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This tutorial will show you how to build a basic speech recognition network that recognizes ten different words. It's important to know that real speech and audio recognition systems are much more complex, but like MNIST for images, it should give you a basic understanding of the techniques involved. Once you've completed this tutorial, you'll have a model that tries to classify a one second audio clip as "down", "go", "left", "no", "right", "stop", "up" and "yes".
Setup
Import necessary modules and dependencies.
import os
import pathlib
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import tensorflow as tf
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras import layers
from tensorflow.keras import models
from IPython import display
# Set seed for experiment reproducibility
seed = 42
tf.random.set_seed(seed)
np.random.seed(seed)
Import the Speech Commands dataset
You'll write a script to download a portion of the Speech Commands dataset. The original dataset consists of over 105,000 WAV audio files of people saying thirty different words. This data was collected by Google and released under a CC BY license.
You'll be using a portion of the dataset to save time with data loading. Extract the mini_speech_commands.zip and load it in using the tf.data API.
data_dir = pathlib.Path('data/mini_speech_commands')
if not data_dir.exists():
tf.keras.utils.get_file(
'mini_speech_commands.zip',
origin="http://storage.googleapis.com/download.tensorflow.org/data/mini_speech_commands.zip",
extract=True,
cache_dir='.', cache_subdir='data')
Downloading data from http://storage.googleapis.com/download.tensorflow.org/data/mini_speech_commands.zip 182083584/182082353 [==============================] - 1s 0us/step
Check basic statistics about the dataset.
commands = np.array(tf.io.gfile.listdir(str(data_dir)))
commands = commands[commands != 'README.md']
print('Commands:', commands)
Commands: ['up' 'down' 'go' 'stop' 'left' 'no' 'yes' 'right']
Extract the audio files into a list and shuffle it.
filenames = tf.io.gfile.glob(str(data_dir) + '/*/*')
filenames = tf.random.shuffle(filenames)
num_samples = len(filenames)
print('Number of total examples:', num_samples)
print('Number of examples per label:',
len(tf.io.gfile.listdir(str(data_dir/commands[0]))))
print('Example file tensor:', filenames[0])
Number of total examples: 8000 Number of examples per label: 1000 Example file tensor: tf.Tensor(b'data/mini_speech_commands/up/50f55535_nohash_0.wav', shape=(), dtype=string)
Split the files into training, validation and test sets using a 80:10:10 ratio, respectively.
train_files = filenames[:6400]
val_files = filenames[6400: 6400 + 800]
test_files = filenames[-800:]
print('Training set size', len(train_files))
print('Validation set size', len(val_files))
print('Test set size', len(test_files))
Training set size 6400 Validation set size 800 Test set size 800
Reading audio files and their labels
The audio file will initially be read as a binary file, which you'll want to convert into a numerical tensor.
To load an audio file, you will use tf.audio.decode_wav, which returns the WAV-encoded audio as a Tensor and the sample rate.
A WAV file contains time series data with a set number of samples per second.
Each sample represents the amplitude of the audio signal at that specific time. In a 16-bit system, like the files in mini_speech_commands, the values range from -32768 to 32767.
The sample rate for this dataset is 16kHz.
Note that tf.audio.decode_wav will normalize the values to the range [-1.0, 1.0].
def decode_audio(audio_binary):
audio, _ = tf.audio.decode_wav(audio_binary)
return tf.squeeze(audio, axis=-1)
The label for each WAV file is its parent directory.
def get_label(file_path):
parts = tf.strings.split(file_path, os.path.sep)
# Note: You'll use indexing here instead of tuple unpacking to enable this
# to work in a TensorFlow graph.
return parts[-2]
Let's define a method that will take in the filename of the WAV file and output a tuple containing the audio and labels for supervised training.
def get_waveform_and_label(file_path):
label = get_label(file_path)
audio_binary = tf.io.read_file(file_path)
waveform = decode_audio(audio_binary)
return waveform, label
You will now apply process_path to build your training set to extract the audio-label pairs and check the results. You'll build the validation and test sets using a similar procedure later on.
