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🦆 Duck Pod

Automated Podcast Audio Post-Production & Ambient Ducking Tool

Duck Pod is a high-performance batch script designed to turn raw voice recordings into polished, "radio-ready" podcast segments. It automates noise reduction, voice leveling, and dynamic music/ambience ducking in a single workflow.

✨ Key Features

  • Dynamic Sidechain Compression: Automatically ducks background music and ambience whenever the speaker is talking.
  • Layered Backgrounds: Supports a secondary looping "Ambience" track (e.g., rain, cafe noise) that ducks in sync with the music.
  • Pro Voice Chain: Built-in FFT noise reduction (afftdn), high-pass filtering (80Hz), and EBU R128 loudness normalization (loudnorm).
  • Infinite Looping: Background tracks automatically loop to match the length of your voice recording.
  • The "Reverse-Fade" Trick: Uses a multi-pass reversal technique to ensure perfectly smooth 5-second fades at the start and the absolute end of the file, regardless of duration.
  • Automatic Versioning: Every export is timestamped (YYYYMMDD_HHMMSS) to prevent overwriting previous takes.

🚀 How to Use

  1. Setup: Place your background music in a folder named /music and your ambient textures in a folder named /ambience (located in the same directory as the script).
  2. Input: Drag and drop your raw voice recording (.wav, .mp3, or .m4a) directly onto DuckPod.bat.
  3. Output: Your finished, mastered file will appear in the same directory as your source file with a unique timestamp.

🛠 Requirements

  • FFmpeg: This script requires FFmpeg to run. You can either:
    • Add ffmpeg.exe to your system's PATH.
    • OR simply place ffmpeg.exe and ffprobe.exe in the same folder as the DuckPod.bat script.

📁 Folder Structure

/YourProject/
├── DuckPod.bat
├── ffmpeg.exe (optional if not in PATH)
├── music/
│   └── (your background mp3s)
└── ambience/
    └── (your looping ambient mp3s)

About

Automated Podcast Audio Post-Production & Ambient Ducking Tool - Duck Pod is a high-performance FFmpeg-based batch script designed to turn raw voice recordings into polished, "radio-ready" podcast segments. It automates the tedious tasks of noise reduction, voice leveling, and—most importantly—dynamic music and ambience ducking.

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