Harness the power of advanced language processing to transcribe, condense, and summarize lengthy audio recordings from group meetings, classes, webinars, movies, YouTube videos, and much more! This repository offers an open-source solution for effectively converting extensive audio content into concise and informative textual summaries.
With Whisper Audio Transcription, AudioReportAI efficiently converts any audio content into clear and accurate transcripts. The Smart Summarization feature takes these detailed transcripts and transforms them into compact summaries that highlight only the essential information. The platform boasts Diverse Audio Support, making it suitable for everything from business meetings to class recordings, webinars, movies, YouTube videos, and much more! Thanks to its Integrated Workflows, users can seamlessly work with a range of open-source models, optimizing both the transcription and summarization processes. Furthermore, the system offers Diverse Model Support, including but not limited to Meta’s LLaMa, Facebook/bart-large-cnn, OpenAI GPT, OpenAI Whisper, Hugging GPT, Hugging Face, and GPT-J. Lastly, the core Tech Stack of AudioReportAI is primarily built on Python, leveraging powerful libraries and frameworks like scikit-learn, tensorflow, pandas, matplotlib, and numpy.
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🎙️ Whisper Audio Transcription: Efficiently convert any audio content into a clear and accurate transcript.
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🗂️ Smart Summarization: Transform detailed transcripts into compact summaries, highlighting only the essential information.
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🎬 Diverse Audio Support: Suitable for meetings, class recordings, webinars, movies, YouTube videos, and much more.
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🔗 Integrated Workflows: Seamlessly integrate with a range of open-source models to optimize the transcription and summarization processes.
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🧠 Diverse Model Support: Support for Meta’s LLaMa, Facebook/bart-large-cnn, OpenAI GPT, OpenAI Whisper, Hugging GPT, Hugging Face, GPT-J, and much more!
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🛠️ Tech Stack: Primarily built on Python, utilizing libraries and frameworks such as scikit-learn, tensorflow, pandas, matplotlib, and numpy.
Follow these easy steps to get AudioReportAI up and running:
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📍 Navigate to the Project Directory:
After cloning the repository, open a terminal and navigate to the AudioReportAI directory:
cd path/to/AudioReportAI
* Replace "path/to/AudioReportAI" with the actual path to the cloned repository on your machine. -
🌍 Set Up Virtual Environment:
If you haven't already, create a Python virtual environment:
python3.9.7 -m venv .venv -
🌱 Activate the Environment:
To activate your virtual environment:
source .venv/bin/activate -
🔧 Install Dependencies:
Ensure you've installed all necessary packages:
pip install -r requirements.txt -
📁 Prepare Your Files:
Place your desired video or audio file into the "input/" directory. -
🎉 Run the Program:
Now, execute the main code:
python -m code.main
Enjoy the seamless audio transcription and summarization!
🚀 Propel the future of smart audio transcription and summarization! Support the development of AudioReportAI on Patreon and be a catalyst in the AI revolution. Every bit counts towards a smarter tomorrow. Your contribution will help accelerate research, foster innovations, and ensure the continuous enhancement of this project!
patreon: vincbeaulieu
ko_fi: vincbeaulieu
