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DeepFilterNet

App : A product you use directly

Real-time noise suppression, lightweight, works even on CPU.

The most effective open-source denoiser for voice.

Official site GitHub · 4,882 ★

Overall score

6.6 /10

Security: nothing to flag

Utility

8.0

Reliability

1.0

Security

No caveat on record

Ease

Technical · Technical

Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.

What it does, concretely

Audio recordings often contain unwanted background noise: fans, keyboards, street sounds or office ambience. DeepFilterNet addresses this problem by suppressing such noise in real time, without sending your files to a remote server.

The tool is based on a technique called Deep Filtering applied to full-band audio at 48 kHz. It comes in three successive versions (DeepFilterNet, DeepFilterNet2, DeepFilterNet3), each improving perceptual quality and reducing computational complexity. It can be used either via a precompiled command-line binary or via a LADSPA plugin integrated with PipeWire for live microphone filtering.

DeepFilterNet is aimed at developers, podcasters, streamers and advanced users on Linux or Windows who want to improve voice quality without relying on an online service. It can also be integrated into Python scripts through the package available on PyPI.

However, it is not suited for users looking for a simple graphical interface or working exclusively on macOS: the interactive demo and model training are only tested on Linux, and the tool remains command-line oriented.

What it offers

·Real-time noise suppression on 48 kHz WAV audio files via the deep-filter command.
·LADSPA plugin compatible with PipeWire for live microphone noise filtering during calls or recordings.
·Three pretrained models available (DeepFilterNet, DeepFilterNet2, DeepFilterNet3) offering progressive quality levels.
·Precompiled binary with no Python dependency, ready to use after download on Linux and Windows.
·Python integration via pip (deepfilternet package) for use in scripts or audio processing pipelines.
·Post-filter option (--pf) to further attenuate very noisy sections.
·CPU processing without requiring a GPU, with optional GPU support via the PyTorch backend.

Our tips

Use the --pf option to enable the post-filter: it slightly over-attenuates very noisy sections and can improve results on heavily degraded recordings.
If timing accuracy matters (subtitling, editing), add the -D (--compensate-delay) option to compensate for the delay introduced by STFT processing.
To filter your microphone live on Linux, integrate the LADSPA plugin with PipeWire via filter-chain: this avoids post-processing files after recording.
To select a specific model, use the -m option followed by the model name (for example DeepFilterNet3): DeepFilterNet2 is loaded by default if none is specified.
If you want to use a GPU to speed up processing, install the PyTorch backend with CUDA support and use the Python interface rather than the Rust binary.

Watch out

Only 48 kHz WAV files are supported by the precompiled binary: any file in a different format or sample rate must be converted beforehand.
The interactive demo and model training are only tested on Linux; Windows and macOS users may encounter limitations for these advanced uses.

In practice

Price: Free
Commercial use: Commercial use: yes
Available on: cli, windows, linux
Licence: MIT
Runs locally: Yes
FR interface: No

Security and data

Rating: Security: nothing to flag
Your data: Data stays local
Maintenance: Inactive for over six months
Last release: 31 August 2023

Links and repository checked on 25 October 2026 · added on 1 January 2021

How to install DeepFilterNet

1. Go to the GitHub releases page (github.com/Rikorose/DeepFilterNet/releases) and download the precompiled binary for your system (Linux or Windows). 2. Verify the integrity of the downloaded file if checksums are provided on the release page. 3. Place the binary in a folder accessible from your terminal (for example /usr/local/bin on Linux). 4. For Python installation, run: pip install deepfilternet (after installing PyTorch from pytorch.org). 5. Test the installation by running: deep-filter --version or deepFilter path/to/noisy_audio.wav. 6. All processing is done locally: no audio data is sent to an external service.

pip install deepfilternet

In the same category

Whole category Audio, voice & music →