Please refer to conf/config.yaml for a reference of the possible options. You should see a file named debug.yaml with the relevant configuration for the debug sample set.Ĭommand line, for instance. Notice, under the conf folder, the dset folder contains the configuration files for The config file with all relevant arguments for training our model can be found under the conf folder. Generally, Hydra is an open-source framework that simplifies the development of research applicationsīy providing the ability to create a hierarchical configuration dynamically. We use Hydra to control all the training configurations. 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)
DENOISER 2 INSTALL
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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