Integrating GPT TTS Model with Nodejs

Posted By :Mohd Ubaish |25th February 2024

Integrating GPT TTS Model with Node.js Application: A Step-by-Step Guide

In this tutorial, we'll explore how to integrate the GPT Text-to-Speech (TTS) model provided by OpenAI into a Node.js application. This integration will enable your application to generate speech from text using the powerful GPT model. We'll also cover how to upload the generated audio to an Amazon S3 bucket. Let's dive in!


Before we start, ensure you have the following:

Basic understanding of JavaScript and Node.js.
Node.js installed on your machine.
Access to the OpenAI GPT API.
An Amazon S3 bucket set up for storing the generated audio files.

Step 1: Setting Up Your Node.js Project
Begin by creating a new directory for your Node.js project and initializing a new Node.js project inside it:


mkdir gpt-tts-nodejs
cd gpt-tts-nodejs
npm init -y


Step 2: Installing Dependencies
Install the necessary dependencies for your project. You'll need the openai and aws-sdk packages:


npm install openai aws-sdk dotenv


Step 3: Environment Configuration
Create a .env file in your project directory to store your environment variables, including your OpenAI API key and AWS S3 bucket credentials:



Replace the placeholder values with your actual API keys and bucket details.

Step 4: Writing the Integration Code
Create a new file named gptTtsIntegration.js in your project directory and add the following code:


const openai = require('openai');
const AWS = require('aws-sdk');

// Configure OpenAI SDK with your API key
openai.apiKey = process.env.OPENAI_API_KEY;

// Configure AWS SDK with your credentials
const s3 = new AWS.S3({
    accessKeyId: process.env.S3_ACCESS_KEY_ID,
    secretAccessKey: process.env.S3_SECRET_ACCESS_KEY,
    region: process.env.S3_BUCKET_REGION,

const GptTts = async (prompt) => {
    try {
        // Generate speech using GPT TTS model
        const mp3 = await{
            model: "tts-1",
            voice: "shimmer",
            input: prompt,

        // Convert audio to a buffer
        const file = Buffer.from(await mp3.arrayBuffer());

        // Generate a unique file name
        const fileName = `${GenerateUniqueKey()}.mp3`;

        // Define content type
        const contentType = 'audio/mp3';

        // Upload the file to S3 bucket
        const url = await UploadToS3(fileName, file, contentType);

        return url;
    } catch (error) {
        console.error('Error generating speech:', error.message);
        throw new Error('Failed to generate speech');

// Function to generate a unique key
const GenerateUniqueKey = () => {
    return Math.random().toString(36).substr(2, 9);

// Function to upload file to S3 bucket
const UploadToS3 = async (fileName, file, type) => {
    return new Promise((resolve, reject) => {
        const params = {
            Bucket: process.env.S3_BUCKET_NAME,
            Key: fileName,
            Body: file,
            ContentType: type,

        s3.upload(params, (err, data) => {
            if (err) {
                console.error('Error uploading to S3:', err);

module.exports = GptTts;


Step 5: Integrating GPT TTS with Your Application
Now that you've implemented the GPT TTS integration code, you can integrate it into your Node.js application. Import the GptTts function into your application's code wherever you need to generate speech from text, and call it with the desired text prompt.


Step 6: Testing Your Integration
You can test your GPT TTS integration by calling the GptTts function with a text prompt and observing the generated speech URL. Ensure that your OpenAI API credentials and AWS S3 bucket details are correctly set up in your environment variables.


Congratulations! You've successfully integrated the GPT TTS model with your Node.js application, allowing you to generate speech from text using OpenAI's powerful GPT model. With this integration, you can enhance your applications with natural-sounding synthesized speech. Feel free to customize and extend this integration based on your specific requirements and use cases.



About Author

Mohd Ubaish

Mohd Ubaish is a highly skilled Backend Developer with expertise in a wide range of technologies, including React, Node.js, Express.js, MongoDB, and JavaScript.With a deep understanding of both front-end and back-end development, he is currently dedicated to the development of TripCongo Web Discovery.He is committed to creating a user-friendly experience on the frontend while ensuring the seamless functionality of the backend. By staying up-to-date with the latest industry trends and advancements, he strives to provide innovative solutions and optimize the performance of the application.

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