AI tutorial

ElevenLabs Tutorial: Your Guide to Building a Voiced AI

Build a brainstorming partner app using ElevenLabs API and speech technology.

Introduction: Unleash the Potential of ElevenLabs API

Welcome to this in-depth tutorial where you'll unlock the full potential of the Eleven Labs API to create a dynamic AI-powered brainstorming partner app. In this section, we'll introduce you to the exciting world of the Eleven Labs API and give you a glimpse of what's ahead in this comprehensive guide.

Are you ready to embark on a journey into the realm of AI-driven creativity? The Eleven Labs API opens the door to endless possibilities, allowing you to harness the power of AI speech synthesis for your unique applications. In the following sections, we'll dive deep into the technologies that form the foundation of our project. Let's begin our exploration of the Eleven Labs API, Anthropic's Claude, ReactJS, and Flask as we build your AI-powered brainstorming partner app step by step.

Introduction to ElevenLabs and Speech Synthesis

ElevenLabs is a voice technology research company that develops "the most compelling AI speech software for publishers and creators." Their goal is to instantly convert spoken audio between languages by making on-demand multilingual audio support a reality across education, streaming, audiobooks, gaming, movies, and even real-time conversation. They offer a suite of tools ranging from voice cloning to designing synthetic voices, with their main offering being text-to-speech models which rely on high compression and context understanding.

Understanding Anthropic's Claude: Your Chatbot's AI Engine

Anthropic is an AI safety and research company. This means that alongside developing products such as AI models, they also place emphasis on the safety, interpretability, and reliability of AI systems. They believe AI has the potential to fundamentally change how the world works. By promoting AI safety on an industry-wide scale, they aim to build frontier AI systems that are reliable, interpretable, and steerable.

Based on their research, they launched their product, an AI model called Claude. Claude is accessible via a chat interface and free-to-use API capable of a wide variety of conversational and text processing tasks while maintaining a high degree of reliability and predictability. Users describe Claude's answers as "detailed and easily understood, feeling like natural conversation". For this reason, we have chosen Anthropic's Claude to power our brainstorming partner chatbot app.

Leveraging ReactJS for Intuitive User Interfaces

ReactJS is a JavaScript/TypeScript library for developing user interfaces that is particularly popular for single-page applications. It allows developers to build both simple and complex apps from the ground up by utilizing a component-based architecture. One especially popular feature of React is its simple but powerful state management, which updates and renders specific parts of the UI without requiring full-page reloads. Therefore, we will use ReactJS to build the frontend of our brainstorming partner chatbot app.

Building a Robust Backend with Flask

Flask is a lightweight and easy-to-use web framework based on Python. Developers can start small by adding a few routes and develop the app further by integrating additional functionalities along the way. This tutorial leverages Flask to build the backend of our chatbot app, as many libraries such as anthropic and elevenlabs are primarily available for Python.

Prerequisites

Before diving into this tutorial, it's advisable to have basic knowledge of:

  • Python, preferably Flask.
  • TypeScript and ReactJS.
  • Access to the ElevenLabs API.
  • Access to Anthropic's Claude API.

Outline

Here's an overview of what we'll cover in this tutorial:

  1. Initializing the Projects
  2. Building the Backend
  3. Testing the Backend
  4. Building the Frontend
  5. Testing the Brainstormy App

Step-by-Step Tutorial: How to Use Eleven Labs API

Step 1: Initializing the Projects

First thing's first, let's initialize our projects for our chatbot app, which we will call "Brainstormy". In this tutorial, we will create two separate projects: one for the backend and one for the frontend.

Initializing the Backend

  • Create a new directory for your project: We will name the backend of our project brainstormy-backend. Let's create the directory and navigate into it.
  • Create a new virtual environment: Next, we will create our virtual environment, referred to as env.
  • Activate the virtual environment: The command varies depending on the operating system.
    • On macOS and Linux:
    • On Windows:
  • Install Flask: Now that our virtual environment is activated, we can install our dependencies, starting with Flask.
  • Create a new Flask application: Let's create a new file called app.py and edit it with our favorite IDE. We will add the following code to create a Flask app with a route that returns Hello, World!.

Initializing the Frontend

  • Create a new ReactJS application: We can create a new ReactJS application using create-react-app. Ensure that Node.js is installed.
  • Installing TailwindCSS for Styling: In the brainstormy-client project, run commands to install TailwindCSS via npm and initialize the library.
  • Start the ReactJS application: Navigate into your new project directory and start the application:
  • The command should run the app in development mode, automatically opening it in your default browser.

Step 2: Building the Backend

Now, let's delve into the coding part! We will begin by adding handlers for our backend's endpoints. First, we'll install the remaining dependencies.

Installing the Necessary Libraries

Run the command to install all required libraries, including flask-cors, anthropic, elevenlabs, and locked version of pydantic to avoid compatible issues.

Editing the app.py file

  • Import necessary modules: Import libraries such as os, flask, elevenlabs, and anthropic, along with CORS for handling cross-origin requests.
  • Initialize Flask application: Set up CORS to allow requests from http://localhost:3000 and http://example.com.
  • Set API key for ElevenLabs: Retrieve the API key from environment variables and configure it in the library.
  • Define route for generating chat response: Create a /chat endpoint that accepts conversation history and returns Claude model's response.
  • Define route for generating speech: Create a /talk endpoint for generating speeches from chat responses.
  • Run the Flask application: Start the Flask app with debug mode enabled for easier troubleshooting.

Editing the .env file

Store your API keys in the .env file for security.

Step 3: Testing the Backend

Verify that the backend functions correctly using tools like Postman or Insomnia to send POST requests to the Flask routes.

/chat endpoint

Test the /chat endpoint by sending a JSON payload with a list of message objects to receive ideas about innovating the healthcare system.

/talk endpoint

Test the /talk endpoint by sending the response from the /chat endpoint as a script for speech generation via ElevenLabs.

Step 4: Building the Frontend

Now that the backend is complete, let's create the frontend. Our focus will be on two components: the App component and the Chatbot component.

App.tsx

Import the Chatbot component and include it inside the element.

Chatbot.tsx

Declare import statements and create interfaces for messages. Handle states such as messages, input, loading status, and an audio player.

Include event handlers for updating input state and sending requests to the /chat endpoint to retrieve and play audio responses.

public/index.html

Update the title of the app to Brainstormy for an engaging user experience.

Step 5: Testing the Brainstormy App

Once the front end is complete, interact with the chatbot, send various messages and check the responses. Verify the interactivity and functionality of the app by ensuring the synthesized speech plays successfully.

Conclusion

In this tutorial, we demonstrated how to use the ElevenLabs API to integrate AI-generated voice into our AI-powered brainstorming partner app. By leveraging Anthropic's Claude model, we ensured more engaging and human-like responses for our ideas, making it an ideal solution for a brainstorming partner app.

By delivering the generated voice as an audio file, we were able to seamlessly incorporate the voice into our front-end app, automatically triggering the audio response upon receipt. This enhances the interactivity of our brainstorming partner app, fostering more engaging discussions.

Reading next

Illustration of Qdrant vector search engine with AI integrations.
GPT-4o tutorial image showcasing chatbot features.

Leave a comment

All comments are moderated before being published.

This site is protected by hCaptcha and the hCaptcha Privacy Policy and Terms of Service apply.