{
    "info": {
        "author": "Prem Seetharaman",
        "author_email": "prem@u.northwestern.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Environment :: Plugins",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Programming Language :: C++",
            "Programming Language :: Python :: 3",
            "Topic :: Multimedia :: Sound/Audio",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": "[![Build Status](https://travis-ci.com/pseeth/pytorch-stft.svg?branch=master)](https://travis-ci.com/pseeth/pytorch-stft)\n\n# STFT/iSTFT in PyTorch\n\nAn STFT/iSTFT written up in PyTorch using 1D Convolutions. Requirements are a recent version PyTorch, numpy, and librosa (for loading audio in test_stft.py). Thanks to Shrikant Venkataramani for sharing code this was based off of and Rafael Valle for catching bugs and adding the proper windowing logic. Uses Python 3.\n\n## Installation\nInstall easily with pip:\n```\npip install torch_stft\n```\n\n## Usage\n```\nimport torch\nfrom torch_stft import STFT\nimport numpy as np\nimport librosa \nimport matplotlib.pyplot as plt\n\naudio = librosa.load(librosa.util.example_audio_file(), duration=10.0, offset=30)[0]\ndevice = 'cpu'\nfilter_length = 1024\nhop_length = 256\nwin_length = 1024 # doesn't need to be specified. if not specified, it's the same as filter_length\nwindow = 'hann'\n\naudio = torch.FloatTensor(audio)\naudio = audio.unsqueeze(0)\naudio = audio.to(device)\n\nstft = STFT(\n    filter_length=filter_length, \n    hop_length=hop_length, \n    win_length=win_length,\n    window=window\n).to(device)\n\nmagnitude, phase = stft.transform(audio)\noutput = stft.inverse(magnitude, phase)\noutput = output.cpu().data.numpy()[..., :]\naudio = audio.cpu().data.numpy()[..., :]\nprint(np.mean((output - audio) ** 2)) # on order of 1e-16\n```\n\nOutput of [`compare_stft.py`](compare_stft.py):\n\n![images/stft.png](images/stft.png)\n\n## Tests\nTest it by just cloning this repo and running\n\n```\npip install -r requirements.txt\npython -m pytest .\n```\n\nUnfortunately, since it's implemented with 1D Convolutions, some filter_length/hop_length \ncombinations can result in out of memory errors on your GPU when run on sufficiently large input.\n\n\n## Contributing\nPull requests welcome.\n\n\n\n",
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        "name": "torch-stft",
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        "summary": "An STFT/iSTFT for PyTorch",
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