width="104" alt="Hush AI">
</p>

<h1 align="center">Hush AI · voice models</h1>

<p align="center">
  The model files that the <a href="https://github.com/alshell7/hush-ai"><b>Hush AI</b></a> app downloads:
  free, open-source, on-device noise cancellation for every app.
</p>

<p align="center">
  <a href="https://github.com/alshell7/hush-ai/releases/latest"><b>Download the app</b></a> ·
  <a href="https://alshell7.github.io/hush-ai/"><b>Website</b></a> ·
  <a href="https://github.com/alshell7/hush-ai"><b>GitHub</b></a>
</p>

![Hush AI app and tray widget](https://raw.githubusercontent.com/alshell7/hush-ai/main/assets/screenshots/hero.png)

## What is here

| File | Contents | License |
| --- | --- | --- |
| `models/DeepFilterNet3_onnx-v0.5.6.tar.gz` | [DeepFilterNet 3](https://github.com/Rikorose/DeepFilterNet) by Hendrik Schröter, from release `v0.5.6`, **unchanged** (ONNX encoder and two decoders, 8 MB) | MIT or Apache-2.0 |
| `models/realtime-reuse-4c0c186.safetensors` | NVIDIA [Real-time RE-USE](https://huggingface.co/nvidia/Real-time_RE-USE) weights, revision `4c0c186`, **unchanged** (FP32, 13.5 M parameters, 54 MB) | NVIDIA One-Way Noncommercial |
| `models/SHA256SUMS` | SHA-256 of both model files | |
| `samples/noisy.mp3`, `samples/hush.mp3` | NVIDIA's sample recording, before and after Hush AI | NVIDIA One-Way Noncommercial |

The app checks the SHA-256 before it uses a file. If this mirror is unreachable, it
downloads the same file from its original source at the same version.

## How Hush AI runs the models

[Hush AI](https://github.com/alshell7/hush-ai) has two engines, and you can switch between them
in the app. Both run on the CPU, with no Python, GPU or network:

- **DeepFilterNet 3**: the official Rust inference (libDF with tract), 48 kHz, 10 ms frames.
  Light on the CPU, and its license allows commercial use.
- **NVIDIA Real-time RE-USE**: a native Rust port of NVIDIA's Mamba model, 20 ms frames.

RE-USE quality settings in the app:

| Quality | Model setting | Apple M1, 2 threads (p99 per 20 ms frame) |
| --- | --- | ---: |
| Light (app default) | 16 kHz, 3 layers | 11.7 ms |
| Balanced | 24 kHz, 4 layers | 21.3 ms |
| Studio | 48 kHz, 3 layers | 36.8 ms |

- **Delay:** about 60–80 ms plus device buffers.
- **Accuracy:** NVIDIA's seven GPU regression cases pass in CI on Windows, macOS and Linux.
  The CPU output matches NVIDIA's GPU reference at 113–118 dB waveform SNR on short speech.
- **Comparison:** measured against DeepFilterNet 3 and RNNoise on VoiceBank-DEMAND in
  [COMPARISON.md](https://github.com/alshell7/hush-ai/blob/main/docs/COMPARISON.md).

Use the RE-USE weights from Rust with the [`hush-core`](https://github.com/alshell7/hush-ai/tree/main/crates/hush-core) crate:

```rust
let mut denoiser = hush_core::Denoiser::with_config(
    "models/realtime-reuse-4c0c186.safetensors",
    hush_core::Config { layers: 3, sample_rate: 16_000, look_ahead: 0 },
)?;
let mut clean = Vec::new();
denoiser.process_into(&noisy_chunk, &mut clean)?;
```

## Licenses

- **DeepFilterNet 3** (`models/DeepFilterNet3_onnx-v0.5.6.tar.gz`): © Hendrik Schröter, MIT
  ([LICENSE-DeepFilterNet-MIT](models/LICENSE-DeepFilterNet-MIT)) or Apache-2.0
  ([LICENSE-DeepFilterNet-APACHE](models/LICENSE-DeepFilterNet-APACHE)), at your option.
  Commercial use is allowed. Paper: Schröter et al., *DeepFilterNet: Perceptually Motivated
  Real-Time Speech Enhancement*, Interspeech 2023.
- **Real-time RE-USE** (`models/realtime-reuse-4c0c186.safetensors`) and the samples:
  NVIDIA's work, redistributed unchanged under the **NVIDIA One-Way Noncommercial License**
  ([LICENSE](LICENSE)): **non-commercial research and educational use only**. NVIDIA
  Corporation and its affiliates may use them commercially. See the original
  [NVIDIA model card](https://huggingface.co/nvidia/Real-time_RE-USE) for the model, its
  training and its papers.

The Hush AI app and engine are Apache-2.0. Hush AI is an independent project and is not
affiliated with or endorsed by NVIDIA or the DeepFilterNet authors.