Building a Voice-to-Text and Text-to-Speech App with FastAPI and OpenAI
Hong T, Changlu L, Luchao J., Jing W. and Jing Z.
One of the main motivations behind our project is to bring large language models to a general audience. Allowing users to interact with the system using voice instead of a keyboard offers many advantages. It is more efficient, safer, and appeals to a broader audience, such as seniors or professionals whose work requires constant use of their hands, like factory workers and drivers.
In this blog post, we’ll explore how to build a voice-to-text and text-to-speech system using FastAPI, OpenAI’s Whisper, a text-to-speech engine, and AWS S3 for file storage. We’ll break down the code structure, outline the key steps, highlight the essential tech stacks for setting up the application, and share the insights gained from this project.
Tech Stack Overview
Before we dive into the code, let’s review the tech stack and its roles in the project:
1. FastAPI: A modern, fast web framework for building APIs in Python.
2. OpenAI Whisper & GPT-4: For transcription (voice-to-text) and language model capabilities (text generation).
3. AWS S3: For storing audio files and text files securely in the cloud.
4. Docker: To containerize the application, making it easy to deploy and run on any platform.
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