# RNNoise is a noise suppression library based on a recurrent neural network ## Quick Demo application While it is meant to be used as a library, a simple command-line tool is provided as an example. ### build librnnoise & rnnoise_demo with CMake ```shell # mkdir build # cd build # cmake .. # make ``` It operates on wav and mp3 files, which can be used as: ```shell # ./rnnoise_demo input.wav # ./rnnoise_demo input.mp3 ``` the output filename is "input_out.wav" or: specify the output filename ```shell # ./rnnoise_demo input.wav output.wav # ./rnnoise_demo input.mp3 output.wav ``` ## Training Process ### Audio feature extract Build audio feature extraction tool ```shell # cd src # ./train_compile.sh ``` Use generated "denoise_training" to get the audio feature array from speech & noise audio clip ```shell # ./denoise_training usage: ./denoise_training # ./denoise_training speech.wav noise.wav 50000 feature.dat matrix size: 50000 x 87 ``` ### RNN model traning Pick feature array to "training" dir and go through the training process ```shell # cd training # mv ../src/feature.dat . # python bin2hdf5.py --bin_file feature.dat --matrix_shape 50000x87 # python rnn_train.py # python dump_rnn.py ``` Training process will generate the RNN model weight code file (default is rnn_data.c) and layer definition header file (default is rnn_data.h). They can be used to refresh the "src/rnn_data.c", "src/rnn_data.h" and rebuild the rnnoise lib & demo app. # References and Resources: - [david8862/rnnoise](https://github.com/david8862/rnnoise) - [RNNoise: Learning Noise Suppression](https://people.xiph.org/~jm/demo/rnnoise/) - [RNNoise: Learning Noise Suppression（深度学习噪声抑制）](https://blog.csdn.net/dakeboy/article/details/88039977) - [基于RNN的音频降噪算法](https://cloud.tencent.com/developer/article/1094567) # Donating If you found this project useful, consider buying me a coffee