{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/22"},{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/11"},{"label":"Application Frameworks","url":"https://skillfed.io/packages/category/software-development-libraries-application-frameworks/6"},{"label":"Speech","url":"https://skillfed.io/packages/category/multimedia-sound-audio-speech"}],"enrichment":{"capability":"DeepFilterNet removes background noise from full-band audio at 48kHz using deep learning models, providing both command-line and Python API interfaces for speech enhancement.","skillfed_tags":["audio-processing","speech-enhancement","deep-learning"],"use_cases":["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."],"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.\n\nYou 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.","worth_installing":"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."},"id":"deepfilternet","links":{"html":"https://skillfed.io/packages/deepfilternet","md":"https://skillfed.io/packages/deepfilternet.md","pypi":"https://pypi.org/project/deepfilternet/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2023-08-31","license_spdx":null,"license_treatment":"permissive","name":"deepfilternet","python_support":"supports_current","summary":"Noise supression using deep filtering"},"popularity":{"monthly_downloads":78622,"position":14421,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.6"}