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:
torchaudio also supports loading sound files in the wav and mp3
format. We call waveform the resulting raw audio signal.
library(torchaudio) url = "https://pytorch.org/tutorials/_static/img/steam-train-whistle-daniel_simon-converted-from-mp3.wav" 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[] sample_rate = waveform_and_sample_rate[] paste("Shape of waveform: ", paste(dim(waveform), collapse = " ")) #>  "Shape of waveform: 2 276858" paste("Sample rate of waveform: ", sample_rate) #>  "Sample rate of waveform: 44100" plot(waveform, col = "royalblue", type = "l") lines(waveform, col = "orange")
specgram <- transform_spectrogram()(waveform) paste("Shape of spectrogram: ", paste(dim(specgram), collapse = " ")) #>  "Shape of spectrogram: 2 201 1385" specgram_as_array <- as.array(specgram$log2()$t()) image(specgram_as_array[,ncol(specgram_as_array):1], col = viridis::viridis(n = 257, option = "magma"))
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.
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