Tag Archives: technology

Free Auto Silence Remover / Slicer – Remove Silence from Videos Automatically

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This post is based on the youtube video I uploaded:

🔗 Related links

🔧 Source code (GitHub): https://github.com/ivmos/SilenceRemover (one of the available repos)

🌐 Try it online

https://silenceslicer.com/ (Jerry Li’s hosted app)

https://silence-remove.vercel.app (vercel deployment example)

Removing Silences from Videos with a Free Open-Source Tool (Local + Vercel Deployment)

Hi friends 👋
In this post, I want to show you the free, open-source tool I currently use to remove silences from my videos. We’ll walk through how it works locally, explore its UI and internals, and finally deploy it to Vercel so you can run it as a hosted solution.

If you create YouTube videos, podcasts, or tutorials, this tool can save you a lot of editing time.

Running the Project Locally

I’m starting directly from the repository. This is a Node.js project, and running it locally is straightforward:

yarn run dev

Once executed, the app runs on a local port and spins up a development server. The local UI is slightly different from the currently hosted version, which makes it ideal for experimentation and debugging.

Analyzing a Video (Silence Detection)

After the app is running, you can simply drag and drop a video file into the interface. I tested it with my previous video about Moises.ai, and the analysis was surprisingly fast.

To better understand what’s happening behind the scenes, I opened the developer tools. You can clearly see FFmpeg being loaded and network activity kicking in while the analysis runs.

Tweaking Detection Parameters

One of the best things about this tool is how configurable it is:

  • Mean volume – controls how quiet a segment must be to count as silence
  • Minimum silence duration – adjusts how long silence must last to be removed

After tweaking these values and clicking Analyze again, you’ll notice different results. Once finished, the app tells you the new duration of the video after silence removal.

Exporting the Result

When you’re happy with the analysis, you can:

  • Export the processed video
  • Export the timeline (useful for further editing)

At this point, everything is handled locally through FFmpeg, without uploading your video anywhere — a big plus for privacy.

Working with the Timeline UI

The UI is honestly one of the highlights of this project.

You get a visual timeline where silence regions are clearly marked. From here you can:

  • Add zones manually
    Click Zone Add and select the part you want to include or modify.
  • Remove zones manually
    Click Zone Delete and simply select the sound you want to remove.

You can immediately play back the result to verify that everything works as expected — and it does, really well.

Deploying to Vercel

Next, I wanted a hosted version, so I deployed the project to Vercel.

Steps:

  1. Go to your Vercel dashboard
  2. Click Import Project
  3. Vercel detects it as a Node.js project automatically
  4. Deploy with default settings

At first, I ran into a deployment error. After copying the error message into ChatGPT and applying a small fix, the deployment worked perfectly.

Once deployed, the app behaves exactly the same as the local version — but now it’s available online under my own Vercel URL.

Quick Look at the Codebase

Since we had some extra time, I explored the code to understand how silence removal actually works.

Tech Stack Overview

  • Node.js
  • UI built with a React-like framework
  • FFmpeg running in the browser
  • WaveSurfer.js for waveform visualization

Key Components

  • VideoEditor component
  • Timeline / waveform component
  • Silence detection logic in the video renderer

How Silence Detection Works

The core logic happens in a helper responsible for silence analysis:

  • It uses WaveSurfer.js with the Regions plugin
  • Regions are automatically extracted based on silence
  • The analyzeRegions helper:
    • Extracts regions
    • Filters them by silence thresholds
    • Produces the final list of segments to keep

FFmpeg is then called with the correct parameters to stitch together only the non-silent parts.

Simple, elegant, and very effective.

Final Thoughts

This tool is a great example of how powerful open-source projects can be when combined with modern web tech. It’s fast, private, configurable, and easy to deploy.

If you edit videos regularly, I highly recommend checking it out and even self-hosting it like I did.

