The Esp32 Chatgpt Powered Voice Speaker Github

Emily Johnson

-Mar 13, 2026, 7:40 AM

the esp32 chatgpt powered voice speaker github

This project demonstrates the integration of the ESP32 microcontroller with ChatGPT to create a voice-powered speaker system. The system uses text-to-speech (TTS) and speech recognition to allow users to interact with the ChatGPT model, enabling conversational AI features with voice commands. It showcases the power of the ESP32 combined with cloud-based AI capabilities. Follow the instructions on the ESP32 Arduino Core GitHub to install the ESP32 core in your Arduino IDE. Modify the following section of the code with your Wi-Fi credentials: To interact with ChatGPT, you will need an API key.

Create an OpenAI account and retrieve the API key. Then, add the API key to the code: Ensure the microphone module is connected to the ESP32 according to its specifications. You can use an I2S microphone like the INMP441 or SPH0645. For those exploring what hardware ChatGPT runs on, the traditional answer involves large-scale cloud infrastructure. However, with the OpenAI API and lightweight microcontrollers like the M5Stack ESP32-based AtomS3R, it’s now possible to build a compact, connected ChatGPT AI device.

Paired with the Atomic Echo Base for audio I/O, this setup enables a tiny AI voice assistant capable of real-time voice interaction via Wi-Fi. In this article, we’ll walk you through how to build your own AI-powered voice assistant using OpenAI—no coding required. The M5Stack AtomS3R is a compact microcontroller powered by the ESP32-S3 chip, measuring just 24 × 24 mm. It supports Wi-Fi, Bluetooth, and offline voice wake-up, making it ideal for building portable AI voice assistant and IoT applications. M5Burner is a tool that enables creators to upload firmware and allows users to flash it onto M5Stack devices. If you haven’t downloaded it before, please select the version compatible with your operating system to proceed.

Double click M5Burner > Locate the OpenAI Voice Assistant for AtomS3R Firmware > Click Download. This project implements a Bluetooth audio device using the ESP32 microcontroller, functioning as both a Bluetooth speaker and microphone. The ESP32 connects to a phone or computer via Classic Bluetooth, enabling two-way voice interaction with ChatGPT web or app interfaces that support voice input/output. Affordability and flexibility characterize ESP32 microcontrollers. This is why it shares a special affinity with the enthusiastic crowds engaged in IoT innovation, just like the ChatGPT advanced AI language model from OpenAI transforms the way to power up for natural... This guide is going to show you the way in which you could create a highly featured ESP32 project using ChatGPT.

Whether you’re an amateur or a professional, it’s this brilliant opportunity for you to construct smart, interacting systems. The reasons for why the ESP32 microcontroller is aptly suited for IoT & AI-based applications include: ESP32-based Embedded Chatbot with AI Example ProjectObjective:Build a chatbot on the ESP32 platform which Here is a list of components required to bring this project to life: This repository contains the code and instructions for creating a voice assistant using an ESP32 and ChatGPT. The voice assistant can record audio, send it to a server for transcription, and then receive and play back a response generated by ChatGPT.

Open the chatgpt_voice project in Arduino IDE or PlatformIO. Navigate to the chatgpt_voice_backend directory: Create a config.js file in the chatgpt_voice_backend directory with your OpenAI API key: This project is licensed under the MIT License - see the LICENSE file for details. Add natural language processing capabilities to your ESP32 project by integrating the ChatGPT using OpenAI API. Step-by-step instructions with code examples

ESP32 series of microcontrollers have become popular among IoT enthusiasts and hobbyists due to their low power consumption, built-in WiFi and Bluetooth connectivity, and powerful processing capabilities. With the addition of ChatGPT, you can create more sophisticated and interactive applications, such as chatbots or voice assistants, that can generate human-like responses to text input. In this article, we'll explore the potential of using ChatGPT on ESP32 microcontrollers and go through instructions on how to set it up. We'll also review the benefits and challenges of this integration. ChatGPT is an advanced natural language processing (NLP) model developed by OpenAI that can generate human-like responses to your provided text. It uses a machine learning algorithm, which allows it to understand the context and meaning of text and remember what was said before.

You probably heard about it by now, as ChatGPT has gained 100 million users in 2 months since launch, making it the fastest-growing consumer app ever and has been trending all over the internet... This challenge takes the ChatGPT terminal launched within the November 2023 problem to the following stage the place it now speaks like a VoiceGPT. On this new design, the operate of talking out the questions and solutions has been added by incorporating an I2S sound module just like the MAX98357A and a 4-ohm loudspeaker. The output energy is spectacular and distortion-free; it should be heard to be believed. Two variations are proven right here. Fig.

