Huzaifa7524 / Whisper_small_openai_finetuned_on_arabic_language

Arabic Speech Recognition with Whisper: Fine-tune the Whisper model from OpenAI for Arabic speech recognition tasks. This repository provides scripts and resources for collecting, preprocessing, and fine-tuning an Arabic speech dataset, as well as evaluating and deploying the trained model for inference.

Arabic Speech Recognition with Whisper

Overview

This project aims to perform Arabic speech recognition using the Whisper model, developed by OpenAI. We fine-tune the Whisper model on an Arabic speech dataset, leveraging the Hugging Face Transformers library. The trained model can transcribe Arabic speech audio into text with high accuracy.

How it Works

1. Data Preparation: We start by collecting and preparing an Arabic speech dataset. The dataset should contain audio files along with their corresponding transcriptions.

2. Fine-Tuning: We fine-tune the pre-trained Whisper model on the Arabic dataset using supervised learning. During fine-tuning, the model learns to map input audio features to text transcriptions.

3. Evaluation: After fine-tuning, we evaluate the trained model's performance on a separate test dataset. We measure metrics such as Word Error Rate (WER) to assess the accuracy of the model's transcriptions.

4. Inference: Once the model is trained and evaluated, it can be used for real-world inference tasks. Given a new Arabic speech audio file, the model can transcribe the audio into text.

Key Components

• Whisper Model: The core of the project is the Whisper model, which is a deep learning model specifically designed for speech recognition tasks.
• Hugging Face Transformers: We leverage the Transformers library from Hugging Face, which provides a user-friendly interface for working with state-of-the-art deep learning models, including Whisper.
• Training Script: We provide a training script that automates the process of fine-tuning the Whisper model on the Arabic dataset.
• Evaluation Script: We also provide an evaluation script to measure the performance of the trained model using standard metrics.
• Inference Script: Finally, we offer an inference script that allows users to transcribe Arabic speech audio using the trained model.

Usage

To use the project, follow these steps:

1. Prepare the Arabic speech dataset.
2. Fine-tune the Whisper model on the dataset using the provided training script.
3. Evaluate the trained model using the evaluation script.
4. Use the trained model for inference tasks using the inference script.

Learn More

For a detailed guide on fine-tuning the Whisper model and other advanced techniques, read the blog post on Hugging Face's blog.