Skip to main content

My Tool - Songs Renamer

We all have downloaded songs from the internet or ripped songs from CD/DVD and stored on our PC or music player. Whenever we do that, the songs are named as xxx01.mp3, xxx02.mp3 and so on, where 'xxx' stands for the album name. The song's actual name is hidden away in its Advanced Properties -> Title. This is really frustrating because we cannot identify the song. The tool 'Songs Renamer' which I have created helps you solve this problem. It automates the process of renaming of songs by picking up the text available in Title property and applying it in the file name.

Download Songs Renamer


Creative Commons License
Songs Renamer by Mayank Kumar is licensed under a Creative Commons Attribution-No Derivative Works 2.5 India License.

Features

1. Creates a tree structure based on the folder selected for processing. It can process upto n-level of nested folders. However, it may take some time if the nesting is too deep.
2. Allows user to choose a single file or entire folder. User can alter his choice at any point of time.
3. Single click renaming of files from the application interface itself.
4. 'Open' and 'Open containing folder' options available from the right-click context menu from the application interface.

Usage


1. Install and launch the application.


2. Click om 'Browse' button and select the folder where the songs you want to be renamed are kept.



3. Choose the songs you want to rename by selecting the adjacent check box.


4. Click on 'Rename' button. The renaming progress bar is displayed and finally a 'Rename Complete' message is displayed.



5. You are done!!

Installation


Supported Operating Systems -

1. Windows XP
2. Windows Server 2003
3. Windows Vista
4. Windows 7 (Not fully tested yet)

Pre-requisites -

1. Microsoft .NET Framework 2.0
2. Disk Space - 5 MB
3. Memory - 128 MB of RAM


Bug Reporting


In case you encounter any bug, please mail me at mayankthebest[at]gmail[dot]com along with the generated error log file.

Comments

Popular posts from this blog

Creating a Smart Playlist

A few days earlier I was thinking that wouldn't it be nice if I had something which will automatically generate a playlist for me with no artists repeated. Also, it would be nice if I could block those artists which I really hate (like Himesh Reshammiya!). Since I couldn't find anything already available, I decided to code it myself. Here is the outcome -  This application is created entirely in .NET Framework 4/WPF and uses Windows Media Player Library as its source of information. So you have to keep your Windows Media Player Library updated for this to work. It is tested only on Windows 7/Vista. You can download it from here . UPDATE : You can download the Windows XP version of the application here . Please provide your feedback!

Integrating React with SonarQube using Azure DevOps Pipelines

In the world of automation, code quality is of paramount importance. SonarQube and Azure DevOps are two tools which solve this problem in a continuous and automated way. They play well for a majority of languages and frameworks. However, to make the integration work for React applications still remains a challenge. In this post we will explore how we can integrate a React application to SonarQube using Azure DevOps pipelines to continuously build and assess code quality. Creating the React Application Let's start at the beginning. We will use npx to create a Typescript based React app. Why Typescript? I find it easier to work and more maintainable owing to its strongly-typed behavior. You can very well follow this guide for jsx based applications too. We will use the fantastic Create-React-App (CRA) tool to create a React application called ' sonar-azuredevops-app '. > npx create-react-app sonar-azuredevops-app --template typescript Once the project creation is done, we

Serverless Generative AI: How to Query Meta’s Llama 2 Model with Microsoft’s Semantic Kernel and AWS Services

Generative AI is a type of artificial intelligence that can create new content such as text, images, music, etc. in response to prompts. Generative AI models learn the patterns and structure of their input training data by applying neural network machine learning techniques, and then generate new data that has similar characteristics. They are all the rage these days. 😀 Some types of generative AI include: Foundation models , which are complex machine learning systems trained on vast quantities of data (text, images, audio or a mix of data types) on a massive scale. Foundation models can be adapted quickly for a wide range of downstream tasks without needing task-specific training. Examples of foundation models are GPT, LaMDA and Llama . Generative adversarial networks (GANs) , which are composed of two competing neural networks: a generator that creates fake data and a discriminator that tries to distinguish between real and fake data. The generator improves its ability to fool the d