parisa-bagherzadeh/voice-classification

Voice Classification

This repository contains code and resources for voice classification, a machine learning task that involves categorizing audio samples into different classes based on their acoustic features.

Table of Contents

• Introduction
• Installation
• Usage
• Contributing

Introduction

Voice classification is a common task in the field of speech processing and machine learning. It involves training a model to recognize and categorize different types of voice samples, such as male vs. female, adult vs. child, or different languages.

This repository provides code and resources for building and training voice classification models using popular machine learning framework, TensorFlow.

Installation

To use the code in this repository, you will need to have Python installed on your system. You can install the required dependencies by running the following command:

pip install -r requirements.txt

This will install all the necessary packages for running the code in this repository

Usage

The main components of this repository include:

• Data preprocessing scripts
• Model training scripts
• Evaluation scripts

To use these components, simply run the corresponding Python scripts with your input data. You can also modify the code to fit your specific use case or dataset.

You can download the dataset I've gathered and preprocessed from this link or you can use your own data to train the model.

Preprocess

Put your data in raw_data folder and run the following command

python preprocess.py --raw_data_path raw_data --min_silence_len YOUR_ARGUMENT --silence_thresh YOUR_ARGUMENT --chunk_duration YOUR_ARGUMENT   

The preprocessed data will be saved in dataset folder.

Train

To train the model on your own data, run the following command

python model.py

It will train the model and save the weights in model folder

Evaluation

To test the model run the following command:

python inference.py --model .h5 file --wav_file Test_wav_file

Contributing

If you would like to contribute to this repository, feel free to submit a pull request with your changes. I welcome contributions from the community and are open to new ideas and improvements.