Building & Deploying a Speech Recognition System Using the Whisper Model & Gradio

Author(s): Youssef Hosni

Originally published on Towards AI.

Speech recognition is the task of converting spoken language into text. This article provides a comprehensive guide on building and deploying a speech recognition system using OpenAI’s Whisper model and Gradio.

The process begins with setting up the working environment, including the installation of necessary packages such as HuggingFace’s transformers and datasets, as well as soundfile, librosa, and radio.

The dataset used is the LibriSpeech corpus, loaded from the HuggingFace dataset hub. Detailed instructions are provided for exploring and listening to the dataset samples.

Next, the article explains how to construct a Transformers pipeline utilizing the distilled version of the Whisper model, optimized for faster and smaller speech recognition tasks while maintaining high accuracy. The deployment section demonstrates how to create a user-friendly web application using Gradio.

This application allows for real-time speech transcription via microphone input or uploaded audio files. The final product is a robust, interactive interface for speech-to-text conversion, complete with step-by-step code examples and deployment instructions.

Setting Up Working EnvironmentPreparing the DatasetBuild Transformers PipelineDeploy Application Demo with Gradio

Most insights I share in Medium have previously been shared in my weekly newsletter, To Data & Beyond.

If you want to be up-to-date with the frenetic world of AI while also feeling inspired to… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI