Python bindings of speexdsp noise suppression library
===================
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](https://arxiv.org/abs/2106.04129).
> Use a VAD and lightweight denoiser (SpeexDSP1) to eliminate the stationary noise before using this data for training.
## Requirements
+ swig
+ compile toolchain
+ python
+ libspeexdsp-dev
## Build
```shell
sudo apt install libspeexdsp-dev
sudo apt install swig
python setup.py install
```
## Get started
```python
"""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:
Raw data
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"description": "Python bindings of speexdsp noise suppression library\n===================\n\nModified from https://github.com/xiongyihui/speexdsp-python\n\nYou can use it in Noise reduction model training as said in [Personalized PercepNet: Real-time, Low-complexity Target Voice Separation and Enhancement](https://arxiv.org/abs/2106.04129).\n> Use a VAD and lightweight denoiser (SpeexDSP1) to eliminate the stationary noise before using this data for training.\n\n## Requirements\n+ swig\n+ compile toolchain\n+ python\n+ libspeexdsp-dev\n\n## Build\n```shell\nsudo apt install libspeexdsp-dev\nsudo apt install swig\npython setup.py install\n```\n\n## Get started\n\n```python\n\"\"\"Acoustic Noise Suppression for wav files.\"\"\"\n\nimport wave\nimport sys\nfrom speexdsp_ns import NoiseSuppression\n\n\nif len(sys.argv) < 3:\n print('Usage: {} near.wav out.wav'.format(sys.argv[0]))\n sys.exit(1)\n\n\nframe_size = 256\n\nnear = wave.open(sys.argv[1], 'rb')\n\nif near.getnchannels() > 1:\n print('Only support mono channel')\n sys.exit(2)\n\nout = wave.open(sys.argv[2], 'wb')\nout.setnchannels(near.getnchannels())\nout.setsampwidth(near.getsampwidth())\nout.setframerate(near.getframerate())\n\n\nprint('near - rate: {}, channels: {}, length: {}'.format(\n near.getframerate(),\n near.getnchannels(),\n near.getnframes() / near.getframerate()))\n\nnoise_suppression = NoiseSuppression.create(frame_size, near.getframerate())\n\nin_data_len = frame_size\nin_data_bytes = frame_size * 2\n\nwhile True:\n in_data = near.readframes(in_data_len)\n if len(in_data) != in_data_bytes:\n break\n\n in_data = noise_suppression.process(in_data)\n\n out.writeframes(in_data)\n\nnear.close()\nout.close()\n```\n\nor\n\n```python examples/main.py in.wav out.wav```\n\nNoise suppression as show in figure below:\n\n\n",
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