DeepFilterNet
DeepFilterNet is a free, open-source speech enhancement framework for full-band 48 kHz audio that uses deep filtering.
What DeepFilterNet is
DeepFilterNet is a free, open-source speech enhancement framework for full-band 48 kHz audio that uses deep filtering. Its command-line tool suppresses noise in WAV files and currently supports only 48 kHz sampling. A LADSPA plugin provides real-time noise suppression and can work with PipeWire filter chains to create a virtual microphone or audio output, including for VoIP apps such as Zoom or Discord. The plugin has a minimum 20 ms latency from STFT processing, plus any additional delay from its LADSPA host. The Rust command-line tool uses DeepFilterNet2 by default, while the Python interface documents DeepFilterNet and DeepFilterNet2. An optional post-filter can slightly over-attenuate very noisy sections. The framework supports Linux, macOS, and Windows, but training is tested only under Linux. Its Python package installs with pip as deepfilternet; an optional train extra adds data-loading functionality for Linux training. Training expects HDF5 datasets and a configuration file for train, validation, and test sets. Precompiled binaries and a Python wheel are available.
Good
- Free and open source under MIT or Apache-2.0
- Includes command-line processing and a real-time LADSPA plugin
- PipeWire filter chains can create a virtual microphone
- Framework supports Linux, macOS, and Windows
Watch out
- Command-line processing currently supports only 48 kHz
- Plugin latency starts at 20 ms plus host delay
- Training is tested only under Linux
- Post-filter may over-attenuate very noisy sections
Who it suits
It suits people seeking WAV noise suppression or a real-time noise-filtering path through LADSPA and PipeWire. Developers can also use the Python package or train with HDF5 datasets, with training tested only on Linux.
Verdict
DeepFilterNet offers both file-based and real-time speech enhancement options. Its 48 kHz processing limit and plugin latency matter when matching it to an audio workflow.
Plans and prices
From DeepFilterNet's own pricing page, checked 4 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| DeepFilterNet | Free | Free and open source · MIT or Apache-2.0 license |
| Free and open source | Free | MIT or Apache-2.0 licensing · self-hosted framework · Linux, macOS and Windows support |
The facts
Each one read from DeepFilterNet's own pages, with the date it was checked.
What it does
- Audio coverage
- The framework targets 48 kHz full-band speech audio.checked 15 Oct 2026
- CLI limitation
- The precompiled deep-filter command currently accepts only WAV files sampled at 48 kHz.checked 15 Oct 2026
- Distribution
- The project provides precompiled binaries through its GitHub releases and a Python wheel through PyPI.checked 4 Oct 2026
- Embedded performance
- The DeepFilterNet2 paper reports a real-time factor of 0.04 on a notebook Core-i5 CPU and describes embedded real-time use.checked 15 Oct 2026
- Latency
- The LADSPA plugin has a minimum latency of 20 ms from STFT processing, plus latency from the LADSPA host.checked 4 Oct 2026
- Latest release
- GitHub lists version 0.5.6 as the latest release and says it fixes issues 421 and 293.checked 15 Oct 2026
- Licence
- The repository is free and dual-licensed so users may choose MIT or Apache License 2.0.checked 15 Oct 2026
- License
- The repository code is free and open source under either the MIT License or Apache License 2.0, at the user's option.checked 4 Oct 2026
- Limit: ASR use
- A Hacker News commenter cautioned that DeepFilterNet would not improve an automatic-speech-recognition model such as Whisper.checked 15 Oct 2026
- Limit: Colab installation
- A 2025 GitHub issue reports metadata-generation failure while installing deepfilternet 0.5.6 in Google Colab.checked 15 Oct 2026
- Limit: dependency setup
- A Hacker News user reported frustrating Poetry setup involving keyring behavior over SSH.checked 15 Oct 2026
- Limit: package installation
- A GitHub issue reports that installing deepfilternet 0.5.3 failed because the required deepfilterlib 0.5.3 distribution was unavailable.checked 15 Oct 2026
- Models and processing
- The Rust command-line tool defaults to DeepFilterNet2, while the Python interface also documents use of DeepFilterNet and DeepFilterNet2 models.checked 4 Oct 2026
- Noise suppression
- Its command-line tool suppresses noise in WAV recordings.checked 15 Oct 2026
- Package activity
- SkillFed lists the package as dormant, with its latest release dated 2023-08-31.checked 15 Oct 2026
- Package downloads
- SkillFed reports 78,622 monthly downloads for deepfilternet in its displayed 30-day window.checked 15 Oct 2026
- PipeWire integration
- The LADSPA plugin can be used in PipeWire filter chains to create a virtual noise-suppressed microphone.checked 15 Oct 2026
- Post-filter
- An optional post-filter slightly over-attenuates very noisy sections.checked 4 Oct 2026
- Postfilter
- An optional postfilter is documented as slightly over-attenuating very noisy sections.checked 15 Oct 2026
- Praise: CPU efficiency
- A Hacker News commenter reported adequate inference speed on plain CPU using an ONNX-based deployment.checked 15 Oct 2026
- Praise: overall capability
- A Hacker News commenter called DeepFilterNet the most well-rounded open-source AI noise-suppression tool they had found.checked 15 Oct 2026
- Pretrained models
- The command-line and Python workflows can load pretrained DeepFilterNet, DeepFilterNet2 and DeepFilterNet3 models.checked 15 Oct 2026
Where it runs
- Framework platforms
- The framework supports Linux, macOS, and Windows; training is only tested under Linux.checked 4 Oct 2026
- Hugging Face demos
- Hendrik Schröter's Hugging Face profile lists two DeepFilterNet Spaces, including one marked Runtime error.checked 15 Oct 2026
The full review of DeepFilterNet
Overview
DeepFilterNet is an open-source speech-enhancement framework for processing recordings and building live audio chains. It suits technically comfortable users who want noise suppression they can integrate into their own desktop workflows. Its flexibility and zero price are appealing, but a 48 kHz input ceiling and plugin delay rule it out for some audio setups.
