Metadata-Version: 2.1
Name: LPCTorch
Version: 0.1.3
Summary: LPC Utility for Pytorch Library.
Home-page: https://github.com/TowardHumanizedInteraction/LPCTorch
Author: yliess
Author-email: hatiyliess86@gmail.com
License: MIT
Description: 
        LPC Utility for Pytorch Library.
        
        # LPC Torch
        
        LPCTorch 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]].
        
        ## Install
        
        Install the module using the pip utility ( may require to run as sudo )
        
        ```bash
        pip3 install lpctorch
        ```
        
        ## Usage
        
        ### LPC Coefficients
        
        ```python
        from lpctorch import LPCCoefficients
        
        # Parameters
        #     * sr            : sample rate of the signal ( 16 kHz )
        #     * frame_duration: duration of the window in seconds ( 16 ms )
        #     * frame_overlap : frame overlapping factor
        #     * K             : number of linear predictive coding coefficients
        sr             = 16000
        frame_duration = .016
        frame_overlap  = .5
        K              = 32
        
        # Initialize the module given all the parameters
        lpc_prep       = LPCCoefficients(
            sr,
            frame_duration,
            frame_overlap,
            order = ( K - 1 )
        )
        
        # Get the coefficients given a signal
        # torch.Tensor of size ( Batch, Samples )
        alphas         = lpc_prep( X )
        ```
        
        ## Example
        
        The repository provides an example application with a 'sample.wav' file.
        The output is the same as the one provided by librosa (bottom).
        
        ![ Ex ]( ./examples/comparison.png )
        
        ## Benchmarks
        
        Here are some benchmarks comparing cpu vs gpu inference times in seconds of the utility from 1 to 32 batch size.
        
        ![ Bench ]( ./examples/benchmark.png )
        
        ## References
        
        * [[1]] Larry Marple A New Autoregressive Spectrum Analysis Algorithm IEEE Transactions on Accoustics, Speech, and Signal Processing vol 28, no. 4, 1980
        * [[2]] Librosa LPC Burg's Method Implementation
        
        [1]: https://ieeexplore.ieee.org/document/1163429
        [2]: https://librosa.github.io/librosa/_modules/librosa/core/audio.html#lpc
        
Platform: UNKNOWN
Requires-Python: >=3.4, <4
Description-Content-Type: text/markdown
