/lua---audio

Module for torch to support audio i/o as well as do common operations like dFFT, generate spectrograms etc.

Primary LanguageCOtherNOASSERTION

Audio Library for Torch

Audio library for Torch-7

  • Support audio I/O (Load files)
  • Common audio operations (Short-time Fourier transforms, Spectrograms)

Load the following formats into a torch Tensor

  • mp3, wav, aac, ogg, flac, avr, cdda, cvs/vms,
  • aiff, au, amr, mp2, mp4, ac3, avi, wmv,
  • mpeg, ircam and any other format supported by libsox.

Calculate Short-time Fourier transforms with

  • window types - rectangular, hamming, hann, bartlett

Generate spectrograms

Dependencies

  • libsox v14.3.2 or above
  • libfftw3

Quick install on OSX (Homebrew):

$ brew install sox
$ brew install fftw

Linux (Ubuntu):

$ sudo apt-get install libfftw3-dev
$ sudo apt-get install sox libsox-dev libsox-fmt-all

Installation

This project can be installed with luarocks like this:

$ luarocks install https://raw.githubusercontent.com/soumith/lua---audio/master/audio-0.1-0.rockspec

On Ubuntu 13.04 64-bit, I had to modify the command slightly because of new library directory structures not picked up by luarocks.

$ sudo luarocks install https://raw.githubusercontent.com/soumith/lua---audio/master/audio-0.1-0.rockspec LIBSOX_LIBDIR=/usr/lib/x86_64-linux-gnu/ LIBFFTW3_LIBDIR=/usr/lib/x86_64-linux-gnu

Or, if you have downloaded this repository on your machine, and you are in its directory:

$ luarocks make

Usage

audio.load

 loads an audio file into a torch.Tensor
 usage:  
 audio.load(  
     string                              -- path to file  
 )

audio.stft

calculate the stft of an audio. returns a 3D tensor, with number_of_windows x window_size/2+1 x 2(complex number with real and complex parts)
usage:
audio.stft(
    torch.Tensor                        -- input single-channel audio
    number                              -- window size
    string                              -- window type: rect, hamming, hann, bartlett
    number                              -- stride
)

audio.spectrogram

generate the spectrogram of an audio. returns a 2D tensor, with number_of_windows x window_size/2+1, each value representing the magnitude of each frequency in dB
usage:
audio.spectrogram(
    torch.Tensor                        -- input single-channel audio
    number                              -- window size
    string                              -- window type: rect, hamming, hann, bartlett
    number                              -- stride
)

Example Usage

Generate a spectrogram

require 'audio'
require 'image' -- to display the spectrogram
voice = audio.samplevoice()
spect = audio.spectrogram(voice, 8192, 'hann', 512)
image.display(spect)