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    "info": {
        "author": "Brian Margolis",
        "author_email": "BrianMargolis2019@u.northwestern.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
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        "description": "AudAugio\n===========\n\nAudAugio (pronounced like \u201cadagio\u201d) is a Python library for augmenting\naudio for machine learning. It includes a built-in set of common\naugmentations as well as the ability to easily define new ones, as well\nas a framework for applying and layering those augmentations.\n\nInstallation\n------------\n\nInstall AudAugio with pip:\n\n::\n\n   pip install audaugio\n\nIf you want to use one of the built-in augmentations that relies on SoX,\nyou\u2019ll need to install it separately. You can do that at `the SoX\nSourceForge page`_. Augmentations that rely on SoX note that in their\ndocumentation, and are listed here:\n\n- Equalizer Augmentation\n- Low Pass Augmentation\n- High Pass Augmentation\n\nUse\n---\n\nBelow is an example of how to use AudAugio to augment an audio file:\n\n.. code:: python\n\n    import librosa\n\n    import audaugio\n\n    y, sr = librosa.load('sample.wav')\n\n    chain = audaugio.CombinatoricChain(audaugio.PitchShiftAugmentation(1),\n                                       audaugio.BackgroundNoiseAugmentation(.005),\n                                       audaugio.EqualizerAugmentation(800, .15, -15))\n\n    augmented_audio = chain(y, sr)\n\n    for i, a in enumerate(augmented_audio):\n        librosa.output.write_wav(\"output/{0}.wav\".format(i), a, sr)\n\nFor more, read `the documentation for AudAugio`_.\n\n\nAugmentation Chains\n-------------------\n\nAugmentations are applied through augmentation chains. There are two\nkinds of chains - combinatoric chains and linear chains.\nCombinatorically applying the augmentations creates a modified and\nunmodified version of the signal for each augmentation, which are then\neach augmented further by the remaining augmentations in the chain. For\nexample, if a single signal is combinatorically augmented with both a\npitch shift augmentation and a background noise augmentation, there will\nbe four resulting augmented signals:\n\n- The dry signal\n- The pitch shifted signal\n- The signal with background noise added\n- The pitch shifted signal with background noise added\n\nLinear chains do not retain the unmodified signal after processing. If\nsame chain as above was applied, the only signal returned would be the\npitch shifted signal with background noise added.\n\nContact\n-------\n\nContact Brian Margolis (brianmargolis [at] u.northwestern.edu) with any\nquestions or issues. Please look at the \u201cissues\u201d page before reporting\nproblems.\n\n.. _the SoX SourceForge page: https://sourceforge.net/projects/sox/files/sox/\n.. _the documentation for AudAugio: https://audaugio.readthedocs.io/\n\n",
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        "name": "AudAugio",
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