Tan_pytorch_segmentation/pytorch_segmentation/PV_RNNOISE-master/README.md

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2025-05-19 20:48:24 +08:00
# 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 <speech> <noise> <sample count> <output denoised>
# ./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
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