A Python-based desktop application with a Tkinter GUI to automatically detect and remove silent segments from audio and video files. It provides a visual waveform display with highlighted silences and allows users to customize detection parameters.

• Load Audio/Video Files: Supports common audio (MP3, WAV, FLAC) and video (MP4, MKV, AVI) formats.
• Waveform Visualization: Displays the audio waveform for visual analysis.
• Silence Detection:
  • Adjustable Noise Level Threshold: Define what's considered silence based on a percentage of the maximum RMS.
  • Adjustable Minimum Silence Duration: Set the shortest duration (in milliseconds) that qualifies as a silence gap.
  • Configurable Offsets: Add padding (in milliseconds) before (Offset In) and after (Offset Out) each detected silence segment to fine-tune cutting.
  • Lock Offsets: Option to keep "Offset In" and "Offset Out" values synchronized.
• Visual Feedback: Detected silence gaps are clearly marked on the waveform.
• Zoom and Scroll:
  • Zoom into the waveform for detailed inspection.
  • Scroll through the waveform when zoomed in.
• Output Options:
  • Saves the processed file with silences removed.
  • For video inputs, allows saving as a new video (MP4 with cut audio) or extracting the processed audio (MP3).
  • For audio inputs, saves as processed audio (MP3).
• Progress and Status: Provides real-time progress updates during loading, detection, and saving operations.
• User-Friendly Interface: Intuitive GUI built with Tkinter.

• Python 3.12: The application is written in Python.
• FFmpeg: Required for audio/video processing, especially for cutting video segments and converting audio formats.
  • Download and install FFmpeg from ffmpeg.org.
  • Important: Ensure FFmpeg is added to your system's PATH environment variable so it can be called from the command line.
• Python Libraries: Listed in requirements.txt.

1. Clone the repository:

   git clone https://github.com/rudra-mondal/silence-cutter.git
   cd silence-cutter

2. Install FFmpeg: Follow the instructions on the FFmpeg website for your operating system. Verify FFmpeg installation by opening a terminal or command prompt and typing:

   ffmpeg -version

   You should see version information if it's installed correctly and in your PATH.

3. Create a virtual environment (recommended):

   python -m venv .venv

   Activate the virtual environment:

   • On Windows:

     .\.venv\Scripts\activate

   • On macOS/Linux:

     source .venv/bin/activate

4. Install Python dependencies:

   pip install -r requirements.txt

1. Run the application:

   python main.py

2. Choose a File:

   • Click the "Choose File" button.
   • Select an audio (e.g., .mp3, .wav) or video (e.g., .mp4, .mkv) file.
   • The waveform will be loaded and displayed.

3. Adjust Parameters (Optional):

   • Noise Level Threshold (%): Use the slider or spinbox to set the sensitivity for silence detection. A lower value detects quieter sounds as silence.
   • Min Silence Duration (ms): Set the minimum length of a silent segment to be detected.
   • Offset In (ms) / Offset Out (ms): Adjust these to add a small buffer before or after the detected silence. This can prevent cutting too close to speech.
   • Lock Offsets: Check this box to make "Offset Out" automatically match "Offset In".

4. Detect Silence:

   • Click the "Detect Silence" button.
   • The application will analyze the audio and highlight the detected silent segments in red on the waveform.
   • The number of detected gaps will be displayed.

5. Review and Zoom/Scroll (Optional):

   • Use the "Zoom" slider to get a closer look at the waveform and the detected silences.
   • Use the "Scroll" slider to move through the waveform when zoomed in.

6. Save Output:

   • Click the "Save Output As..." button (this button is enabled after a file is loaded).
   • Choose a location and filename for the output.
     • If the input was a video, you can choose to save as .mp4 (video with cut audio) or .mp3 (audio only).
     • If the input was audio, the default output is .mp3.
   • The application will process the file and save the version with silences removed. Progress will be displayed.
   • A confirmation message will appear upon completion.

Contributions are welcome! If you have suggestions for improvements or find any bugs, please feel free to:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeature or bugfix/YourBugfix).
3. Make your changes.
4. Commit your changes (git commit -m 'Add some feature').
5. Push to the branch (git push origin feature/YourFeature).
6. Open a Pull Request.

This project is licensed under the MIT License - see the LICENSE file for details.