AUTOTUNE = tf.data.AUTOTUNE
files_ds = tf.data.Dataset.from_tensor_slices(train_files)
waveform_ds = files_ds.map(get_waveform_and_label, num_parallel_calls=AUTOTUNE)
Let's examine a few audio waveforms with their corresponding labels.
rows = 3
cols = 3
n = rows*cols
fig, axes = plt.subplots(rows, cols, figsize=(10, 12))
for i, (audio, label) in enumerate(waveform_ds.take(n)):
r = i // cols
c = i % cols
ax = axes[r][c]
ax.plot(audio.numpy())
ax.set_yticks(np.arange(-1.2, 1.2, 0.2))
label = label.numpy().decode('utf-8')
ax.set_title(label)
plt.show()
Spectrogram
You'll convert the waveform into a spectrogram, which shows frequency changes over time and can be represented as a 2D image. This can be done by applying the short-time Fourier transform (STFT) to convert the audio into the time-frequency domain.
A Fourier transform (tf.signal.fft) converts a signal to its component frequencies, but loses all time information. The STFT (tf.signal.stft) splits the signal into windows of time and runs a Fourier transform on each window, preserving some time information, and returning a 2D tensor that you can run standard convolutions on.
STFT produces an array of complex numbers representing magnitude and phase. However, you'll only need the magnitude for this tutorial, which can be derived by applying tf.abs on the output of tf.signal.stft.
Choose frame_length and frame_step parameters such that the generated spectrogram "image" is almost square. For more information on STFT parameters choice, you can refer to this video on audio signal processing.
You also want the waveforms to have the same length, so that when you convert it to a spectrogram image, the results will have similar dimensions. This can be done by simply zero padding the audio clips that are shorter than one second.
def get_spectrogram(waveform):
# Padding for files with less than 16000 samples
zero_padding = tf.zeros([16000] - tf.shape(waveform), dtype=tf.float32)
# Concatenate audio with padding so that all audio clips will be of the
# same length
waveform = tf.cast(waveform, tf.float32)
equal_length = tf.concat([waveform, zero_padding], 0)
spectrogram = tf.signal.stft(
equal_length, frame_length=255, frame_step=128)
spectrogram = tf.abs(spectrogram)
return spectrogram
Next, you will explore the data. Compare the waveform, the spectrogram and the actual audio of one example from the dataset.
for waveform, label in waveform_ds.take(1):
label = label.numpy().decode('utf-8')
spectrogram = get_spectrogram(waveform)
print('Label:', label)
print('Waveform shape:', waveform.shape)
print('Spectrogram shape:', spectrogram.shape)
print('Audio playback')
display.display(display.Audio(waveform, rate=16000))
Label: up Waveform shape: (16000,) Spectrogram shape: (124, 129) Audio playback
def plot_spectrogram(spectrogram, ax):
# Convert to frequencies to log scale and transpose so that the time is
# represented in the x-axis (columns).
log_spec = np.log(spectrogram.T)
height = log_spec.shape[0]
width = log_spec.shape[1]
X = np.linspace(0, np.size(spectrogram), num=width, dtype=int)
Y = range(height)
ax.pcolormesh(X, Y, log_spec)
fig, axes = plt.subplots(2, figsize=(12, 8))
timescale = np.arange(waveform.shape[0])
axes[0].plot(timescale, waveform.numpy())
axes[0].set_title('Waveform')
axes[0].set_xlim([0, 16000])
plot_spectrogram(spectrogram.numpy(), axes[1])
axes[1].set_title('Spectrogram')
plt.show()
/home/kbuilder/.local/lib/python3.6/site-packages/ipykernel_launcher.py:9: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3. Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading']. This will become an error two minor releases later. if __name__ == '__main__':
Now transform the waveform dataset to have spectrogram images and their corresponding labels as integer IDs.
def get_spectrogram_and_label_id(audio, label):
spectrogram = get_spectrogram(audio)
spectrogram = tf.expand_dims(spectrogram, -1)
label_id = tf.argmax(label == commands)
return spectrogram, label_id
spectrogram_ds = waveform_ds.map(
get_spectrogram_and_label_id, num_parallel_calls=AUTOTUNE)
Examine the spectrogram "images" for different samples of the dataset.
rows = 3
cols = 3
n = rows*cols
fig, axes = plt.subplots(rows, cols, figsize=(10, 10))
for i, (spectrogram, label_id) in enumerate(spectrogram_ds.take(n)):
r = i // cols
c = i % cols
ax = axes[r][c]
plot_spectrogram(np.squeeze(spectrogram.numpy()), ax)
ax.set_title(commands[label_id.numpy()])
ax.axis('off')
plt.show()
/home/kbuilder/.local/lib/python3.6/site-packages/ipykernel_launcher.py:9: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3. Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading']. This will become an error two minor releases later. if __name__ == '__main__': /home/kbuilder/.local/lib/python3.6/site-packages/ipykernel_launcher.py:4: RuntimeWarning: divide by zero encountered in log after removing the cwd from sys.path.
Build and train the model
Now you can build and train your model. But before you do that, you'll need to repeat the training set preprocessing on the validation and test sets.
def preprocess_dataset(files):
files_ds = tf.data.Dataset.from_tensor_slices(files)
output_ds = files_ds.map(get_waveform_and_label, num_parallel_calls=AUTOTUNE)
output_ds = output_ds.map(
get_spectrogram_and_label_id, num_parallel_calls=AUTOTUNE)
return output_ds
train_ds = spectrogram_ds
val_ds = preprocess_dataset(val_files)
test_ds = preprocess_dataset(test_files)
Batch the training and validation sets for model training.
batch_size = 64
train_ds = train_ds.batch(batch_size)
val_ds = val_ds.batch(batch_size)
Add dataset cache() and prefetch() operations to reduce read latency while training the model.
train_ds = train_ds.cache().prefetch(AUTOTUNE)
val_ds = val_ds.cache().prefetch(AUTOTUNE)
For the model, you'll use a simple convolutional neural network (CNN), since you have transformed the audio files into spectrogram images. The model also has the following additional preprocessing layers:
- A
Resizinglayer to downsample the input to enable the model to train faster. - A
Normalizationlayer to normalize each pixel in the image based on its mean and standard deviation.
For the Normalization layer, its adapt method would first need to be called on the training data in order to compute aggregate statistics (i.e. mean and standard deviation).
for spectrogram, _ in spectrogram_ds.take(1):
input_shape = spectrogram.shape
print('Input shape:', input_shape)
num_labels = len(commands)
norm_layer = preprocessing.Normalization()
norm_layer.adapt(spectrogram_ds.map(lambda x, _: x))
model = models.Sequential([
layers.Input(shape=input_shape),
preprocessing.Resizing(32, 32),
norm_layer,
layers.Conv2D(32, 3, activation='relu'),
layers.Conv2D(64, 3, activation='relu'),
layers.MaxPooling2D(),
layers.Dropout(0.25),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dropout(0.5),
layers.Dense(num_labels),
])
model.summary()
Input shape: (124, 129, 1) Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= resizing (Resizing) (None, 32, 32, 1) 0 _________________________________________________________________ normalization (Normalization (None, 32, 32, 1) 3 _________________________________________________________________ conv2d (Conv2D) (None, 30, 30, 32) 320 _________________________________________________________________ conv2d_1 (Conv2D) (None, 28, 28, 64) 18496 _________________________________________________________________ max_pooling2d (MaxPooling2D) (None, 14, 14, 64) 0 _________________________________________________________________ dropout (Dropout) (None, 14, 14, 64) 0 _________________________________________________________________ flatten (Flatten) (None, 12544) 0 _________________________________________________________________ dense (Dense) (None, 128) 1605760 _________________________________________________________________ dropout_1 (Dropout) (None, 128) 0 _________________________________________________________________ dense_1 (Dense) (None, 8) 1032 ================================================================= Total params: 1,625,611 Trainable params: 1,625,608 Non-trainable params: 3 _________________________________________________________________
