https://github.com/latent-variable/real_time_fallacy_detection
Real-time Fallacy Detection using OpenAI whisper and ChatGPT/LLaMA/Mistral
https://github.com/latent-variable/real_time_fallacy_detection
Last synced: 5 months ago
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Real-time Fallacy Detection using OpenAI whisper and ChatGPT/LLaMA/Mistral
- Host: GitHub
- URL: https://github.com/latent-variable/real_time_fallacy_detection
- Owner: latent-variable
- License: mit
- Created: 2023-10-02T04:02:50.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-10T00:09:04.000Z (over 1 year ago)
- Last Synced: 2024-08-02T01:25:15.084Z (9 months ago)
- Language: Python
- Homepage:
- Size: 3.52 MB
- Stars: 101
- Watchers: 3
- Forks: 10
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
- awesome-ChatGPT-repositories - Real_time_fallacy_detection - Real-time Fallacy Detection using OpenAI whisper and ChatGPT/LLaMA/Mistral (Langchain)
README
# Real-time Fallacy Detection
## Overview
This project aims to perform real-time fallacy detection during events like presidential debates. It uses the [Whisper](https://github.com/openai/whisper) for audio transcription. For natural language understanding and fallacy classification you have the option to use the OpenAI ChatGPT API or a local LLM through the [text-generation-webui](https://github.com/oobabooga/text-generation-webui). I was able to run both whisper with the [IconicAI_NeuralHermes-2.5-Mistral-7B-exl2-5bpw](https://huggingface.co/IconicAI/NeuralHermes-2.5-Mistral-7B-exl2-5bpw) on a laptop with 3080TI 16GB of VRAM.
[Watch Video](https://www.youtube.com/watch?v=I9ScRL_10So)
## Features
- **Real-time Audio Transcription**: Uses OpenAI's Whisper ASR model for accurate real-time transcription locally or through API access.
- **Fallacy Detection**: Utilizes OpenAI's ChatGPT to classify and identify fallacies in real-time.
- **Overlay Display**: Provides a transparent overlay display to show both the transcription and fallacy detection results.
- **Text analysis**: using GPT-3/4 or local LLM.
## Dependencies
- Anaconda
- PyQt5 - Overlay
- PyAudio - Audio processing
- openai-whisper - ASR
- openai - ChatGPT API
- torch with cuda, for real time transtriction
- Have the text-generation-webui running with the API flag
## Installation
I build the application using Anaconda with python 3.9 on windows
1. Clone the repository:
```
git clone https://github.com/latent-variable/Real_time_fallacy_detection.git
```
2. Navigate to the project directory:
```
cd real-time-fallacy-detection
```
3. Create a conda environment:
```
conda create --name rtfd python=3.9
```
4. Activate the created environment:
```
conda activate rtfd
```
5. Install the required packages:
```
pip install -r requirements.txt
```
6. (Optional) Install the required packages to run whisper locally:
```
pip install -r requirements_local_whisper.txt
```
7. Installing [VB-Audio](https://vb-audio.com/Cable/), to forward audio ouput as an input device (*Optional, but I don't know how to redirect audio otherwise)
## Usage
Run the main script to start the application:
```
python main.py
optional arguments:
-h, --help show this help message and exit
--auto Automatically get commentary
--api_whisper Run whisper through api instead of locally
--api_gpt Will use use gpt api otherwise, by defualt will use a local LLM through the text-generation-webui API
```
If you plan to use your local LLM, please have the text-generation-webui running with the --api flag
**Note**: The application routes the audio to VB-AUDIO for processing and then redirects it back to the user for playback.
## Arguments
--use_gpt(3/4): Use this flag to toggle between ChatGPT with or without GPT4. Default is to use local LLM.
## Display
The application will display an overlay with two sections:
- **Top Box**: Displays the fallacy classification from ChatGPT.
- **Bottom Box**: Displays the real-time transcription from the Whisper API.
Press the `Esc` key to close the application.
## Configuration
[Audio Settings]
You can configure the audio input and outsource in the `settings.ini`.
- **device_input_name = VB-Audio** <- must have
- **device_output_name = Headphones (2- Razer** <- replace with your own
*Note: when the application loads if will redirect the audio back to the output device
[Local LLM Settings]
- **instruction_template = mistral-openorca** <- replace with the model specific template
*Note: this is a custom template, which you will likely note have in your text-generation-webui
Rename the `api_key.txt.template` to `api_key.txt` and add your OpenAI API key to it.
## License
This project is licensed under the MIT License.