Simple audio classification with torch

This article translates Daniel Falbel’s ‘Simple Audio Classification’ article from tensorflow/keras to torch/torchaudio. The main goal is to introduce torchaudio and illustrate its contributions to the torch ecosystem. Here, we focus on a popular dataset, the audio loader and the spectrogram transformer. An interesting side product is the parallel between torch and tensorflow, showing sometimes the differences, sometimes the similarities between them.

Downloading and Importing

torchaudio has the speechcommand_dataset built in. It filters out background_noise by default and lets us choose between versions v0.01 and v0.02.

# set an existing folder here to cache the dataset
DATASETS_PATH  "~/datasets/"

# 1.4GB download
df  speechcommand_dataset(
  root = DATASETS_PATH, 
  url = "speech_commands_v0.01",
  download = TRUE
)

# expect folder: _background_noise_
df$EXCEPT_FOLDER
# [1] "_background_noise_"

# number of audio files
length(df)
# [1] 64721

# a sample
sample  df[1]

sample$waveform[, 1:10]

torch_tensor
0.0001 *
 0.9155  0.3052  1.8311  1.8311 -0.3052  0.3052  2.4414  0.9155 -0.9155 -0.6104
[ CPUFloatType{1,10} ]

sample$sample_rate
# 16000
sample$label
# bed

plot(sample$waveform[1], type = "l", col = "royalblue", main = sample$label)

Figure 1: A sample waveform for a ‘bed’.

Classes

 [1] "bed"    "bird"   "cat"    "dog"    "down"   "eight"  "five"  
 [8] "four"   "go"     "happy"  "house"  "left"   "marvin" "nine"  
[15] "no"     "off"    "on"     "one"    "right"  "seven"  "sheila"
[22] "six"    "stop"   "three"  "tree"   "two"    "up"     "wow"   
[29] "yes"    "zero"  

Generator Dataloader

torch::dataloader has the same task as data_generator defined in the original article. It is responsible for preparing batches – including shuffling, padding, one-hot encoding, etc. – and for taking care of parallelism / device I/O orchestration.

In torch we do this by passing the train/test subset to torch::dataloader and encapsulating all the batch setup logic inside a collate_fn() function.

At this point, dataloader(train_subset) would not work because the samples are not padded. So we need to build our own collate_fn() with the padding strategy.

I suggest using the following approach when implementing the collate_fn():

1. begin with collate_fn .
2. instantiate dataloader with the collate_fn()
3. create an environment by calling enumerate(dataloader) so you can ask to retrieve a batch from dataloader.
4. run environment[[1]][[1]]. Now you should be sent inside collate_fn() with access to batch input object.
5. build the logic.

collate_fn  function(batch) {
  browser()
}

ds_train  dataloader(
  train_subset, 
  batch_size = 32, 
  shuffle = TRUE, 
  collate_fn = collate_fn
)

ds_train_env  enumerate(ds_train)
ds_train_env[[1]][[1]]

The final collate_fn() pads the waveform to length 16001 and then stacks everything up together. At this point there are no spectrograms yet. We going to make spectrogram transformation a part of model architecture.

pad_sequence  function(batch) {
    # Make all tensors in a batch the same length by padding with zeros
    batch  sapply(batch, function(x) (x$t()))
    batch  torch::nn_utils_rnn_pad_sequence(batch, batch_first = TRUE, padding_value = 0.)
    return(batch$permute(c(1, 3, 2)))
  }

# Final collate_fn
collate_fn  function(batch) {
 # Input structure:
 # list of 32 lists: list(waveform, sample_rate, label, speaker_id, utterance_number)
 # Transpose it
 batch  purrr::transpose(batch)
 tensors  batch$waveform
 targets  batch$label_index

 # Group the list of tensors into a batched tensor
 tensors  pad_sequence(tensors)
 
 # target encoding
 targets  torch::torch_stack(targets)

 list(tensors = tensors, targets = targets) # (64, 1, 16001)
}

Batch structure is:

• batch[[1]]: waveforms – tensor with dimension (32, 1, 16001)
• batch[[2]]: targets – tensor with dimension (32, 1)

Also, torchaudio comes with 3 loaders, av_loader, tuner_loader, and audiofile_loader– more to come. set_audio_backend() is used to set one of them as the audio loader. Their performances differ based on audio format (mp3 or wav). There is no perfect world yet: tuner_loader is best for mp3, audiofile_loader is best for wav, but neither of them has the option of partially loading a sample from an audio file without bringing all the data into memory first.

For a given audio backend we need pass it to each worker through worker_init_fn() argument.

ds_train  dataloader(
  train_subset, 
  batch_size = 128, 
  shuffle = TRUE, 
  collate_fn = collate_fn,
  num_workers = 16,
  worker_init_fn = function(.) {torchaudio::set_audio_backend("audiofile_loader")},
  worker_globals = c("pad_sequence") # pad_sequence is needed for collect_fn
)

ds_test  dataloader(
  test_subset, 
  batch_size = 64, 
  shuffle = FALSE, 
  collate_fn = collate_fn,
  num_workers = 8,
  worker_globals = c("pad_sequence") # pad_sequence is needed for collect_fn
)

Model definition

Instead of keras::keras_model_sequential(), we are going to define a torch::nn_module(). As referenced by the original article, the model is based on this architecture for MNIST from this tutorial, and I’ll call it ‘DanielNN’.

dan_nn  torch::nn_module(
  "DanielNN",
  
  initialize = function(
    window_size_ms = 30, 
    window_stride_ms = 10
  ) {
    
    # spectrogram spec
    window_size  as.integer(16000*window_size_ms/1000)
    stride  as.integer(16000*window_stride_ms/1000)
    fft_size  as.integer(2^trunc(log(window_size, 2) + 1))
    n_chunks  length(seq(0, 16000, stride))
    
    self$spectrogram  torchaudio::transform_spectrogram(
      n_fft = fft_size, 
      win_length = window_size, 
      hop_length = stride, 
      normalized = TRUE, 
      power = 2
    )
    
    # convs 2D
    self$conv1  torch::nn_conv2d(in_channels = 1, out_channels = 32, kernel_size = c(3,3))
    self$conv2  torch::nn_conv2d(in_channels = 32, out_channels = 64, kernel_size = c(3,3))
    self$conv3  torch::nn_conv2d(in_channels = 64, out_channels = 128, kernel_size = c(3,3))
    self$conv4  torch::nn_conv2d(in_channels = 128, out_channels = 256, kernel_size = c(3,3))
    
    # denses
    self$dense1  torch::nn_linear(in_features = 14336, out_features = 128)
    self$dense2  torch::nn_linear(in_features = 128, out_features = 30)
  },
  
  forward = function(x) {
    x %>% # (64, 1, 16001)
      self$spectrogram() %>% # (64, 1, 257, 101)
      torch::torch_add(0.01) %>%
      torch::torch_log() %>%
      self$conv1() %>%
      torch::nnf_relu() %>%
      torch::nnf_max_pool2d(kernel_size = c(2,2)) %>%
      
      self$conv2() %>%
      torch::nnf_relu() %>%
      torch::nnf_max_pool2d(kernel_size = c(2,2)) %>%
      
      self$conv3() %>%
      torch::nnf_relu() %>%
      torch::nnf_max_pool2d(kernel_size = c(2,2)) %>%
      
      self$conv4() %>%
      torch::nnf_relu() %>%
      torch::nnf_max_pool2d(kernel_size = c(2,2)) %>%
      
      torch::nnf_dropout(p = 0.25) %>%
      torch::torch_flatten(start_dim = 2) %>%
      
      self$dense1() %>%
      torch::nnf_relu() %>%
      torch::nnf_dropout(p = 0.5) %>%
      self$dense2() 
  }
)

model  dan_nn()


device  torch::torch_device(if(torch::cuda_is_available()) "cuda" else "cpu")
model$to(device = device)

print(model)

An `nn_module` containing 2,226,846 parameters.

── Modules ──────────────────────────────────────────────────────
● spectrogram:  #0 parameters
● conv1:  #320 parameters
● conv2:  #18,496 parameters
● conv3:  #73,856 parameters
● conv4:  #295,168 parameters
● dense1:  #1,835,136 parameters
● dense2:  #3,870 parameters

Model fitting

Unlike in tensorflow, there is no model %>% compile(...) step in torch, so we are going to set loss criterion, optimizer strategy and evaluation metrics explicitly in the training loop.

loss_criterion  torch::nn_cross_entropy_loss()
optimizer  torch::optim_adadelta(model$parameters, rho = 0.95, eps = 1e-7)
metrics  list(acc = yardstick::accuracy_vec)

Training loop

library(glue)
library(progress)

pred_to_r  function(x) {
  classes  factor(df$classes)
  classes[as.numeric(x$to(device = "cpu"))]
}

set_progress_bar  function(total) {
  progress_bar$new(
    total = total, clear = FALSE, width = 70,
    format = ":current/:total [:bar] - :elapsed - loss: :loss - acc: :acc"
  )
}

epochs  20
losses  c()
accs  c()

for(epoch in seq_len(epochs)) {
  pb  set_progress_bar(length(ds_train))
  pb$message(glue("Epoch {epoch}/{epochs}"))
  coro::loop(for(batch in ds_train) {
    optimizer$zero_grad()
    predictions  model(batch[[1]]$to(device = device))
    targets  batch[[2]]$to(device = device)
    loss  loss_criterion(predictions, targets)
    loss$backward()
    optimizer$step()
    