See you in the next video 👋

Exploring Steganography with Hidden Unicode Characters

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In the digital age, where information security is paramount, steganography has emerged as a fascinating and subtle method for concealing information. Unlike traditional encryption, which transforms data into a seemingly random string, steganography hides information in plain sight. One intriguing technique is the use of hidden Unicode characters in plain text, an approach that combines simplicity with stealth.

Related video from my Youtube channel:

What is Steganography?

Steganography, derived from the Greek words “steganos” (hidden) and “graphein” (to write), is the practice of concealing messages or information within other non-suspicious messages or media. The goal is not to make the hidden information undecipherable but to ensure that it goes unnoticed. Historically, this could mean writing a message in invisible ink between the lines of an innocent letter. In the digital realm, it can involve embedding data in images, audio files, or text.

The Role of Unicode in Text Steganography

Unicode is a universal character encoding standard that allows for text representation from various writing systems. It includes many characters, including letters, numbers, symbols, and control characters. Some of these characters are non-printing or invisible, making them perfect for hiding information within plain text without altering its visible appearance.

How Does Unicode Steganography Work?

Unicode steganography leverages the non-printing characters within the Unicode standard to embed hidden messages in plain text. These characters can be inserted into the text without affecting its readability or format. Here’s a simple breakdown of the process:

  1. Choose Hidden Characters: Unicode offers several invisible characters, such as the zero-width space (U+200B), zero-width non-joiner (U+200C), and zero-width joiner (U+200D). These characters do not render visibly in the text.
  2. Encode the Message: Convert the hidden message into a binary or encoded format. Each bit or group of bits can be represented by a unique combination of invisible characters.
  3. Embed the Message: Insert the invisible characters into the plain text at predetermined positions or intervals, embedding the hidden message within the regular text.
  4. Extract the Message: A recipient who knows the encoding scheme can extract the invisible characters from the text and decode the hidden message.

Example: Hiding a Message

Let’s say we want to hide the message “Hi” within the text “Hello World”. First, we convert “Hi” into binary (using ASCII values):

  • H = 72 = 01001000
  • i = 105 = 01101001

Next, we map these binary values to invisible characters. For simplicity, let’s use the zero-width space (U+200B) for ‘0’ and zero-width non-joiner (U+200C) for ‘1’. The binary for “Hi” becomes a sequence of these characters:

  • H: 01001000 → U+200B U+200C U+200B U+200B U+200C U+200B U+200B U+200B
  • i: 01101001 → U+200B U+200C U+200C U+200B U+200C U+200B U+200B U+200C

We then embed this sequence in the text “Hello World”:

H\u200B\u200C\u200B\u200B\u200C\u200B\u200B\u200B e\u200B\u200C\u200C\u200B\u200C\u200B\u200B\u200C llo World

To the naked eye, “Hello World” appears unchanged, but the hidden message “Hi” is embedded within.

Advantages and Disadvantages

Advantages:

  • Subtlety: The hidden information is invisible to the casual observer.
  • Preserves Original Format: The visible text remains unaltered, maintaining readability and meaning.
  • Easy to Implement: Inserting and extracting hidden characters is straightforward with proper tools.

Disadvantages:

  • Limited Capacity: The amount of data that can be hidden is relatively small.
  • Vulnerability: If the presence of hidden characters is suspected, they can be detected and removed.
  • Dependence on Format: Changes in text formatting or encoding can corrupt the hidden message.

Practical Applications

  1. Secure Communication: Concealing sensitive messages within seemingly innocuous text.
  2. Watermarking: Embedding copyright information in digital documents.
  3. Data Integrity: Adding hidden markers to verify the authenticity of text.

Conclusion

Unicode steganography in plain text with hidden characters offers a clever and discreet way to conceal information. By understanding and utilizing the invisible aspects of Unicode, individuals can enhance their data security practices, ensuring their messages remain hidden in plain sight. As with all security techniques, it’s essential to stay informed about potential vulnerabilities and to use these methods responsibly.