Easy Audio Classification with Keras

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Easy Audio Classification with Keras


Introduction

On this tutorial we are going to construct a deep studying mannequin to categorise phrases. We’ll use tfdatasets to deal with information IO and pre-processing, and Keras to construct and practice the mannequin.

We’ll use the Speech Instructions dataset which consists of 65,000 one-second audio recordsdata of individuals saying 30 completely different phrases. Every file accommodates a single spoken English phrase. The dataset was launched by Google underneath CC License.

Our mannequin is a Keras port of the TensorFlow tutorial on Easy Audio Recognition which in flip was impressed by Convolutional Neural Networks for Small-footprint Key phrase Recognizing. There are different approaches to the speech recognition activity, like recurrent neural networks, dilated (atrous) convolutions or Studying from Between-class Examples for Deep Sound Recognition.

The mannequin we are going to implement right here shouldn’t be the cutting-edge for audio recognition programs, that are far more advanced, however is comparatively easy and quick to coach. Plus, we present the right way to effectively use tfdatasets to preprocess and serve information.

Audio illustration

Many deep studying fashions are end-to-end, i.e. we let the mannequin be taught helpful representations instantly from the uncooked information. Nevertheless, audio information grows very quick – 16,000 samples per second with a really wealthy construction at many time-scales. To be able to keep away from having to take care of uncooked wave sound information, researchers often use some form of function engineering.

Each sound wave could be represented by its spectrum, and digitally it may be computed utilizing the Quick Fourier Remodel (FFT).

Easy Audio Classification with Keras

A typical technique to signify audio information is to interrupt it into small chunks, which often overlap. For every chunk we use the FFT to calculate the magnitude of the frequency spectrum. The spectra are then mixed, facet by facet, to kind what we name a spectrogram.

It’s additionally frequent for speech recognition programs to additional rework the spectrum and compute the Mel-Frequency Cepstral Coefficients. This transformation takes into consideration that the human ear can’t discern the distinction between two intently spaced frequencies and neatly creates bins on the frequency axis. An awesome tutorial on MFCCs could be discovered right here.

By Aquegg - Own work, Public Domain, https://commons.wikimedia.org/w/index.php?curid=5544473

After this process, we’ve got a picture for every audio pattern and we will use convolutional neural networks, the usual structure kind in picture recognition fashions.

Downloading

First, let’s obtain information to a listing in our venture. You may both obtain from this hyperlink (~1GB) or from R with:

dir.create("information")

obtain.file(
  url = "http://obtain.tensorflow.org/information/speech_commands_v0.01.tar.gz", 
  destfile = "information/speech_commands_v0.01.tar.gz"
)

untar("information/speech_commands_v0.01.tar.gz", exdir = "information/speech_commands_v0.01")

Contained in the information listing we may have a folder referred to as speech_commands_v0.01. The WAV audio recordsdata inside this listing are organised in sub-folders with the label names. For instance, all one-second audio recordsdata of individuals talking the phrase “mattress” are contained in the mattress listing. There are 30 of them and a particular one referred to as _background_noise_ which accommodates varied patterns that may very well be combined in to simulate background noise.

Importing

On this step we are going to checklist all audio .wav recordsdata right into a tibble with 3 columns:

  • fname: the file identify;
  • class: the label for every audio file;
  • class_id: a novel integer quantity ranging from zero for every class – used to one-hot encode the lessons.

This will likely be helpful to the following step after we will create a generator utilizing the tfdatasets package deal.

Generator

We’ll now create our Dataset, which within the context of tfdatasets, provides operations to the TensorFlow graph to be able to learn and pre-process information. Since they’re TensorFlow ops, they’re executed in C++ and in parallel with mannequin coaching.

The generator we are going to create will likely be accountable for studying the audio recordsdata from disk, creating the spectrogram for every one and batching the outputs.

Let’s begin by creating the dataset from slices of the information.body with audio file names and lessons we simply created.

