AI-Powered Meeting Summarizer
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AI Powered Meeting Summarizer
Overview :
The AI-Powered Meeting Summarizer is a web application based on Gradio that converts meeting recordings into text using whisper.cpp for audio-to-text conversion and the Ollama server for text summarization. This tool is excellent for quickly extracting key points, decisions, and action items from meetings.
Target Users :
The target audience includes professionals who need to organize meeting notes and quickly extract key points, as well as researchers who need to analyze and summarize large volumes of meeting content. This product or technology is particularly well-suited for users who handle multilingual meeting materials, as it offers translation features.
Total Visits: 474.6M
Top Region: US(19.34%)
Website Views : 52.2K
Use Cases
Meeting notes organization: Users upload meeting recordings, and the system automatically generates meeting summaries and complete text records.
Remote meeting key point extraction: Users upload recordings of remote meetings, and the system provides summaries of key points.
Meeting decisions and action item summaries: Users upload meeting recordings, and the system helps quickly identify decisions and action items from the meeting.
Features
Audio to text conversion: Uses whisper.cpp to convert audio files into text.
Text summarization: Utilizes models on the Ollama server for summarizing text.
Support for multiple models: Compatible with various Whisper models (base, small, medium, large V3) and any available models on the Ollama server.
Translation feature: Allows non-English audio to be translated into English.
Gradio interface: Provides a user-friendly web interface for uploading audio files, viewing summaries, and downloading text.
Requires Python 3.x environment: Ensures compatibility and stability of the Python environment.
FFmpeg (for audio processing): Ensures compatibility of audio file formats.
Whisper.cpp (for audio-to-text conversion): Ensures accurate audio file conversion.
Ollama server (for text summarization): Ensures accuracy and efficiency of text summarization.
Gradio (for web interface): Ensures user-friendliness and ease of use.
Requests (for handling Ollama server API calls): Ensures stability and efficiency of API calls.
How to Use
Step 1: Clone the repository to your local machine.
Step 2: Run the setup script to install all necessary dependencies (including Python virtual environment, whisper.cpp, FFmpeg, and Python packages) and launch the application.
Step 3: Access the application. After setup and execution, Gradio will provide a URL (usually http://127.0.0.1:7860). Open this URL in a web browser to access the meeting summarizer interface.
Step 4: Upload an audio file. Click on the audio upload area and select any supported format (such as .wav or .mp3).
Step 5: Provide additional context (optional). For better summarization, you may provide extra context (e.g., 'Meeting on AI and ethics').
Step 6: Select a Whisper model. Choose one of the available Whisper models (base, small, medium, large V3) for audio-to-text conversion.
Step 7: Choose a summarization model. Select a model from the available options on the Ollama server.
Step 8: Review the results. After uploading the audio file, you will receive summary text generated by the selected model.
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