The Tech Buffet #5: Build and Deploy a Voice Assistant with LangChain

Create your personalized voice-based ChatGPT

🔒 This issue is accessible to paid members only as it covers an in-depth step-by-step tutorial (with code included) on building and scaling a machine learning app.

Today, I’ll show you how to build and deploy a voice assistant from scratch.

I started this project a few months ago with the initial goal of learning more about LLMs through a hands-on project. (Yes, I wanted to create a voice-based ChatGPT before it recently became a thing).

Little did I know back then that I’d end up also learning about app development, deployment, and DevOps practices.

If you’re curious about these topics and want to extend your ML skills, you may find this issue worth reading.

But before we dive in, here’s what the app looks like:

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To make this tutorial easy to follow, we can decompose the app into the following components:

• A speech-to-text (STT) model to convert the user’s input into text

• An LLM that takes the text as input and generates an answer

• A text-to-speech (TTS) model that converts the LLM’s answer to an audio file and plays it back

We’ll implement each of these components separately, then we’ll assemble everything into a single app and deploy it to the cloud.

To serve the models behind this app, we’ll use BentoML, a framework for serving and deploying ML models.

Then, we’ll deploy everything to BentoCloud. (But this is not the only option)

This issue will guide you through all these steps from local development to deployment. You can reuse every code snippet and adapt it to your projects.

Let’s dive in!