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Build your own real-time voice command recognition model with TensorFlow

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Introduction

In today's tutorial, we will create a speech recognition model using TensorFlow that can recognize specific keywords in real-time via your microphone. This project is a foundation for applications like home automation, or in our demonstration case, controlling a turtle in Python. Let’s dive into the details of how to implement this exciting project!

Demonstration

Before we get into the code, let's go through a quick demo. When running the model, you will see output indicating that it classifies the spoken keywords—commands such as "up," "down," "right," "go," "left," and "stop." With these spoken commands, you can control the movement of a turtle graphics object in Python.

Setting Up the Project

The code we will use follows the guidelines detailed in TensorFlow's official documentation on simple audio recognition. You can start by opening a Google Colab notebook and ensuring that the runtime is set to GPU. After launching the notebook, we’ll run all cells to kick off the setup.

This project utilizes a publicly available dataset known as the Speech Commands dataset which contains various audio commands labeled as follows: "down," "go," "left," "no," "right," "stop," "up," and "yes."

Initially, we will import the required libraries, download the dataset, and check the commands. The audio files are organized in separate folders for each label, which will later be used to create our training, validation, and testing datasets.

Audio Processing

To process the audio, we will perform the following steps:

  1. Extract Audio File Names: Accessing the dataset structure and file names is essential for training our model.
  2. **Waveform Creation:** Convert the audio into a waveform format and plot the data.
  3. Spectrogram Conversion: Utilize a helper function to convert the waveform to a spectrogram, generating an image representation of the audio data that we can classify using a convolutional neural network.

After completing the preprocessing steps, we will build our TensorFlow model using Convolutional layers and execute the training phase. Once trained, we will analyze the model accuracy and evaluate its performance.

Model Saving and Loading

After obtaining satisfactory results (85% accuracy on the test set), we need to save this model to use it later on our local machine. This involves creating a zip file for easier download.

From Google Colab to Local Machine

  1. Use TensorFlow’s model.save() to save the model.
  2. Create a zip file containing the saved model folder.
  3. Download this zip file to your local environment.

To verify the success of this operation, we’ll reload the model using models.load_model() and perform the same predictions again.

Implementing Real-Time Audio Recognition

As we move forward, the essential step is to stop using the built-in TensorFlow method for audio readouts. Instead, we'll set up the model to recognize audio directly from our microphone. This requires:

  • Installing the pyaudio library for capturing real-time audio.
  • Creating a custom audio recording helper function.

This function captures audio for one second at a rate of 16,000 samples, matching our training data setup.

Putting It All Together

Next, we’ll write the main logic of our application that combines all previous steps. It will include functions for recording audio, preprocessing it into the correct format, obtaining model predictions, and executing commands based on detected keywords.

Using a loop that runs indefinitely, we’ll facilitate real-time voice command recognition that stops when we say "stop."

Adding Turtle Graphics

Finally, we will incorporate a turtle helper class that enables movement commands corresponding with the recognized keywords. Each direction command will lead to the turtle moving in that specific direction on the screen.

Conclusion

After running the program, you should have a fully functional real-time voice command recognition system. This project is versatile and can be expanded upon for various applications.

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FAQ

1. What is TensorFlow?
TensorFlow is an open-source machine learning framework developed by Google for building and training machine learning models.

2. How does voice recognition work?
Voice recognition involves converting spoken language into text by analyzing audio signals and identifying patterns that correspond to specific words.

3. What dataset are we using for this project?
We are using the Speech Commands dataset, which contains audio files labeled with specific commands.

4. How can I run this project on my local machine?
You can follow the steps outlined in the article to set up your virtual environment, install required packages, and run the Python scripts for real-time audio recognition.

5. Can I modify the keywords?
Yes, you can modify the keywords by adjusting the dataset and training the model accordingly.