Building & Deploying a Speech Recognition System Using the Whisper Model & Gradio
Last Updated on June 3, 2024 by Editorial Team
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
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