Realtime Censor is a Python application that captures live audio from your microphone, transcribes it in real time using state-of-the-art speech-to-text models, and censors (mutes) any detected swear words before the audio is played through your speakers. The application also displays a live transcription with censored words highlighted, providing both audio and visual feedback.

• Python 3.10+
  The main programming language for the application.

• sounddevice
  For real-time audio capture from the microphone and playback to the speakers.

• NumPy
  For efficient audio data manipulation.

• RealtimeSTT
  Handles real-time speech-to-text transcription, supporting models like Whisper and faster-whisper.

• CTranslate2
  Backend for running Whisper and faster-whisper models efficiently.

• better_profanity
  For fast and accurate detection of swear words in transcribed text.

• Rich
  For beautiful, colorized live transcription display in the terminal.

• colorama
  For cross-platform colored terminal output.

1. Captures Audio:
   Continuously records audio from your microphone in small chunks.

2. Transcribes Speech:
   Uses a real-time local speech-to-text engine (like Whisper or faster-whisper) to transcribe each chunk of audio as you speak.

3. Detects Swear Words:
   Checks each transcribed chunk for the presence of swear words using a customizable profanity filter.

4. Censors Audio:
   If a swear word is detected, after a short delay, the corresponding audio chunk is muted before being played through your speakers, ensuring that offensive language is not broadcast.

1. Install dependencies:

   pip install -r requirements.txt
   

2. Run the application:

   python main.py
   

3. Speak into your microphone:

   • Your speech will be transcribed and displayed in real time.
   • If you say a swear word, it will be muted in the audio output and highlighted in the transcription.

• Swear Words List:
  You can expand or modify the list of censored words in the SWEAR_WORDS set in main.py.

• Model Selection:
  You can choose different Whisper or faster-whisper models for speed/accuracy trade-offs using command-line arguments.

• Audio Devices:
  Specify input/output devices using --input-device and --output-device arguments.

• OpenAI Whisper, faster-whisper, and CTranslate2 teams for speech-to-text technology.
• The authors of sounddevice, better_profanity, and Rich