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docs | 1 year ago | |
examples | 1 month ago | |
mindaudio | 3 months ago | |
tests | 10 months ago | |
tutorials | 10 months ago | |
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.pre-commit-config.yaml | 1 year ago | |
CONTRIBUTING.md | 1 year ago | |
LICENSE | 1 year ago | |
README.md | 10 months ago | |
package.sh | 1 year ago | |
requirements-dev.txt | 7 months ago | |
requirements.txt | 7 months ago | |
setup.py | 10 months ago |
MindAudio is a toolbox of audio models and algorithms based on MindSpore. It provides a series of API for common audio data processing,data enhancement,feature extraction, so that users can preprocess data conveniently. Also provides examples to show how to build audio deep learning models with mindaudio.
# read audio
>>> import mindaudio.data.io as io
>>> audio_data, sr = io.read(data_file)
# feature extraction
>>> import mindaudio.data.features as features
>>> feats = features.fbanks(audio_data)
The released version of MindAudio can be installed via PyPI
as follows:
pip install mindaudio
The latest version of MindAudio can be installed as follows:
git clone https://github.com/mindspore-lab/mindaudio.git
cd mindaudio
pip install -r requirements/requirements.txt
python setup.py install
mindaudio provides a series of commonly used audio data processing apis, which can be easily invoked for data analysis and feature extraction.
>>> import mindaudio.data.io as io
>>> import mindaudio.data.spectrum as spectrum
>>> import numpy as np
>>> import matplotlib.pyplot as plt
# read audio
>>> audio_data, sr = io.read("./tests/samples/ASR/BAC009S0002W0122.wav")
# feature extraction
>>> n_fft = 512
>>> matrix = spectrum.stft(audio_data, n_fft=n_fft)
>>> magnitude, _ = spectrum.magphase(matrix, 1)
# display
>>> x = [i for i in range(0, 256*750, 256)]
>>> f = [i/n_fft * sr for i in range(0, int(n_fft/2+1))]
>>> plt.pcolormesh(x,f,magnitude, shading='gouraud', vmin=0, vmax=np.percentile(magnitude, 98))
>>> plt.title('STFT Magnitude')
>>> plt.ylabel('Frequency [Hz]')
>>> plt.xlabel('Time [sec]')
>>> plt.show()
Result presentation:
We appreciate all contributions to improve MindSpore Audio. Please refer to CONTRIBUTING.md for the contributing guideline.
This project is released under the Apache License 2.0.
If you find this project useful in your research, please consider citing:
@misc{MindSpore Audio 2022,
title={{MindSpore Audio}:MindSpore Audio Toolbox and Benchmark},
author={MindSpore Audio Contributors},
howpublished = {\url{https://github.com/mindspore-lab/mindaudio}},
year={2022}
}
A toolbox of audio models and algorithms based on MindSpore
https://github.com/mindspore-lab/mindaudio
Jupyter Notebook Python other
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