Automatic Speech Recognition (ASR) Model

Overview

This Automatic Speech Recognition (ASR) model converts spoken language into text, designed for real-time transcription and voice command applications.

Key Features

• Real-time Transcription: Converts speech to text in real-time with high accuracy.
• Multilingual Support: Recognizes multiple languages and dialects.
• Adaptability: Can be trained on custom datasets for specific use cases.

Technologies Used

• Python: Programming language used to build the model.
• Google Colab: Platform used for training and testing the ASR model.
• TensorFlow: Machine learning framework for building and training the model.

Challenges & Solutions

• Accuracy with Noisy Data: Pre-processed audio data to remove noise and improve transcription accuracy.
• Training Time: Used Google Colab’s GPU to speed up model training.

Future Plans

• Adding support for real-time translation.
• Improving model accuracy with larger and more diverse datasets.