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        "author": "Bea Steers",
        "author_email": "bea.steers@gmail.com",
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            "Development Status :: 3 - Alpha",
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            "License :: OSI Approved :: ISC License (ISCL)",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development"
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        "description": "# VGGish: A VGG-like audio classification model \n\nThis repository provides a VGGish model, implemented in Keras with tensorflow backend (since `tf.slim` is [deprecated](https://github.com/tensorflow/tensorflow/issues/16182#issuecomment-372397483), I think we should have an up-to-date interface). This repository is developed \nbased on the model for [AudioSet](https://research.google.com/audioset/index.html). \nFor more details, please visit the [slim version](https://github.com/tensorflow/models/tree/master/research/audioset).\n\n\n\n## Install\n\n```bash\npip install vggish-keras\n```\nWeights will be automatically downloaded when installing via pip. \n\nCurrently - this relies on a pending change to `pumpp` in https://github.com/bmcfee/pumpp/pull/123. To get those changes, you need \n\n```bash\npip install git+https://github.com/beasteers/pumpp@tf_keras\n```\n\n## Usage\n```python\nimport librosa\nimport numpy as np\nimport vggish_keras as vgk\n\n# define the model\npump = vgk.get_pump()\nmodel = vgk.VGGish(pump)\n\n# transform audio into VGGish embeddings without fc layers\nX = pump.transform(librosa.util.example_audio_file())[vgk.params.PUMP_INPUT]\nX = np.concatenate([X]*5)\nZ = model.predict(X)\n\n# calculate timestamps\nop = pump['mel']\nts = np.arange(len(Z)) / op.sr * op.hop_length\nassert Z.shape == (5, 512)\n```\n\n## Reference:\n\n* Gemmeke, J. et. al.,\n  [AudioSet: An ontology and human-labelled dataset for audio events](https://research.google.com/pubs/pub45857.html),\n  ICASSP 2017\n\n* Hershey, S. et. al.,\n  [CNN Architectures for Large-Scale Audio Classification](https://research.google.com/pubs/pub45611.html),\n  ICASSP 2017\n  \n* [Model](https://drive.google.com/open?id=1mhqXZ8CANgHyepum7N4yrjiyIg6qaMe6) with the top fully connected layers\n\n* [Model](https://drive.google.com/open?id=16JrWEedwaZFVZYvn1woPKCuWx85Ghzkp) without the top fully connected layers\n\n## TODO\n - add fully connected layers\n - add PCA postprocessing (needs fully connected layers and to add PCA params to model)\n - currently, parameters (sample rate, hop size, etc) can be changed globally via `vgk.params` - I'd like to allow for parameter overrides to be passed to `vgk.VGGish`",
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