Now, let’s outline the parameters for spectrogram creation. We have to outline window_size_ms which is the scale in milliseconds of every chunk we are going to break the audio wave into, and window_stride_ms, the gap between the facilities of adjoining chunks:

window_size_ms <- 30
window_stride_ms <- 10

Now we are going to convert the window measurement and stride from milliseconds to samples. We’re contemplating that our audio recordsdata have 16,000 samples per second (1000 ms).

window_size <- as.integer(16000*window_size_ms/1000)
stride <- as.integer(16000*window_stride_ms/1000)

We’ll acquire different portions that will likely be helpful for spectrogram creation, just like the variety of chunks and the FFT measurement, i.e., the variety of bins on the frequency axis. The operate we’re going to use to compute the spectrogram doesn’t enable us to alter the FFT measurement and as a substitute by default makes use of the primary energy of two higher than the window measurement.

We’ll now use dataset_map which permits us to specify a pre-processing operate for every statement (line) of our dataset. It’s on this step that we learn the uncooked audio file from disk and create its spectrogram and the one-hot encoded response vector.

# shortcuts to used TensorFlow modules.
audio_ops <- tf$contrib$framework$python$ops$audio_ops

ds <- ds %>%
  dataset_map(operate(obs) {
    
    # a great way to debug when constructing tfdatsets pipelines is to make use of a print
    # assertion like this:
    # print(str(obs))
    
    # decoding wav recordsdata
    audio_binary <- tf$read_file(tf$reshape(obs$fname, form = checklist()))
    wav <- audio_ops$decode_wav(audio_binary, desired_channels = 1)
    
    # create the spectrogram
    spectrogram <- audio_ops$audio_spectrogram(
      wav$audio, 
      window_size = window_size, 
      stride = stride,
      magnitude_squared = TRUE
    )
    
    # normalization
    spectrogram <- tf$log(tf$abs(spectrogram) + 0.01)
    
    # transferring channels to final dim
    spectrogram <- tf$transpose(spectrogram, perm = c(1L, 2L, 0L))
    
    # rework the class_id right into a one-hot encoded vector
    response <- tf$one_hot(obs$class_id, 30L)
    
    checklist(spectrogram, response)
  }) 

Now, we are going to specify how we wish batch observations from the dataset. We’re utilizing dataset_shuffle since we need to shuffle observations from the dataset, in any other case it might observe the order of the df object. Then we use dataset_repeat to be able to inform TensorFlow that we need to hold taking observations from the dataset even when all observations have already been used. And most significantly right here, we use dataset_padded_batch to specify that we wish batches of measurement 32, however they need to be padded, ie. if some statement has a distinct measurement we pad it with zeroes. The padded form is handed to dataset_padded_batch by way of the padded_shapes argument and we use NULL to state that this dimension doesn’t must be padded.

ds <- ds %>% 
  dataset_shuffle(buffer_size = 100) %>%
  dataset_repeat() %>%
  dataset_padded_batch(
    batch_size = 32, 
    padded_shapes = checklist(
      form(n_chunks, fft_size, NULL), 
      form(NULL)
    )
  )

That is our dataset specification, however we would want to rewrite all of the code for the validation information, so it’s good apply to wrap this right into a operate of the info and different vital parameters like window_size_ms and window_stride_ms. Under, we are going to outline a operate referred to as data_generator that may create the generator relying on these inputs.

data_generator <- operate(df, batch_size, shuffle = TRUE, 
                           window_size_ms = 30, window_stride_ms = 10) {
  
  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 <- size(seq(window_size/2, 16000 - window_size/2, stride))
  
  ds <- tensor_slices_dataset(df)
  
  if (shuffle) 
    ds <- ds %>% dataset_shuffle(buffer_size = 100)  
  
  ds <- ds %>%
    dataset_map(operate(obs) {
      
      # decoding wav recordsdata
      audio_binary <- tf$read_file(tf$reshape(obs$fname, form = checklist()))
      wav <- audio_ops$decode_wav(audio_binary, desired_channels = 1)
      
      # create the spectrogram
      spectrogram <- audio_ops$audio_spectrogram(
        wav$audio, 
        window_size = window_size, 
        stride = stride,
        magnitude_squared = TRUE
      )
      
      spectrogram <- tf$log(tf$abs(spectrogram) + 0.01)
      spectrogram <- tf$transpose(spectrogram, perm = c(1L, 2L, 0L))
      