Key features
File processing and models
The command-line tool suppresses noise in WAV files and defaults to the DeepFilterNet2 model. The Python interface documents both DeepFilterNet and DeepFilterNet2, making it the more adaptable route for users building custom processing workflows. The command-line tool currently supports only 48 kHz audio, so recordings at other sample rates need to be converted before processing.
Live noise suppression
A LADSPA plugin enables real-time suppression. With PipeWire filter chains, it can create a virtual microphone or audio output for use with VoIP apps such as Zoom and Discord. That is useful for users willing to configure their audio routing, but this is not a ready-made call interface. The plugin adds at least 20 ms of latency from STFT processing, before the LADSPA host's own delay; that can be a drawback for latency-sensitive workflows.
Installation and training
Precompiled binaries are available through GitHub releases, and the Python package installs with pip as deepfilternet. An optional training extra adds data-loading functionality, but training is only tested under Linux. Training also requires HDF5 datasets and a configuration file defining the train, validation and test sets, so it is aimed at users prepared to manage a machine-learning data pipeline.
Post-filter
An optional post-filter slightly over-attenuates very noisy sections. It may be useful when stronger suppression matters more than preserving every detail in difficult passages, but that trade-off makes it less suitable when natural-sounding output is the priority.
Pricing
DeepFilterNet is free and open source. Its sole plan costs nothing and uses the MIT License or Apache License 2.0, at the user's option. There is no paid tier or free-trial period to weigh against it. The zero price makes it accessible for individual projects, while users must handle their own installation and audio configuration rather than relying on a turnkey call app.
Platforms
The framework supports Linux, macOS and Windows, although training is only tested under Linux. The real-time plugin's PipeWire workflow is specifically relevant to Linux audio routing. Precompiled binaries and a Python wheel provide installation routes, but DeepFilterNet's 48 kHz processing limit applies to the command-line tool.
Who it's for
DeepFilterNet is a good fit for developers, audio enthusiasts and other technically confident users who want to process 48 kHz WAV recordings, integrate suppression into a Python workflow, or configure live noise reduction through PipeWire. It is a weaker choice for people who want a simple call app, need to process audio at other sample rates directly, or cannot tolerate the plugin's baseline delay.
Pros and cons
- Pro: Free, open-source licensing under either MIT or Apache 2.0 lets users choose a license for their project.
- Pro: File processing, a Python interface and a LADSPA plugin support several ways to incorporate noise suppression.
- Pro: PipeWire chains can supply a virtual microphone or output to VoIP apps, including Zoom and Discord.
- Con: The command-line tool handles only 48 kHz WAV audio, limiting direct use with recordings at other sample rates.
- Con: The plugin adds at least 20 ms of processing latency, with more delay from its host.
- Con: Training entails HDF5 datasets and configuration work, and is only tested under Linux.
Alternatives
Kleervox is worth considering for users who want a virtual microphone for calls, neural noise cancellation and acoustic echo cancellation, with a free plan capped at 1 hour of cleaning per day. Noise Blocker is a Windows alternative with a 30-day business trial for up to 20 employees or computers, subject to request and approval. NVIDIA ShadowPlay is another free Windows option, provided the system meets its Windows 10 or 11 and GeForce driver requirements and has a supported GPU.
DeskBroadcast is a macOS alternative with a free version and a one-time Pro plan priced at $79, paid once for three Macs. Linux users seeking a simpler noise-suppression option can consider NoiseTorch-ng, which supports PulseAudio or PipeWire. NoNoise Mac is a free, open-source option for macOS. Krisp AI Meeting Assistant offers a seven-day free trial for readers considering a broader meeting-focused tool. Noise Suppression for Voice is a free option for Linux, macOS and Windows.
Explore more options in AI Noise Cancellation Software and AI Speech Enhancement Software.
Verdict
Choose DeepFilterNet if you want free, open-source speech enhancement that you can incorporate into file processing, Python workflows or a PipeWire audio chain. Its integration options are the main reason to choose it; the 48 kHz command-line limit, configuration demands and plugin latency are the reasons to look elsewhere if you need broad-format processing or a low-delay, ready-to-use call tool.
Getting started
- Visit the DeepFilterNet GitHub repository.
- Download a precompiled binary from GitHub releases, or install the Python wheel from PyPI.
- For Python use, install the package with pip as deepfilternet.
- Use the Rust command-line tool for WAV processing or the LADSPA plugin for real-time suppression.
- For PipeWire use, configure the plugin with a filter chain; for Linux training, add the optional train extra.
Limits worth knowing
The command-line tool currently supports only 48 kHz WAV audio. Training is tested only under Linux, and the real-time plugin has at least 20 ms of STFT processing latency plus LADSPA host latency.
Questions
Does DeepFilterNet cost anything?
No. It is free and open source under either the MIT License or Apache License 2.0.
Which platforms does it support?
The framework supports Linux, macOS, and Windows. Training is tested only under Linux.
What audio can the command-line tool process?
It suppresses noise in WAV files and currently supports only a 48 kHz sampling rate.
Can it suppress noise in real time?
Yes. The project includes a LADSPA plugin for real-time noise suppression. Its minimum STFT processing latency is 20 ms, plus any LADSPA host latency.
How can I install it?
Precompiled binaries are available through GitHub releases, and a Python wheel is available through PyPI. The Python package installs with pip as deepfilternet.
Which model does it use?
The Rust command-line tool defaults to DeepFilterNet2. The Python interface documents DeepFilterNet and DeepFilterNet2.
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