Run sh make_debug.sh to generate json files for the toy dataset. Python -m denoiser.live -in "Soundflower (2ch) " -out "NAME OF OUT IFACE " Training and evaluation Quick Start with Toy Example Through pip (you just want to use pre-trained model out of the box) Installationįirst, install Python 3.7 (recommended with Anaconda). The proposed model is based on the Demucs architecture, originally proposed for music source-separation: ( Paper, Code). It is optimized on both time and frequency domains, using multiple loss functions.Įmpirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb.Īdditionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities. The proposed model is based on an encoder-decoder architecture with skip-connections. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. 
We provide a PyTorch implementation of the paper: Real Time Speech Enhancement in the Waveform Domain. Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)
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