      # rework the class_id right into a one-hot encoded vector
      response <- tf$one_hot(obs$class_id, 30L)
      
      checklist(spectrogram, response)
    }) %>%
    dataset_repeat()
  
  ds <- ds %>% 
    dataset_padded_batch(batch_size, checklist(form(n_chunks, fft_size, NULL), form(NULL)))
  
  ds
}

Now, we will outline coaching and validation information mills. It’s price noting that executing this received’t truly compute any spectrogram or learn any file. It’ll solely outline within the TensorFlow graph the way it ought to learn and pre-process information.

set.seed(6)
id_train <- pattern(nrow(df), measurement = 0.7*nrow(df))

ds_train <- data_generator(
  df[id_train,], 
  batch_size = 32, 
  window_size_ms = 30, 
  window_stride_ms = 10
)
ds_validation <- data_generator(
  df[-id_train,], 
  batch_size = 32, 
  shuffle = FALSE, 
  window_size_ms = 30, 
  window_stride_ms = 10
)

To really get a batch from the generator we may create a TensorFlow session and ask it to run the generator. For instance:

sess <- tf$Session()
batch <- next_batch(ds_train)
str(sess$run(batch))
Checklist of two
 $ : num [1:32, 1:98, 1:257, 1] -4.6 -4.6 -4.61 -4.6 -4.6 ...
 $ : num [1:32, 1:30] 0 0 0 0 0 0 0 0 0 0 ...

Every time you run sess$run(batch) it is best to see a distinct batch of observations.

Mannequin definition

Now that we all know how we are going to feed our information we will give attention to the mannequin definition. The spectrogram could be handled like a picture, so architectures which might be generally utilized in picture recognition duties ought to work properly with the spectrograms too.

We’ll construct a convolutional neural community much like what we’ve got constructed right here for the MNIST dataset.

The enter measurement is outlined by the variety of chunks and the FFT measurement. Like we defined earlier, they are often obtained from the window_size_ms and window_stride_ms used to generate the spectrogram.

We’ll now outline our mannequin utilizing the Keras sequential API:

mannequin <- keras_model_sequential()
mannequin %>%  
  layer_conv_2d(input_shape = c(n_chunks, fft_size, 1), 
                filters = 32, kernel_size = c(3,3), activation = 'relu') %>% 
  layer_max_pooling_2d(pool_size = c(2, 2)) %>% 
  layer_conv_2d(filters = 64, kernel_size = c(3,3), activation = 'relu') %>% 
  layer_max_pooling_2d(pool_size = c(2, 2)) %>% 
  layer_conv_2d(filters = 128, kernel_size = c(3,3), activation = 'relu') %>% 
  layer_max_pooling_2d(pool_size = c(2, 2)) %>% 
  layer_conv_2d(filters = 256, kernel_size = c(3,3), activation = 'relu') %>% 
  layer_max_pooling_2d(pool_size = c(2, 2)) %>% 
  layer_dropout(price = 0.25) %>% 
  layer_flatten() %>% 
  layer_dense(models = 128, activation = 'relu') %>% 
  layer_dropout(price = 0.5) %>% 
  layer_dense(models = 30, activation = 'softmax')

We used 4 layers of convolutions mixed with max pooling layers to extract options from the spectrogram photos and a couple of dense layers on the prime. Our community is relatively easy when in comparison with extra superior architectures like ResNet or DenseNet that carry out very properly on picture recognition duties.

Now let’s compile our mannequin. We’ll use categorical cross entropy because the loss operate and use the Adadelta optimizer. It’s additionally right here that we outline that we’ll take a look at the accuracy metric throughout coaching.

mannequin %>% compile(
  loss = loss_categorical_crossentropy,
  optimizer = optimizer_adadelta(),
  metrics = c('accuracy')
)

Mannequin becoming

Now, we are going to match our mannequin. In Keras we will use TensorFlow Datasets as inputs to the fit_generator operate and we are going to do it right here.

