TorchAudio Ruby

🔥 An audio library for Torch.rb

First, install SoX. For Homebrew, use:

brew install sox

Add this line to your application’s Gemfile:

gem "torchaudio"

This library follows the Python API. Many methods and options are missing at the moment. PRs welcome!

• PyTorch tutorial
• Ruby code

Download the audio file and install the matplotlib gem first.

Load a file

waveform, sample_rate = TorchAudio.load("file.wav")

Save a file

TorchAudio.save("new.wave", waveform, sample_rate)

TorchAudio::Transforms::Spectrogram.new.call(waveform)

Supported transforms are:

• AmplitudeToDB
• ComputeDeltas
• Fade
• MelScale
• MelSpectrogram
• MFCC
• MuLawDecoding
• MuLawEncoding
• Spectrogram
• Vol

TorchAudio::Functional.lowpass_biquad(waveform, sample_rate, cutoff_freq)

Supported functions are:

• amplitude_to_DB
• compute_deltas
• create_dct
• create_fb_matrix
• DB_to_amplitude
• dither
• gain
• highpass_biquad
• lowpass_biquad
• mu_law_decoding
• mu_law_encoding
• spectrogram

Load a dataset

TorchAudio::Datasets::YESNO.new(".", download: true)

Supported datasets are:

• YESNO

This library downloads and prepares public datasets. We don’t host any datasets. Be sure to adhere to the license for each dataset.

If you’re a dataset owner and wish to update any details or remove it from this project, let us know.

brew install sox

todo

sudo apt install sox libsox-dev libsox-fmt-all

Add to .travis.yml:

addons:
  apt:
    packages:
      - sox
      - libsox-dev
      - libsox-fmt-all

View the changelog

Everyone is encouraged to help improve this project. Here are a few ways you can help:

• Report bugs
• Fix bugs and submit pull requests
• Write, clarify, or fix documentation
• Suggest or add new features

To get started with development:

git clone https://github.com/ankane/torchaudio-ruby.git
cd torchaudio-ruby
bundle install
bundle exec rake compile
bundle exec rake test