DeepFilterNet is a full-band audio low-complexity speech enhancement framework.

This framework supports Linux, macOS and Windows, and the structure of the framework is as follows:

  • libDF Contains Rust code for data loading and augmentation
  • DeepFilterNet Contains DeepFilterNet code training, evaluation and visualization, and pretrained model weights
  • pyDF-data Contains a Python wrapper for libDF dataset functionality and provides a PyTorch data loader.
  • ladspa A LADSPA plugin is included for real-time noise suppression.
  • models Contains pre-training used in DeepFilterNet (Python) or libDF/deep-filter (Rust).



Download precompiled binaries from the Release page, you can use deep-filter to suppress noise in noisy .wav audio files. Currently, only wav files with a sampling rate of 48kHz are supported.

    deep-filter [OPTIONS] [FILES]...


    -D, --compensate-delay
            Compensate delay of STFT and model lookahead
    -h, --help
            Print help information
    -m, --model <MODEL>
            Path to model tar.gz. Defaults to DeepFilterNet2.
    -o, --out-dir <OUT_DIR>
            [default: out]
            Enable postfilter
    -v, --verbose
            Logging verbosity
    -V, --version
            Print version information

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