Modified from https://github.com/xiongyihui/speexdsp-python
You can use it in Noise reduction model training as said in Personalized PercepNet: Real-time, Low-complexity Target Voice Separation and Enhancement.
Use a VAD and lightweight denoiser (SpeexDSP1) to eliminate the stationary noise before using this data for training.
- swig
- compile toolchain
- python
- libspeexdsp-dev
sudo apt install libspeexdsp-dev sudo apt install swig python setup.py install
"""Acoustic Noise Suppression for wav files.""" import wave import sys from speexdsp_ns import NoiseSuppression if len(sys.argv) < 3: print('Usage: {} near.wav out.wav'.format(sys.argv[0])) sys.exit(1) frame_size = 256 near = wave.open(sys.argv[1], 'rb') if near.getnchannels() > 1: print('Only support mono channel') sys.exit(2) out = wave.open(sys.argv[2], 'wb') out.setnchannels(near.getnchannels()) out.setsampwidth(near.getsampwidth()) out.setframerate(near.getframerate()) print('near - rate: {}, channels: {}, length: {}'.format( near.getframerate(), near.getnchannels(), near.getnframes() / near.getframerate())) noise_suppression = NoiseSuppression.create(frame_size, near.getframerate()) in_data_len = frame_size in_data_bytes = frame_size * 2 while True: in_data = near.readframes(in_data_len) if len(in_data) != in_data_bytes: break in_data = noise_suppression.process(in_data) out.writeframes(in_data) near.close() out.close()
or
python examples/main.py in.wav out.wav
Noise suppression as show in figure below:
