Speech Recognition with TensorFlow and Keras Libraries in Python. (Yes, like Siri and Alexa)
Speech recognition models have a wide range of practical applications. One of the most common uses is in virtual assistants, such as Apple’s Siri, Amazon’s Alexa, and Google Assistant. These virtual assistants use speech recognition models to understand and respond to user commands and queries. In addition, speech recognition models are used in call center operations to transcribe customer service calls, in dictation software to transcribe spoken words into text, and in language learning apps to help learners practice their pronunciation. Moreover, speech recognition models are increasingly used in the healthcare industry, where they can be used to transcribe medical notes and patient information, reducing the burden on healthcare professionals and improving patient care.
Sounds pretty cool, right? Here’s how you can get started building one.
Step1. Install the required libraries:
First, you need to install TensorFlow and Keras libraries in Python. You can install them using pip command in the terminal.
pip install tensorflow pip install keras
Step 2. Import the required libraries:
Once the libraries are installed, you need to import them in your Python script.
import tensorflow as tf from tensorflow import keras
Step 3. Load the dataset:
Next, you need to load a dataset of audio recordings and their corresponding transcriptions that you will use to train your model. For this example, we will use the Mozilla Common Voice dataset, which contains thousands of hours of speech data in multiple languages.
# Load the Mozilla Common Voice dataset data = tf.keras.utils.get_file( fname="cv-corpus-6.1-2020-12-11.tar.gz", origin="https://common-voice-data-download.%(domain_name)s/cv-corpus-6.1-2020-12-11/%(file_name)s", extract=True ) # Preprocess the data # TODO: Add preprocessing code here
Step 4. Define the model:
Once the data is preprocessed, you need to define the architecture of the model. For this example, we will use a recurrent neural network (RNN) with LSTM cells
# Define the model inputs = keras.Input(shape=(None, 13)) x = keras.layers.LSTM(128, return_sequences=True)(inputs) x = keras.layers.LSTM(64)(x) outputs = keras.layers.Dense(num_classes, activation='softmax')(x) model = keras.Model(inputs, outputs)
Step 5. Train the model:
Once the model is defined, you need to train it using the preprocessed data.
# Compile the model with a categorical cross-entropy loss function and Adam optimizer model.compile( loss='categorical_crossentropy', optimizer=keras.optimizers.Adam(lr=0.001), metrics=['accuracy'] ) # Train the model for 10 epochs model.fit( x_train, y_train, batch_size=32, epochs=10, validation_data=(x_val, y_val) )
Step 6. Evaluate the model:
After training the model, you need to evaluate its performance on the validation set.
# Evaluate the model on the validation set loss, accuracy = model.evaluate(x_val, y_val) print('Validation accuracy:', accuracy)
Step 7. Test the model:
Once you are satisfied with the model’s performance on the validation set, you can test it on a new set of audio recordings to see how well it generalizes to unseen data.
# Evaluate the model on the test set loss, accuracy = model.evaluate(x_test, y_test) print('Test accuracy:', accuracy)
Step 8. Save the model:
If you want to use the model in a real-world application, you can save it as a file.
# Save the model as a file model.save('speech_recognition_model.h5')
Speech recognition models have the potential to improve the efficiency and accuracy of a wide range of tasks, and can be a powerful tool for automating repetitive and time-consuming tasks. You can learn more about Machine Learning and A.I. by checking out my book: A.I. & Machine Learning by Lyron Foster.