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        "description": "\nLPC Utility for Pytorch Library.\n\n# LPC Torch\n\nLPCTorch is a small pytorch utility for Linear Predictive Coding. It provides a simple way to compute windowed Linear Predictive Coding Coefficients on a input audio signal. The repo uses the Burg's methods [[1]] and is heavily inspired from the librosa audio library implementation [[2]].\n\n## Install\n\nInstall the module using the pip utility ( may require to run as sudo )\n\n```bash\npip3 install lpctorch\n```\n\n## Usage\n\n### LPC Coefficients\n\n```python\nfrom lpctorch import LPCCoefficients\n\n# Parameters\n#     * sr            : sample rate of the signal ( 16 kHz )\n#     * frame_duration: duration of the window in seconds ( 16 ms )\n#     * frame_overlap : frame overlapping factor\n#     * K             : number of linear predictive coding coefficients\nsr             = 16000\nframe_duration = .016\nframe_overlap  = .5\nK              = 32\n\n# Initialize the module given all the parameters\nlpc_prep       = LPCCoefficients(\n    sr,\n    frame_duration,\n    frame_overlap,\n    order = ( K - 1 )\n)\n\n# Get the coefficients given a signal\n# torch.Tensor of size ( Batch, Samples )\nalphas         = lpc_prep( X )\n```\n\n## Example\n\nThe repository provides an example application with a 'sample.wav' file.\nThe output is the same as the one provided by librosa (bottom).\n\n![ Ex ]( ./examples/comparison.png )\n\n## Benchmarks\n\nHere are some benchmarks comparing cpu vs gpu inference times in seconds of the utility from 1 to 32 batch size.\n\n![ Bench ]( ./examples/benchmark.png )\n\n## References\n\n* [[1]] Larry Marple A New Autoregressive Spectrum Analysis Algorithm IEEE Transactions on Accoustics, Speech, and Signal Processing vol 28, no. 4, 1980\n* [[2]] Librosa LPC Burg's Method Implementation\n\n[1]: https://ieeexplore.ieee.org/document/1163429\n[2]: https://librosa.github.io/librosa/_modules/librosa/core/audio.html#lpc\n\n\n",
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