model.compile(
optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'],
)
EPOCHS = 10
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=EPOCHS,
callbacks=tf.keras.callbacks.EarlyStopping(verbose=1, patience=2),
)
Epoch 1/10 100/100 [==============================] - 16s 45ms/step - loss: 1.9259 - accuracy: 0.2930 - val_loss: 1.3422 - val_accuracy: 0.5638 Epoch 2/10 100/100 [==============================] - 0s 4ms/step - loss: 1.2773 - accuracy: 0.5598 - val_loss: 0.9881 - val_accuracy: 0.6812 Epoch 3/10 100/100 [==============================] - 0s 4ms/step - loss: 0.9726 - accuracy: 0.6546 - val_loss: 0.7903 - val_accuracy: 0.7362 Epoch 4/10 100/100 [==============================] - 0s 4ms/step - loss: 0.7634 - accuracy: 0.7252 - val_loss: 0.6914 - val_accuracy: 0.7550 Epoch 5/10 100/100 [==============================] - 0s 4ms/step - loss: 0.6580 - accuracy: 0.7596 - val_loss: 0.6356 - val_accuracy: 0.7975 Epoch 6/10 100/100 [==============================] - 0s 4ms/step - loss: 0.5770 - accuracy: 0.7912 - val_loss: 0.5791 - val_accuracy: 0.8062 Epoch 7/10 100/100 [==============================] - 0s 4ms/step - loss: 0.5080 - accuracy: 0.8128 - val_loss: 0.5495 - val_accuracy: 0.8163 Epoch 8/10 100/100 [==============================] - 0s 4ms/step - loss: 0.4813 - accuracy: 0.8282 - val_loss: 0.5315 - val_accuracy: 0.8087 Epoch 9/10 100/100 [==============================] - 0s 4ms/step - loss: 0.4303 - accuracy: 0.8467 - val_loss: 0.4886 - val_accuracy: 0.8263 Epoch 10/10 100/100 [==============================] - 0s 4ms/step - loss: 0.3820 - accuracy: 0.8646 - val_loss: 0.5023 - val_accuracy: 0.8413
Let's check the training and validation loss curves to see how your model has improved during training.
metrics = history.history
plt.plot(history.epoch, metrics['loss'], metrics['val_loss'])
plt.legend(['loss', 'val_loss'])
plt.show()
Evaluate test set performance
Let's run the model on the test set and check performance.
test_audio = []
test_labels = []
for audio, label in test_ds:
test_audio.append(audio.numpy())
test_labels.append(label.numpy())
test_audio = np.array(test_audio)
test_labels = np.array(test_labels)
y_pred = np.argmax(model.predict(test_audio), axis=1)
y_true = test_labels
test_acc = sum(y_pred == y_true) / len(y_true)
print(f'Test set accuracy: {test_acc:.0%}')
Test set accuracy: 83%
Display a confusion matrix
A confusion matrix is helpful to see how well the model did on each of the commands in the test set.
confusion_mtx = tf.math.confusion_matrix(y_true, y_pred)
plt.figure(figsize=(10, 8))
sns.heatmap(confusion_mtx, xticklabels=commands, yticklabels=commands,
annot=True, fmt='g')
plt.xlabel('Prediction')
plt.ylabel('Label')
plt.show()
Run inference on an audio file
Finally, verify the model's prediction output using an input audio file of someone saying "no." How well does your model perform?
sample_file = data_dir/'no/01bb6a2a_nohash_0.wav'
sample_ds = preprocess_dataset([str(sample_file)])
for spectrogram, label in sample_ds.batch(1):
prediction = model(spectrogram)
plt.bar(commands, tf.nn.softmax(prediction[0]))
plt.title(f'Predictions for "{commands[label[0]]}"')
plt.show()
You can see that your model very clearly recognized the audio command as "no."
Next steps
This tutorial showed how you could do simple audio classification using a convolutional neural network with TensorFlow and Python.
To learn how to use transfer learning for audio classification, check out the Sound classification with YAMNet tutorial.
To build your own interactive web app for audio classification, consider taking the TensorFlow.js - Audio recognition using transfer learning codelab.
TensorFlow also has additional support for audio data preparation and augmentation to help with your own audio-based projects.