<div align="center">
# MindAudio
[
](<img alt="GitHub" src="https://img.shields.io/github/license/mindspore-lab/mindaudio">)
[](https://github.com/psf/black)
[](https://pycqa.github.io/isort/)
[Introduction](#introduction) |
[Installation](#installation) |
[Get Started](#get-started)
</div>
## Introduction
MindAudio is a toolbox of audio models and algorithms based on [MindSpore](https://www.mindspore.cn/). 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.
### data processing
```python
# 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)
```
## Installation
### Install with PyPI
The released version of MindAudio can be installed via `PyPI` as follows:
```shell
pip install mindaudio
```
### Install from Source
The latest version of MindAudio can be installed as follows:
```shell
git clone https://github.com/mindspore-lab/mindaudio.git
cd mindaudio
pip install -r requirements/requirements.txt
python setup.py install
```
## Get started with audio data analysis
###
mindaudio provides a series of commonly used audio data processing apis, which can be easily invoked for data analysis and feature extraction.
```python
>>> 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:
## What's New
- 2023/06/24: version 0.1.1, bug fix and readme update
- 2023/03/30: version 0.1.0, including 50+ data processing APIs, 5 models supported.
- 2022/09/30: beta, 33 data APIs + 3 models
## Contributing
We appreciate all contributions to improve MindSpore Audio. Please refer to [CONTRIBUTING.md](CONTRIBUTING.md) for the contributing guideline.
## License
This project is released under the [Apache License 2.0](LICENSE).
## Citation
If you find this project useful in your research, please consider citing:
```latex
@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}
}
```
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"description": "<div align=\"center\">\n\n\n# MindAudio\n\n[\n\n\n](<img alt=\"GitHub\" src=\"https://img.shields.io/github/license/mindspore-lab/mindaudio\">)\n[](https://github.com/psf/black)\n[](https://pycqa.github.io/isort/)\n\n[Introduction](#introduction) |\n[Installation](#installation) |\n[Get Started](#get-started)\n\n</div>\n\n## Introduction\n\nMindAudio is a toolbox of audio models and algorithms based on [MindSpore](https://www.mindspore.cn/). 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.\n\n### data processing\n\n\n```python\n# read audio\n>>> import mindaudio.data.io as io\n>>> audio_data, sr = io.read(data_file)\n# feature extraction\n>>> import mindaudio.data.features as features\n>>> feats = features.fbanks(audio_data)\n```\n\n## Installation\n\n### Install with PyPI\n\nThe released version of MindAudio can be installed via `PyPI` as follows:\n\n```shell\npip install mindaudio\n```\n\n### Install from Source\n\nThe latest version of MindAudio can be installed as follows:\n\n```shell\ngit clone https://github.com/mindspore-lab/mindaudio.git\ncd mindaudio\npip install -r requirements/requirements.txt\npython setup.py install\n```\n\n## Get started with audio data analysis\n\n###\n\nmindaudio provides a series of commonly used audio data processing apis, which can be easily invoked for data analysis and feature extraction.\n\n```python\n>>> import mindaudio.data.io as io\n>>> import mindaudio.data.spectrum as spectrum\n>>> import numpy as np\n>>> import matplotlib.pyplot as plt\n# read audio\n>>> audio_data, sr = io.read(\"./tests/samples/ASR/BAC009S0002W0122.wav\")\n# feature extraction\n>>> n_fft = 512\n>>> matrix = spectrum.stft(audio_data, n_fft=n_fft)\n>>> magnitude, _ = spectrum.magphase(matrix, 1)\n# display\n>>> x = [i for i in range(0, 256*750, 256)]\n>>> f = [i/n_fft * sr for i in range(0, int(n_fft/2+1))]\n>>> plt.pcolormesh(x,f,magnitude, shading='gouraud', vmin=0, vmax=np.percentile(magnitude, 98))\n>>> plt.title('STFT Magnitude')\n>>> plt.ylabel('Frequency [Hz]')\n>>> plt.xlabel('Time [sec]')\n>>> plt.show()\n```\n\nResult presentation:\n\n\n\n## What's New\n- 2023/06/24: version 0.1.1, bug fix and readme update\n- 2023/03/30: version 0.1.0, including 50+ data processing APIs, 5 models supported.\n- 2022/09/30: beta, 33 data APIs + 3 models\n\n## Contributing\n\nWe appreciate all contributions to improve MindSpore Audio. Please refer to [CONTRIBUTING.md](CONTRIBUTING.md) for the contributing guideline.\n\n## License\n\nThis project is released under the [Apache License 2.0](LICENSE).\n\n## Citation\n\nIf you find this project useful in your research, please consider citing:\n\n```latex\n@misc{MindSpore Audio 2022,\n title={{MindSpore Audio}:MindSpore Audio Toolbox and Benchmark},\n author={MindSpore Audio Contributors},\n howpublished = {\\url{https://github.com/mindspore-lab/mindaudio}},\n year={2022}\n}\n```\n",
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