Lifecycle: experimental R build status CRAN status

torchaudio is an extension for torch providing audio loading, transformations, common architectures for signal processing, pre-trained weights and access to commonly used datasets. An almost literal translation from PyTorch’s Torchaudio library to R.


The CRAN release can be installed with:


You can install the development version from GitHub with:


A Waveform

torchaudio also supports loading sound files in the wav and mp3 format. We call waveform the resulting raw audio signal.


url = ""
filename = tempfile(fileext = ".wav")
r = httr::GET(url, httr::write_disk(filename, overwrite = TRUE))

waveform_and_sample_rate = transform_to_tensor(tuneR_loader(filename))
waveform = waveform_and_sample_rate[[1]]
sample_rate = waveform_and_sample_rate[[2]]

paste("Shape of waveform: ", paste(dim(waveform), collapse = " "))
#> [1] "Shape of waveform:  2 276858"
paste("Sample rate of waveform: ", sample_rate)
#> [1] "Sample rate of waveform:  44100"

plot(waveform[1], col = "royalblue", type = "l")
lines(waveform[2], col = "orange")

A Spectrogram

specgram <- transform_spectrogram()(waveform)

paste("Shape of spectrogram: ", paste(dim(specgram), collapse = " "))
#> [1] "Shape of spectrogram:  2 201 1385"

specgram_as_array <- as.array(specgram$log2()[1]$t())
image(specgram_as_array[,ncol(specgram_as_array):1], col = viridis::viridis(n = 257,  option = "magma"))

Datasets (go to issue)

Models (go to issue)

I/O Backend

Code of Conduct

Please note that the torchaudio project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.