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        "author": "Sean Robertson",
        "author_email": "sdrobert@cs.toronto.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6"
        ],
        "description": "[![Build Status](https://travis-ci.com/sdrobert/pydrobert-speech.svg?branch=master)](https://travis-ci.com/sdrobert/pydrobert-speech)\n[![Documentation Status](https://readthedocs.org/projects/pydrobert-speech/badge/?version=latest)](https://pydrobert-speech.readthedocs.io/en/latest/?badge=latest)\n[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)\n\n# pydrobert-speech\n\nThis pure-python library allows for flexible computation of speech features.\n\nFor example, given feature configuration called `fbanks.json`:\n\n``` json\n{\n  \"name\": \"stft\",\n  \"bank\": \"fbank\",\n  \"frame_length_ms\": 25,\n  \"include_energy\": true,\n  \"pad_to_nearest_power_of_two\": true,\n  \"window_function\": \"hanning\",\n  \"use_power\": true\n}\n```\n\nYou can compute triangular, overlapping filters like\n[Kaldi](http://kaldi-asr.org/) or [HTK](http://htk.eng.cam.ac.uk/) with the\ncommands\n\n``` python\nimport json\nfrom pydrobert.speech import *\n# get the feature computer ready\nparams = json.load(open('fbank.json'))\ncomputer = util.alias_factory_subclass_from_arg(compute.FrameComputer, params)\n# assume \"signal\" is a numpy float array\nfeats = computer.compute_full(signal)\n```\n\nIf you plan on using a [PyTorch](https://pytorch.org) `DataLoader` or Kaldi\ntables in your ASR pipeline, you can compute all a corpus' features by\nusing the commmands `signals-to-torch-feat-dir` (requires *pytorch* package)\nor `compute-feats-from-kaldi-tables` (requires *pydrobert-kaldi* package).\n\nThis package can compute much more than f-banks, with many different\npermutations. Consult the documentation for a more in-depth discussion of how\nto use it.\n\n## Documentation\n\n- [Latest](https://pydrobert-speech.readthedocs.io/en/latest/)\n- [Stable](https://pydrobert-speech.readthedocs.io/en/stable/)\n\n# Installation\n\n_pydrobert-speech_ is available via both PyPI and Conda.\n\n``` sh\nconda install -c sdrobert pydrobert-speech\npip install pydrobert-speech\npip install git+https://github.com/sdrobert/pydrobert-speech # bleeding edge\n```\n\n\n",
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