Project Summary

This project focuses on building an audio digit classification system using Python. The main goal is to recognize spoken digits from audio files using machine learning techniques.

Problem Statement

Accurately identifying spoken digits from audio recordings is a challenging task due to variations in speech, background noise, and recording quality. The project aims to develop a robust solution for digit recognition from audio data.

Dataset

Link to HugginFace dataset: https://huggingface.co/datasets/mteb/free-spoken-digit-dataset/viewer/default/train?views%5B%5D=train

Solution Overview

The solution involves:

• Preparing and processing an audio digit dataset.
• Designing and training a neural network model for classification.
• Evaluating the model's performance.
• Providing utility functions for data handling and preprocessing.

Process Breakdown

1. Data Preparation (audio_digit_dataset.py)

• Loads and preprocesses audio files containing spoken digits.
• Extracts features (e.g., MFCCs) suitable for machine learning.
• Splits data into training and testing sets.

2. Model Definition (audio_model.py)

• Implements a neural network architecture tailored for audio classification.
• Defines layers, activation functions, and output for digit prediction.

3. Training and Evaluation (digit_classifier.py)

• Trains the model using the processed dataset.
• Evaluates accuracy and performance on test data.
• Handles model saving and loading for reuse.

4. Utilities (utils.py)

• Provides helper functions for audio processing, feature extraction, and data management.
• Ensures reproducibility and efficient workflow.

Conclusion

By integrating data preparation, model design, training, and utility functions, this project delivers an end-to-end solution for audio digit classification. The modular structure allows easy adaptation and extension for similar audio recognition tasks.