1 shows the writer’s prototype, and the parts required for the terminal are listed within the Invoice of Supplies (Desk 1). Desk 2 exhibits the pin connections between the ESP32 board (MOD1) and the 8.89cm (3.5-inch) TFT (MOD2). Desk 3 exhibits the pin connections between the PS2 keyboard and the ESP32 board. Fig. 2 exhibits the circuit diagram of the ChatGPT terminal that talks to the ESP32 board. It’s constructed across the ESP32 board (MOD1), 8.89cm (3.5-inch) TFT (MOD2), MAX98357A (MOD3), 5V voltage regulator 7805 (IC1), PS2 keyboard, and some different parts.

Join the parts and show them in response to the circuit diagram. The voltage regulator used within the circuit powers the machine within the voltage vary of 5V to 9V. Nevertheless, you’ll be able to change the voltage regulator with a 5V-regulated energy adaptor. Artificial intelligence is rapidly reshaping the way we work and interact with technology. From text-based chatbots to voice assistants, AI has become an integral part of our daily lives, enhancing productivity and convenience. Large-scale models like GPT-4o have demonstrated remarkable capabilities in understanding and generating human-like text, but deploying these powerful models in real-time applications remains a challenge, especially in embedded systems.

Embedded AI offers significant benefits, such as localised processing, reduced cloud dependency, and lower latency in real-time applications. These advantages are crucial for applications requiring voice interaction, automation, or accessibility features. Yet, challenges persist—speech recognition and text generation require substantial computational power, making real-time execution difficult on microcontrollers like the ESP32. The recent work by Binh Pham, showcased on his YouTube channel Build With Binh, is a remarkable example of pushing the boundaries of embedded AI. In his latest project, Pham successfully implemented a real-time conversational AI system on an ESP32-powered device. He used the SenseCap Watcher from Seed Studio to run his real-time conversational AI, because of its hardware features such as 32MB of flash, 8MB of PSRAM, built-in Display, built-in microphone and built-in speaker...

The project integrates multiple AI technologies, including Silero for voice activity detection, Whisper for speech-to-text conversion, GPT-4o for text processing, and ElevenLabs for text-to-speech synthesis. This sophisticated pipeline enables the device to engage in natural conversations with users, mimicking the voice and personality of Wheatley, a well-known AI character from Portal 2. By leveraging LiveKit’s real-time pipeline, Pham overcame hardware limitations, allowing smooth interaction despite the constraints of the ESP32 microcontroller.The project stands out not only for its technical achievement but also for its creative execution. Using an open-source SenseCap Watcher device, Pham incorporated an interactive visual display powered by the LVGL library, bringing Wheatley’s animated persona to life. The implementation required deep integration with WebRTC protocols and optimization of real-time audio streaming, demonstrating a blend of software ingenuity and embedded system expertise. But for those who want to experiment with such a project, Binh made his entire project open source.

The source code and written tutorial of the project can be found in his GitHub repository. In this video tutorial, we will be adding a new feature to our ESP32 project using Chat GPT. This new feature will allow us to listen to the responses coming from Chat GPT. We will demonstrate how to connect a speaker to the ESP32 board and convert text to speech using the i2s audio amplifier. We will also provide a step-by-step guide on how to use the Arduino JSON library to fetch and deserialize the responses from the Chat GPT server. By the end of this tutorial, You will be able to integrate the Chat GPT feature into your own projects and listen to the audio feedback.

To get started, we will need an ESP32 board, an i2s audio amplifier module, and a 4 ohm speaker. These components can be easily ordered from our Website. Once you have all the components, you can proceed with the following steps: Before we dive into the Chat GPT feature, let's first learn how to convert any text into speech using the i2s audio amplifier with the ESP32 board. We have provided a sample sketch that demonstrates this functionality. The code is similar to the one used in our internet radio project.

By making a slight modification in the code, we can achieve text-to-speech conversion. The modified code uses the function audio.connectToSpeech to provide the input text in STRING format and specify the language for speech conversion. The code leverages Google Text-to-Speech (TTS) service to convert the text into audio. Once the code is uploaded and executed, you will be able to hear the audio feedback for the provided text. To add the Chat GPT feature, we need to make some changes to our existing code. We will introduce two new libraries: Arduino JSON and audio.h.

These libraries enable us to handle the JSON-formatted responses from the Chat GPT server and extract the required text. In addition to these changes, we will define certain parameters such as the name, password, Chat GPT token, temperature, and max tokens. These parameters will be used in the request made to the Chat GPT server. After receiving the response, we will deserialize the JSON data and extract the answer. The answer will then be printed on the serial monitor and spoken out using the text-to-speech conversion feature. Let's go through the code in more Detail to understand how it works.

First, we import the necessary libraries and define the required parameters. The setup part includes connecting to Wi-Fi and configuring the audio settings through the i2s amplifier. In the loop function, We Prompt the user to ask a question and wait until a question is provided through the serial monitor. Once a question is received, an HTTP POST request is made to the Chat GPT server with the question, temperature, and max tokens. The response from the server, in JSON format, is stored in the payload variable. We use the Arduino JSON library to extract the answer from the JSON response.

The Esp32 Chatgpt Powered Voice Speaker Github

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