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    "info": {
        "author": "Jordi Pons",
        "author_email": "",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: ISC License (ISCL)",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.7",
            "Topic :: Multimedia :: Sound/Audio :: Analysis"
        ],
        "description": "# musicnn\nPronounced as \"musician\", `musicnn` is a set of pre-trained musically motivated convolutional neural networks for music audio tagging. This repository also includes some pre-trained [vgg-like](https://github.com/jordipons/musicnn/blob/master/vgg_example.ipynb) baselines.\n\nCheck the [documentation](https://github.com/jordipons/musicnn/blob/master/DOCUMENTATION.md) and our [basic](https://github.com/jordipons/musicnn/blob/master/tagging_example.ipynb) / [advanced](https://github.com/jordipons/musicnn/blob/master/musicnn_example.ipynb) examples to understand how to use `musicnn`.\n\nDo you have questions? Check the [FAQs](https://github.com/jordipons/musicnn/blob/master/FAQs.md).\n\n## Installation\n```pip install musicnn```\n\nor, to get bigger models and all the documentation (including jupyter notebooks), install from source:\n\n``` git clone https://github.com/jordipons/musicnn.git```\n\n``` python setup.py install```\n\n## Predict tags\n\nFrom within **python**, you can estimate the topN tags:\n~~~~python\nfrom musicnn.tagger import top_tags\ntop_tags('./audio/joram-moments_of_clarity-08-solipsism-59-88.mp3', model='MTT_musicnn', topN=10)\n~~~~\n>['techno', 'electronic', 'synth', 'fast', 'beat', 'drums', 'no vocals', 'no vocal', 'dance', 'ambient']\n\nLet's try another song!\n\n~~~~python\ntop_tags('./audio/TRWJAZW128F42760DD_test.mp3')\n~~~~\n>['guitar', 'piano', 'fast']\n\nFrom the **command-line**, you can also print the topN tags on the screen:\n\n~~~~\npython -m musicnn.tagger file_name.ogg --print\npython -m musicnn.tagger file_name.au --model 'MSD_musicnn' --topN 3 --length 3 --overlap 1.5 --print\n~~~~~\n\nor save to a file:\n\n~~~~\npython -m musicnn.tagger file_name.wav --save out.tags\npython -m musicnn.tagger file_name.mp3 --model 'MTT_musicnn' --topN 10 --length 3 --overlap 1 --print --save out.tags\n~~~~\n\n## Extract the Taggram\n\nYou can also compute the taggram using **python** (see our [basic](https://github.com/jordipons/musicnn/blob/master/tagging_example.ipynb) example for more details on how to depict it):\n\n~~~~python\nfrom musicnn.extractor import extractor\ntaggram, tags = extractor('./audio/joram-moments_of_clarity-08-solipsism-59-88.mp3', model='MTT_musicnn')\n~~~~\n![Taggram](./images/taggram.png \"Taggram\")\n\nThe above analyzed music clips are included in the `./audio/` folder of this repository. \n\nYou can listen to those and evaluate `musicnn` yourself!\n\n\n",
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        "home_page": "http://github.com/jordipons/musicnn",
        "keywords": "audio music deep learning tagging tensorflow machine listening",
        "license": "ISC",
        "maintainer": "",
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        "name": "musicnn",
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        "summary": "Pronounced as \"musician\", musicnn is a set of pre-trained deep convolutional neural networks for music audio tagging",
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