The Promise of TinyML
The world of artificial intelligence has long been associated with powerful computers, cloud servers, and complex neural networks. Yet, in recent years, a new frontier has emerged that brings AI directly to tiny, everyday devices. This frontier is TinyML – the practice of running machine learning models on microcontrollers and other resource-constrained hardware. TinyML allows developers to implement intelligent features in devices that consume minimal power, have limited memory, and operate without relying on cloud services. One exciting application of TinyML is enabling microcontrollers to understand and respond to voice commands.
Project Overview
In this series, we will explore a project that brings TinyML to life: using an ESP32 microcontroller to control an RGB LED through simple voice commands. By saying “red”, “green”, “blue”, or “off”, the device will recognize the spoken word and change the LED’s color accordingly. This project demonstrates how AI can be embedded directly in hardware, offering an accessible and low-cost way to experiment with voice recognition, machine learning, and real-time control.
Hardware Components
The core of this project is the ESP32-WROVER board, a versatile microcontroller that combines Wi-Fi and Bluetooth capabilities with sufficient RAM to run lightweight neural networks. The board itself does not include a microphone, so we will integrate a small I²S microphone, such as the INMP441, to capture audio signals. The microphone converts sound into digital data that the ESP32 can process in real time. Alongside the microphone, an RGB LED is connected to the board to provide immediate visual feedback for recognized commands.
Collecting and Preparing Audio Data
One of the key challenges in building a voice-controlled system is collecting and preparing audio data. The ESP32 must be trained to distinguish between the target commands and other sounds, which means creating a dataset of recorded samples. For this project, short audio clips of each command will be recorded, organized into labeled folders, and preprocessed to extract relevant features for the model. Preprocessing ensures the machine learning model can effectively learn the patterns associated with each command despite variations in voice, pitch, or background noise.
Training a TinyML Keyword Spotting Model
Once the dataset is ready, a small neural network model can be trained using TensorFlow Lite Micro, a framework designed for running machine learning models on microcontrollers. The model will learn to recognize the keywords from the audio input and output the corresponding label. Training such a model involves iterating through the data, optimizing the network for accuracy and performance, and validating it to ensure reliable predictions. The result is a compact model that can fit within the ESP32’s memory constraints while remaining responsive in real time.
Deploying the Model on ESP32
Deploying the trained model onto the ESP32 involves converting it into a format suitable for microcontrollers and integrating it with code that handles the I²S microphone input. The ESP32 will continuously capture audio, process it through the model, and use the predictions to control the RGB LED. This seamless loop of listening, processing, and acting demonstrates the power of TinyML: intelligence running directly on edge devices without any dependency on cloud computing.
Broader Applications of TinyML
Beyond the immediate goal of controlling an LED, this project highlights broader possibilities for TinyML in embedded systems. Applications include voice-activated home automation, wearable devices that respond to commands, industrial sensors that react to environmental cues, and even educational tools that teach AI concepts through hands-on experimentation. By embedding intelligence locally, TinyML enables faster response times, enhanced privacy, and reduced energy consumption compared to cloud-dependent solutions.
What to Expect in This Series
To guide you through the project step by step, this blog series will include the following posts:
- Post 1: Introduction to a Voice-Controlled RGB LED with TinyML on ESP32 (this post)
- Post 2: Setting Up ESP32 Hardware for Voice-Controlled LED Projects
- Post 3: Recording High-Quality Audio for TinyML Keyword Spotting
- Post 4: Preparing Your Audio Data for TinyML Training
- Post 5: Training a TinyML Keyword Spotting Model
- Post 6: Deploying TinyML Models on ESP32
- Post 7: Controlling an RGB LED Using Voice Commands
- Post 8: Optimizing Your TinyML Voice-Control System
- Post 9: Project Showcase: TinyML Voice-Controlled LED
This roadmap ensures you can understand what each post will cover and can follow along with the project from start to finish.
Conclusion
By the end of this series, you will have a working voice-controlled RGB LED system powered by TinyML on the ESP32, along with the knowledge and experience to expand the concept to other devices and applications. This project is a stepping stone into the broader world of embedded AI, demonstrating that powerful machine learning capabilities can reside not in the cloud, but in the small, everyday devices around us.