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:
libDFContains Rust code for data loading and augmentation
DeepFilterNetContains DeepFilterNet code training, evaluation and visualization, and pretrained model weights
pyDF-dataContains a Python wrapper for libDF dataset functionality and provides a PyTorch data loader.
ladspaA LADSPA plugin is included for real-time noise suppression.
modelsContains 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.
USAGE: deep-filter [OPTIONS] [FILES]... ARGS: <FILES>... OPTIONS: -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] --pf Enable postfilter -v, --verbose Logging verbosity -V, --version Print version information
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