Easy Audio Classification with Keras

Introduction

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

We are going to use the Speech Instructions dataset which consists of 65,000 one-second audio information of individuals saying 30 totally different phrases. Every file comprises a single spoken English phrase. The dataset was launched by Google below 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 process, like recurrent neural networks, dilated (atrous) convolutions or Studying from Between-class Examples for Deep Sound Recognition.

The mannequin we’ll implement right here will not be the state-of-the-art for audio recognition techniques, that are far more complicated, however is comparatively easy and quick to coach. Plus, we present find out how 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 study helpful representations immediately from the uncooked information. Nonetheless, audio information grows very quick – 16,000 samples per second with a really wealthy construction at many time-scales. In an effort to keep away from having to take care of uncooked wave sound information, researchers normally use some form of function engineering.

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

A standard approach to signify audio information is to interrupt it into small chunks, which normally 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 techniques to additional remodel 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 well creates bins on the frequency axis. An important tutorial on MFCCs might be discovered right here.

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

Importing

On this step we’ll listing all audio .wav information right into a tibble with 3 columns:

• fname: the file title;
• 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 can be helpful to the subsequent step once we will create a generator utilizing the tfdatasets bundle.

Generator

We are going to now create our Dataset, which within the context of tfdatasets, provides operations to the TensorFlow graph so as 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’ll create can be liable for studying the audio information 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 dimensions in milliseconds of every chunk we’ll break the audio wave into, and window_stride_ms, the space between the facilities of adjoining chunks:

window_size_ms <- 30
window_stride_ms <- 10

Now we’ll convert the window measurement and stride from milliseconds to samples. We’re contemplating that our audio information 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 are going to receive different portions that can 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 perform we’re going to use to compute the spectrogram doesn’t enable us to alter the FFT measurement and as an alternative by default makes use of the primary energy of two higher than the window measurement.

We are going to now use dataset_map which permits us to specify a pre-processing perform 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(perform(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 information
    audio_binary <- tf$read_file(tf$reshape(obs$fname, form = listing()))
    wav <- audio_ops$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))
    
    # remodel the class_id right into a one-hot encoded vector
    response <- tf$one_hot(obs$class_id, 30L)
    
    listing(spectrogram, response)
  }) 

Now, we’ll specify how we would like 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 so as to inform TensorFlow that we need to preserve 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 would like batches of measurement 32, however they need to be padded, ie. if some statement has a unique measurement we pad it with zeroes. The padded form is handed to dataset_padded_batch through the padded_shapes argument and we use NULL to state that this dimension doesn’t should be padded.

ds <- ds %>% 
  dataset_shuffle(buffer_size = 100) %>%
  dataset_repeat() %>%
  dataset_padded_batch(
    batch_size = 32, 
    padded_shapes = listing(
      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 perform of the info and different necessary parameters like window_size_ms and window_stride_ms. Beneath, we’ll outline a perform known as data_generator that may create the generator relying on these inputs.

data_generator <- perform(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(perform(obs) {
      
      # decoding wav information
      audio_binary <- tf$read_file(tf$reshape(obs$fname, form = listing()))
      wav <- audio_ops$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))
      
      # remodel the class_id right into a one-hot encoded vector
      response <- tf$one_hot(obs$class_id, 30L)
      
      listing(spectrogram, response)
    }) %>%
    dataset_repeat()
  
  ds <- ds %>% 
    dataset_padded_batch(batch_size, listing(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 gained’t truly compute any spectrogram or learn any file. It would 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 might 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 unique batch of observations.

Mannequin definition

Now that we all know how we’ll feed our information we will deal with the mannequin definition. The spectrogram might be handled like a picture, so architectures which can be generally utilized in picture recognition duties ought to work nicely with the spectrograms too.

We are going to construct a convolutional neural community just 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 are going to 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(charge = 0.25) %>% 
  layer_flatten() %>% 
  layer_dense(models = 128, activation = 'relu') %>% 
  layer_dropout(charge = 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 photographs and a couple of dense layers on the high. Our community is relatively easy when in comparison with extra superior architectures like ResNet or DenseNet that carry out very nicely on picture recognition duties.

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

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

Mannequin becoming

Now, we’ll match our mannequin. In Keras we will use TensorFlow Datasets as inputs to the fit_generator perform and we’ll 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 learn to make predictions and try the confusion matrix.

Making predictions

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

We will calculate the variety of steps by realizing the batch measurement, and the dimensions of the validation dataset.

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

We will then use the predict_generator perform:

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. Notice 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 very best likelihood, 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
)

We will see from the diagram that probably 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 is probably not on small gadgets. We might prepare 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 functions the place it’s frequent to produce other irrelevant sounds occurring within the surroundings.

The complete code to breed this tutorial is offered right here.