Curse Word Bleeper

This is a Mac application that automatically detects curse words in video or audio files and bleeps them out.

Features

• Detects curse words in audio and video files using advanced speech recognition
• Automatically bleeps out detected curse words with adjustable volume
• Extends bleep duration for specific words for extra safety
• Preserves original video content for MP4 files, only modifying the audio track
• Simple graphical user interface for easy use
• Supports various audio and video file formats

Requirements

• Python 3.7+
• FFmpeg (for audio and video processing)
• PyQt6
• whisper (OpenAI's speech recognition model)
• pydub

Installation

1. Clone this repository or download the source code.
2. Install the required Python packages:

pip install PyQt6 openai-whisper pydub

3. Make sure FFmpeg is installed on your system. If not, you can install it using Homebrew:

brew install ffmpeg

Usage

1. Run the application:

python main.py

2. Click the "Select Audio/Video File" button to choose a file for processing.
3. Wait for the processing to complete. The progress bar will show the current status.
4. Once finished, the application will save a new file with "_bleeped" appended to the original filename.

Customizing Curse Words

You can modify the list of curse words by editing the curse_words.txt file. Add or remove words as needed, with one word per line.

Adjusting Bleep Settings

You can customize the bleep settings by modifying the following variables in the BleepWorker class:

• bleep_volume_reduction: Adjust the volume of the bleep sound (in dB).
• extended_bleep_words: Extend the bleep duration for specific words.

Example:

self.bleep_volume_reduction = 6  # Reduce bleep volume by 6 dB
self.extended_bleep_words = {"cunt": 0.2, "fuck": 0.1}  # Extend bleep by 0.2s for "cunt" and 0.1s for "fuck"

Handling Misrecognized Words

The application includes a dictionary to map commonly misrecognized words to their intended curse words. You can modify this in the BleepWorker class:

self.misrecognized_words = {
    "con": "cunt",
    # Add more misrecognized words here if needed
}

Note

This application uses the Whisper speech recognition model, which may require a significant amount of processing power and time depending on the length of the audio/video file. The accuracy of curse word detection depends on the quality of the audio and the accuracy of the speech recognition.

License

This project is open-source and available under the MIT License.