deepfilternet
Noise supression using deep filtering
Decision gist · record as of 2026-08-14
Yes, if you need robust speech denoising at 48kHz and can tolerate dormant maintenance. The package is stable, well-documented, has no known vulnerabilities, and carries a permissive MIT license. Install friction is low. Verify external deep learning dependencies and model availability before production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- External deep learning framework must be installed separately; pretrained models are loaded at runtime and may require network access.
- Low install friction; pure Python wheel with standard dependencies.
- Maintenance is dormant—last release was 2023-08-31 and last commit 2024-10-17, so expect no active bug fixes or feature updates, though the codebase remains stable.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you may use, modify, and distribute this package freely in commercial or private projects with minimal restrictions.
last release 2023-08-31 (1079 days) · last repo commit 2024-10-17 · 4,596 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 78,622 downloads/mo, #14,421 on PyPI
Alternatives
Verify before relying
pip install deepfilternet
from deepfilternet import enhance, init_df
model, df_state, _ = init_df()
enhanced_audio = enhance(model, df_state, noisy_audio)- Whether pretrained models are bundled with the wheel or must be downloaded separately at runtime.
- GPU acceleration support and whether external deep learning dependencies are pinned or flexible.
- Real-time latency and CPU/memory footprint for typical audio streams.
- Exact PyTorch version compatibility and whether it must be installed before deepfilternet.
What it is and what it does
DeepFilterNet is a speech enhancement framework that uses deep learning to suppress noise from audio at 48kHz sampling rate. It wraps a Rust-based STFT/ISTFT processing core with a Python interface, offering both a command-line tool and a programmatic API for batch or real-time noise reduction. The package depends on numpy for numerical work, loguru for logging, requests for downloads, packaging for version handling, sympy for symbolic math, appdirs for configuration paths, and deepfilterlib for core filtering logic.
You can use it to clean up noisy recordings by passing audio files through a pretrained model, or integrate it into Python applications via the init_df() and enhance() functions. The framework supports multiple model variants and includes optional postfiltering for aggressive noise attenuation. Training and dataset preparation are documented but require additional dependencies not bundled in the base wheel.
Use it for
- Clean up noisy voice recordings or podcast audio before publishing or further processing.
- Suppress background noise in real-time video conferencing or streaming applications.
- Preprocess speech data for downstream tasks like transcription or speaker recognition.
- Batch denoise archived audio collections or archival speech datasets.
- Integrate noise reduction into assistive audio devices via the LADSPA plugin.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need robust speech denoising at 48kHz and can tolerate dormant maintenance.
The package is stable, well-documented, has no known vulnerabilities, and carries a permissive MIT license. Install friction is low. Verify external deep learning dependencies and model availability before production use.
Install
deepfilternet on PyPI
Before you install
Low install friction; pure Python wheel with standard dependencies. Maintenance is dormant—last release was 2023-08-31 and last commit 2024-10-17, so expect no active bug fixes or feature updates, though the codebase remains stable.
External deep learning framework must be installed separately; pretrained models are loaded at runtime and may require network access.
License in practice
MIT license is permissive; you may use, modify, and distribute this package freely in commercial or private projects with minimal restrictions.
Quickstart
pip install deepfilternet
from deepfilternet import enhance, init_df
model, df_state, _ = init_df()
enhanced_audio = enhance(model, df_state, noisy_audio)Verify before relying
- Whether pretrained models are bundled with the wheel or must be downloaded separately at runtime.
- GPU acceleration support and whether external deep learning dependencies are pinned or flexible.
- Real-time latency and CPU/memory footprint for typical audio streams.
- Exact PyTorch version compatibility and whether it must be installed before deepfilternet.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8,<4.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagesappdirsdeepfilterliblogurunumpypackagingrequestssympy |
| Maintenance | Dormant 1,079 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 78,622 / month, #14,421 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Programming Language :: RustTopic :: Multimedia :: Sound/Audio :: SpeechTopic :: Software Development :: Libraries :: Application FrameworksTopic :: Software Development :: Libraries :: Python ModulesTopic :: Utilities |
Evidence: deepfilternet-0.5.6-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “noise suppression deep learning”
- deepfilternetDeepFilterNet removes background noise from full-band audio at 48kHz…
- pyrnnoisePython bindings for RNNoise that perform real-time noise suppression…
- batchgeneratorsbatchgenerators provides data augmentation transforms for 2D and 3D…
Give your agent the search over MCP, or paste the wish link into any chat.
More Python Modules packages
Converts domain names between Unicode and ASCII-compatible encoding (Punycode) according to IDNA 2008 and Unicode Technical Standard 46, with security validation and broader script coverage than the standard library.
Install it if you work with internationalized domain names, need to validate domains, or use HTTP clients that depend on it transitively.
Setuptools is a Python build backend and package management tool that handles building, distributing, and installing Python packages, including support for C/C++ extension modules.
PyYAML parses and emits YAML 1.1 data format, enabling serialization and deserialization of configuration files and Python objects to and from human-readable YAML text.
Pydantic validates Python data structures against type hints, coercing and checking input at runtime to ensure it matches a declared schema.
Provides reusable metadata objects for use with PEP-593 `typing.Annotated` to express common constraints like bounds, collection sizes, and predicates on types.
Install it if you use or build libraries that need to express type constraints in a standardized, inspectable way—or if you want to annotate your own types with…
Provides runtime tools to inspect and introspect Python type annotations, enabling programmatic examination of type hints at execution time.
See also noisereduce · pyrnnoise · livekit-plugins-ai-coustics · torchaudio · pystoi · speechbrain · livekit-plugins-noise-cancellation · torchcrepe · audiomentations · transparent-background