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NomoNotes

NomoNotes is a tool designed to streamline your note-taking process during meetings, lectures, podcasts etc. It transcribes audio in real-time, summarizes the transcript using a language model, and saves the summary directly into Apple Notes.

Features

  • Real-time Audio Transcription: Utilizes Whisper for accurate and efficient transcription (code from https://github.com/davabase/whisper_real_time)
  • Summarization: Summarizes the transcript using an LLM.
  • Apple Notes Integration: Saves the summary into Apple Notes.

Installation

  1. Clone the repository:
    git clone https://github.com/yourusername/nomonotes.git
    cd nomonotes
    
  2. Install the required dependencies: pip install -r requirements.txt

Usage

Run the script with the following command: python transcribe.py --model medium --llm_model gpt-4o --summary_prompt meeting_prompt --notes_folder_name "NOMO Notes" To end the transcription, just keyboard interrupt the process. The script will then start generating your summary and save it to Apple Notes.

Command Line Arguments

  • --model: Model to use for transcription (choices: tiny, base, small, medium, large).
  • --non_english: Use non-English model.
  • --energy_threshold: Energy level for mic to detect.
  • --llm_model: LLM model to use for summarization (this should either be an OAI model or a .gguf model you've downloaded to models/)
  • --summary_prompt: Path to the summary prompt (this is the name of the file with the prompt in the prompts/ folder)
  • --notes_folder_name: Name of the folder where the note will be created.

Example

`python transcribe.py --model medium --llm_model gpt-4o --summary_prompt meeting_prompt --notes_folder_name "NOMO Notes"`

Streamlit

If you prefer a UI, use streamlit:

python -m streamlit run ui.py

Use your own models

You can use your own local models for summarization. For example, if you have a fine-tuned model that summarizes meetings and podcasts in a specific format, you can specify it using the --llm_model argument.

To use an off-the-shelf llama, you can run run_llama.sh which downloads and runs the script with an 8b llama model.

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