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67 lines
1.7 KiB
Markdown
67 lines
1.7 KiB
Markdown
# ReNameNoise
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ReNameNoise - a fork of [RNNoise](https://gitlab.xiph.org/xiph/rnnoise) - is a noise suppression library based on a recurrent neural network.
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A description of the algorithm is provided in the following paper:
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```
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J.-M. Valin, A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech
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Enhancement, Proceedings of IEEE Multimedia Signal Processing (MMSP) Workshop,
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arXiv:1709.08243, 2018.
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https://arxiv.org/pdf/1709.08243.pdf
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```
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An interactive demo is available at: https://jmvalin.ca/demo/rnnoise/
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## Prerequisites
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To build the library with the existing, pre-trained network data you will need to install the following packages:
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* build-essential
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* cmake
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## Build
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To compile, open a terminal in the repository root directory and type:
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```bash
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cmake .
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make
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```
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To compile the library only, without the demo executable:
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```bash
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cmake -DRENAMENOISE_DEMO_EXECUTABLE=OFF .
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make
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```
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## Usage
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While it is meant to be used as a library, a simple command-line tool is
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provided as an example. It operates on RAW 16-bit (machine endian) mono
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PCM files sampled at 48 kHz. It can be used as:
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``./examples/renamenoise_demo <noisy speech> <output denoised>``
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The output is also a 16-bit raw PCM file.d
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## Training
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Training is not necessary to use the library as presented in this repository.
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However, if you want to train the network on your own samples you need to follow these steps:
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```bash
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cd src ; ./compile.sh
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./denoise_training signal.raw noise.raw count > training.f32
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# (note the matrix size and replace 500000 87 below)
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cd training ; ./bin2hdf5.py ../src/training.f32 500000 87 training.h5
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./rnn_train.py
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./dump_rnn.py weights.hdf5 ../src/rnn_data.c ../src/rnn_data.h
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```
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