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    "info": {
        "author": "Raymond Xia",
        "author_email": "raymondxia@cmu.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Multimedia :: Sound/Audio :: Analysis"
        ],
        "description": "# pyaudlib\n\n[![Build Status](https://travis-ci.com/raymondxyy/pyaudlib.svg?token=xNuzdfgseSXz1yHDnh9L&branch=master)](https://travis-ci.org/raymondxyy/pyaudlib)\n\n> A speech signal processing library in Python with emphasis on deep learning.\n\npyaudlib (name subject to change) provides a collection of utilities for developing speech-related applications using both signal processing and deep learning. The package offers the following high-level features:\n\n- Speech signal processing utilities with ready-to-use applications\n- Deep learning architectures for speech processing tasks in [PyTorch][pytorch]\n- PyTorch-compatible interface (similar to torchvision) for batch processing\n- I/O utilities for interfacing with [CMUSPHINX][sphinx]\n- A command-line interface with a unix-pipe-like syntax\n\nSome use cases of pyaudlib are:\n\n- Extracting common speech features for your backend\n- Integrating CMUSPHINX with modern deep learning architectures\n- Developing your own deep-learning-based tools for speech tasks\n- Quickly try out speech enhancers and visualize the spectrogram in command line\n\npyaudlib focuses on correctness, efficiency, and simplicity. Signal processing functionalities are mathematically checked whenever possible (e.g. constant overlap-add, `istft(stft(X))==X`), and benchmarked in comparison to popular speech or audio processing toolbox (e.g. librosa). Deep neural networks and the training pipeline are consistent with that of PyTorch and torchvision.\n\n## Installation\nUnder the source directory, run:\n```sh\npip install .\n```\n\n## Test\nInstall test dependencies:\n```sh\npip install \".[test]\"\n```\nRun test:\n```sh\npython -m pytest tests/sig\n```\n\n## Usage example\n\n- Command-line feature extraction and visualization\n```sh\naudiopipe open -i samples/welcome16k.wav read logspec plot\n```\n![](https://filedn.com/lx3TheNX5ifLtAEMJg2YxFh/sn/pyaudlib/welcome-logspec.png)\n\nMore extensive examples can be found in `egs`.\n\n\n## Development setup\n\nSee `doc/0-getting-started.md`.\n\n## Release history\n\n- 0.0.1\n    - Work in progress\n\n## Authors\n\nRaymond Xia - yangyanx@andrew.cmu.edu\n\nMahmoud Alismail - mahmoudi@andrew.cmu.edu\n\nShangwu Yao - shangwuyao@gmail.com\n\nFeel free to send us any issue you find and question you have.\n\n## Contributing\n\nPlease contact one of the authors.\n\n## License\nDistributed under the GNU GPLv3 license. See ``LICENSE`` for more information.\n\n[pytorch]: https://pytorch.org/\n[sphinx]: https://cmusphinx.github.io/\n\n\n",
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        "keywords": "SPEECH,AUDIO,SIGNAL,SOUND",
        "license": "MIT",
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