    # eval reports
    prediction_r  pred_to_r(predictions$argmax(dim = 2))
    targets_r  pred_to_r(targets)
    acc  metrics$acc(targets_r, prediction_r)
    accs  c(accs, acc)
    loss_r  as.numeric(loss$item())
    losses  c(losses, loss_r)
    
    pb$tick(tokens = list(loss = round(mean(losses), 4), acc = round(mean(accs), 4)))
  })
}



# test
predictions_r  c()
targets_r  c()
coro::loop(for(batch_test in ds_test) {
  predictions  model(batch_test[[1]]$to(device = device))
  targets  batch_test[[2]]$to(device = device)
  predictions_r  c(predictions_r, pred_to_r(predictions$argmax(dim = 2)))
  targets_r  c(targets_r, pred_to_r(targets))
})
val_acc  metrics$acc(factor(targets_r, levels = 1:30), factor(predictions_r, levels = 1:30))
cat(glue("val_acc: {val_acc}\n\n"))

Epoch 1/20                                                            
[W SpectralOps.cpp:590] Warning: The function torch.rfft is deprecated and will be removed in a future PyTorch release. Use the new torch.fft module functions, instead, by importing torch.fft and calling torch.fft.fft or torch.fft.rfft. (function operator())
354/354 [=========================] -  1m - loss: 2.6102 - acc: 0.2333
Epoch 2/20                                                            
354/354 [=========================] -  1m - loss: 1.9779 - acc: 0.4138
Epoch 3/20                                                            
354/354 [============================] -  1m - loss: 1.62 - acc: 0.519
Epoch 4/20                                                            
354/354 [=========================] -  1m - loss: 1.3926 - acc: 0.5859
Epoch 5/20                                                            
354/354 [==========================] -  1m - loss: 1.2334 - acc: 0.633
Epoch 6/20                                                            
354/354 [=========================] -  1m - loss: 1.1135 - acc: 0.6685
Epoch 7/20                                                            
354/354 [=========================] -  1m - loss: 1.0199 - acc: 0.6961
Epoch 8/20                                                            
354/354 [=========================] -  1m - loss: 0.9444 - acc: 0.7181
Epoch 9/20                                                            
354/354 [=========================] -  1m - loss: 0.8816 - acc: 0.7365
Epoch 10/20                                                           
354/354 [=========================] -  1m - loss: 0.8278 - acc: 0.7524
Epoch 11/20                                                           
354/354 [=========================] -  1m - loss: 0.7818 - acc: 0.7659
Epoch 12/20                                                           
354/354 [=========================] -  1m - loss: 0.7413 - acc: 0.7778
Epoch 13/20                                                           
354/354 [=========================] -  1m - loss: 0.7064 - acc: 0.7881
Epoch 14/20                                                           
354/354 [=========================] -  1m - loss: 0.6751 - acc: 0.7974
Epoch 15/20                                                           
354/354 [=========================] -  1m - loss: 0.6469 - acc: 0.8058
Epoch 16/20                                                           
354/354 [=========================] -  1m - loss: 0.6216 - acc: 0.8133
Epoch 17/20                                                           
354/354 [=========================] -  1m - loss: 0.5985 - acc: 0.8202
Epoch 18/20                                                           
354/354 [=========================] -  1m - loss: 0.5774 - acc: 0.8263
Epoch 19/20                                                           
354/354 [==========================] -  1m - loss: 0.5582 - acc: 0.832
Epoch 20/20                                                           
354/354 [=========================] -  1m - loss: 0.5403 - acc: 0.8374
val_acc: 0.876705979296493

Making predictions

We already have all predictions calculated for test_subset, let’s recreate the alluvial plot from the original article.

library(dplyr)
library(alluvial)
df_validation  data.frame(
  pred_class = df$classes[predictions_r],
  class = df$classes[targets_r]
)
x   df_validation %>%
  mutate(correct = pred_class == class) %>%
  count(pred_class, class, correct)

alluvial(
  x %>% select(class, pred_class),
  freq = x$n,
  col = ifelse(x$correct, "lightblue", "red"),
  border = ifelse(x$correct, "lightblue", "red"),
  alpha = 0.6,
  hide = x$n  20
)

Figure 2: Model performance: true labels predicted labels.

Model accuracy is 87,7%, somewhat worse than tensorflow version from the original post. Nevertheless, all conclusions from original post still hold.

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don’t fall under this license and can be recognized by a note in their caption: “Figure from …”.

Citation

For attribution, please cite this work as

Damiani (2021, Feb. 4). Posit AI Blog: Simple audio classification with torch. Retrieved from 

BibTeX citation

@misc{athossimpleaudioclassification,
  author = {Damiani, Athos},
  title = {Posit AI Blog: Simple audio classification with torch},
  url = {},
  year = {2021}
}