https://github.com/mumble-voip/renamenoise
A fork of RNNoise with non-clashing symbols, the original tried and tested model, CMake support, and other optimizations
https://github.com/mumble-voip/renamenoise
Last synced: 7 months ago
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A fork of RNNoise with non-clashing symbols, the original tried and tested model, CMake support, and other optimizations
- Host: GitHub
- URL: https://github.com/mumble-voip/renamenoise
- Owner: mumble-voip
- License: bsd-3-clause
- Created: 2024-04-03T12:49:25.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2024-04-03T19:09:37.000Z (about 1 year ago)
- Last Synced: 2024-10-15T03:53:10.507Z (8 months ago)
- Language: C
- Homepage:
- Size: 4.12 MB
- Stars: 4
- Watchers: 6
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: COPYING
- Authors: AUTHORS
Awesome Lists containing this project
README
# ReNameNoise
ReNameNoise - a fork of [RNNoise](https://gitlab.xiph.org/xiph/rnnoise) - is a noise suppression library based on a recurrent neural network.
A description of the algorithm is provided in the following paper:
```
J.-M. Valin, A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech
Enhancement, Proceedings of IEEE Multimedia Signal Processing (MMSP) Workshop,
arXiv:1709.08243, 2018.
https://arxiv.org/pdf/1709.08243.pdf
```
An interactive demo is available at: https://jmvalin.ca/demo/rnnoise/
## Prerequisites
To build the library with the existing, pre-trained network data you will need to install the following packages:
* build-essential
* cmake
## Build
To compile, open a terminal in the repository root directory and type:
```bash
cmake .
make
```
To compile the library only, without the demo executable:
```bash
cmake -DRENAMENOISE_DEMO_EXECUTABLE=OFF .
make
```
## Usage
While it is meant to be used as a library, a simple command-line tool is
provided as an example. It operates on RAW 16-bit (machine endian) mono
PCM files sampled at 48 kHz. It can be used as:
``./examples/renamenoise_demo ``
The output is also a 16-bit raw PCM file.d
## Training
Training is not necessary to use the library as presented in this repository.
However, if you want to train the network on your own samples you need to follow these steps:
```bash
cd src ; ./compile.sh
./denoise_training signal.raw noise.raw count > training.f32
# (note the matrix size and replace 500000 87 below)
cd training ; ./bin2hdf5.py ../src/training.f32 500000 87 training.h5
./rnn_train.py
./dump_rnn.py weights.hdf5 ../src/rnn_data.c ../src/rnn_data.h
```