## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE#identical(Sys.getenv("TORCH_TEST", unset = "0"), "1")
)

## ----setup, message=FALSE, warning=FALSE--------------------------------------
#  library(torchaudio)
#  library(viridis)

## ---- fig.height=4, fig.width=6-----------------------------------------------
#  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[[1]]
#  sample_rate = waveform_and_sample_rate[[2]]
#  
#  paste("Shape of waveform: ", paste(dim(waveform), collapse = " "))
#  paste("Sample rate of waveform: ", sample_rate)
#  
#  plot(waveform[1], col = "royalblue", type = "l")
#  lines(waveform[2], col = "orange")

## ---- fig.height=3, fig.width=6-----------------------------------------------
#  specgram <- transform_spectrogram()(waveform)
#  
#  paste("Shape of spectrogram: ", paste(dim(specgram), collapse = " "))
#  
#  specgram_as_array <- as.array(specgram$log2()[1]$t())
#  image(specgram_as_array[,ncol(specgram_as_array):1], col = viridis(n = 257,  option = "magma"))

## ---- fig.height=3, fig.width=6-----------------------------------------------
#  specgram <- transform_mel_spectrogram()(waveform)
#  
#  paste("Shape of spectrogram: ", paste(dim(specgram), collapse = " "))
#  
#  specgram_as_array <- as.array(specgram$log2()[1]$t())
#  image(specgram_as_array[,ncol(specgram_as_array):1], col = viridis(n = 257,  option = "magma"))

## ---- fig.height=4, fig.width=6-----------------------------------------------
#  new_sample_rate <- sample_rate/10
#  
#  # Since Resample applies to a single channel, we resample first channel here
#  channel <- 1
#  transformed <- transform_resample(sample_rate, new_sample_rate)(waveform[channel, ]$view(c(1,-1)))
#  
#  paste("Shape of transformed waveform: ", paste(dim(transformed), collapse = " "))
#  
#  plot(transformed[1], col = "royalblue", type = "l")

## -----------------------------------------------------------------------------
#  # Let's check if the tensor is in the interval [-1,1]
#  cat(sprintf("Min of waveform: %f \nMax of waveform: %f \nMean of waveform: %f", as.numeric(waveform$min()), as.numeric(waveform$max()), as.numeric(waveform$mean())))

## -----------------------------------------------------------------------------
#  normalize <- function(tensor) {
#   # Subtract the mean, and scale to the interval [-1,1]
#   tensor_minusmean <- tensor - tensor.mean()
#   return(tensor_minusmean/tensor_minusmean$abs()$max())
#  }
#  
#  # Let's normalize to the full interval [-1,1]
#  # waveform = normalize(waveform)

## ---- fig.height=4, fig.width=6-----------------------------------------------
#  transformed <- transform_mu_law_encoding()(waveform)
#  
#  paste("Shape of transformed waveform: ", paste(dim(transformed), collapse = " "))
#  
#  plot(transformed[1], col = "royalblue", type = "l")

## ---- fig.height=4, fig.width=6-----------------------------------------------
#  reconstructed <- transform_mu_law_decoding()(transformed)
#  
#  paste("Shape of recovered waveform: ", paste(dim(reconstructed), collapse = " "))
#  
#  plot(reconstructed[1], col = "royalblue", type = "l")

## -----------------------------------------------------------------------------
#  # Compute median relative difference
#  err <- as.numeric(((waveform - reconstructed)$abs() / waveform$abs())$median())
#  
#  paste("Median relative difference between original and MuLaw reconstucted signals:", scales::percent(err, accuracy = 0.01))

## ---- fig.height=4, fig.width=6-----------------------------------------------
#  mu_law_encoding_waveform <- functional_mu_law_encoding(waveform, quantization_channels = 256)
#  
#  paste("Shape of transformed waveform: ", paste(dim(mu_law_encoding_waveform), collapse = " "))
#  
#  plot(mu_law_encoding_waveform[1], col = "royalblue", type = "l")

## ---- fig.height=3, fig.width=6-----------------------------------------------
#  computed <- functional_compute_deltas(specgram$contiguous(), win_length=3)
#  
#  paste("Shape of computed deltas: ", paste(dim(computed), collapse = " "))
#  
#  computed_as_array <- as.array(computed[1]$t())
#  image(computed_as_array[,ncol(computed_as_array):1], col = viridis(n = 257,  option = "magma"))

## -----------------------------------------------------------------------------
#  gain_waveform <- as.numeric(functional_gain(waveform, gain_db=5.0))
#  cat(sprintf("Min of gain_waveform: %f\nMax of gain_waveform: %f\nMean of gain_waveform: %f", min(gain_waveform), max(gain_waveform), mean(gain_waveform)))
#  
#  dither_waveform <- as.numeric(functional_dither(waveform))
#  cat(sprintf("Min of dither_waveform: %f\nMax of dither_waveform: %f\nMean of dither_waveform: %f", min(dither_waveform), max(dither_waveform), mean(dither_waveform)))

## ----lowpass, cache=FALSE-----------------------------------------------------
#  lowpass_waveform <- as.array(functional_lowpass_biquad(waveform, sample_rate, cutoff_freq=3000))
#  
#  cat(sprintf("Min of lowpass_waveform: %f\nMax of lowpass_waveform: %f\nMean of lowpass_waveform: %f", min(lowpass_waveform), max(lowpass_waveform), mean(lowpass_waveform)))
#  
#  plot(lowpass_waveform[1,], col = "royalblue", type = "l")
#  lines(lowpass_waveform[2,], col = "orange")

## ----highpass, cache=FALSE----------------------------------------------------
#  highpass_waveform <- as.array(functional_highpass_biquad(waveform, sample_rate, cutoff_freq=3000))
#  
#  cat(sprintf("Min of highpass_waveform: %f\nMax of highpass_waveform: %f\nMean of highpass_waveform: %f", min(highpass_waveform), max(highpass_waveform), mean(highpass_waveform)))
#  
#  plot(highpass_waveform[1,], col = "royalblue", type = "l")
#  lines(highpass_waveform[2,], col = "orange")

## ---- fig.height=4, fig.width=6-----------------------------------------------
#  temp <- tempdir()
#  yesno_data <- yesno_dataset(temp, download=TRUE)
#  
#  # A data point in Yesno is a list (waveform, sample_rate, labels) where labels is a list of integers with 1 for yes and 0 for no.
#  
#  # Pick data point number 3 to see an example of the the yesno_data:
#  n <- 3
#  sample <- yesno_data[n]
#  sample
#  
#  plot(sample[[1]][1], col = "royalblue", type = "l")