mannequin %>% fit_generator(
  generator = ds_train,
  steps_per_epoch = 0.7*nrow(df)/32,
  epochs = 10, 
  validation_data = ds_validation, 
  validation_steps = 0.3*nrow(df)/32
)
Epoch 1/10
1415/1415 [==============================] - 87s 62ms/step - loss: 2.0225 - acc: 0.4184 - val_loss: 0.7855 - val_acc: 0.7907
Epoch 2/10
1415/1415 [==============================] - 75s 53ms/step - loss: 0.8781 - acc: 0.7432 - val_loss: 0.4522 - val_acc: 0.8704
Epoch 3/10
1415/1415 [==============================] - 75s 53ms/step - loss: 0.6196 - acc: 0.8190 - val_loss: 0.3513 - val_acc: 0.9006
Epoch 4/10
1415/1415 [==============================] - 75s 53ms/step - loss: 0.4958 - acc: 0.8543 - val_loss: 0.3130 - val_acc: 0.9117
Epoch 5/10
1415/1415 [==============================] - 75s 53ms/step - loss: 0.4282 - acc: 0.8754 - val_loss: 0.2866 - val_acc: 0.9213
Epoch 6/10
1415/1415 [==============================] - 76s 53ms/step - loss: 0.3852 - acc: 0.8885 - val_loss: 0.2732 - val_acc: 0.9252
Epoch 7/10
1415/1415 [==============================] - 75s 53ms/step - loss: 0.3566 - acc: 0.8991 - val_loss: 0.2700 - val_acc: 0.9269
Epoch 8/10
1415/1415 [==============================] - 76s 54ms/step - loss: 0.3364 - acc: 0.9045 - val_loss: 0.2573 - val_acc: 0.9284
Epoch 9/10
1415/1415 [==============================] - 76s 53ms/step - loss: 0.3220 - acc: 0.9087 - val_loss: 0.2537 - val_acc: 0.9323
Epoch 10/10
1415/1415 [==============================] - 76s 54ms/step - loss: 0.2997 - acc: 0.9150 - val_loss: 0.2582 - val_acc: 0.9323

The mannequin’s accuracy is 93.23%. Let’s discover ways to make predictions and try the confusion matrix.

Making predictions

We will use thepredict_generator operate to make predictions on a brand new dataset. Let’s make predictions for our validation dataset. The predict_generator operate wants a step argument which is the variety of instances the generator will likely be referred to as.

We will calculate the variety of steps by figuring out the batch measurement, and the scale of the validation dataset.

df_validation <- df[-id_train,]
n_steps <- nrow(df_validation)/32 + 1

We will then use the predict_generator operate:

predictions <- predict_generator(
  mannequin, 
  ds_validation, 
  steps = n_steps
  )
str(predictions)
num [1:19424, 1:30] 1.22e-13 7.30e-19 5.29e-10 6.66e-22 1.12e-17 ...

This may output a matrix with 30 columns – one for every phrase and n_steps*batch_size variety of rows. Be aware that it begins repeating the dataset on the finish to create a full batch.

We will compute the anticipated class by taking the column with the best chance, for instance.

lessons <- apply(predictions, 1, which.max) - 1

A pleasant visualization of the confusion matrix is to create an alluvial diagram:

library(dplyr)
library(alluvial)
x <- df_validation %>%
  mutate(pred_class_id = head(lessons, nrow(df_validation))) %>%
  left_join(
    df_validation %>% distinct(class_id, class) %>% rename(pred_class = class),
    by = c("pred_class_id" = "class_id")
  ) %>%
  mutate(appropriate = pred_class == class) %>%
  depend(pred_class, class, appropriate)

alluvial(
  x %>% choose(class, pred_class),
  freq = x$n,
  col = ifelse(x$appropriate, "lightblue", "pink"),
  border = ifelse(x$appropriate, "lightblue", "pink"),
  alpha = 0.6,
  disguise = x$n < 20
)
Alluvial Plot

We will see from the diagram that essentially the most related mistake our mannequin makes is to categorise “tree” as “three”. There are different frequent errors like classifying “go” as “no”, “up” as “off”. At 93% accuracy for 30 lessons, and contemplating the errors we will say that this mannequin is fairly cheap.

The saved mannequin occupies 25Mb of disk house, which is cheap for a desktop however might not be on small units. We may practice a smaller mannequin, with fewer layers, and see how a lot the efficiency decreases.

In speech recognition duties its additionally frequent to do some form of information augmentation by mixing a background noise to the spoken audio, making it extra helpful for actual purposes the place it’s frequent to produce other irrelevant sounds taking place within the atmosphere.

The complete code to breed this tutorial is on the market right here.

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