podanal

A powerful system for analyzing audio content that provides insights such as word count, key topics, sentiment analysis, and concise summaries of podcast conversations.

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📖 Project Overview

PodAnal is an advanced tool designed to process and analyze podcasts and audio files, offering unique capabilities for users:

• Text Extraction from Audio: Accurate conversion of audio files into text.
• Topic and Sentiment Analysis: Identification of key topics and sentiment analysis in conversations.
• Automated Summarization: Generate short, medium, or long summaries based on user needs.
• Advanced Search: Quickly search and highlight keywords in the extracted text.
• Archive Storage: Store audio files along with processed text for easy access.

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🎯 Features and Capabilities

• Text Extraction and NLP Analysis
• Key Topics Identification
• Sentiment Analysis
• Interactive Dashboards: Graphical representation of analysis results.
• Text Summarization: Select summary length (short, medium, long).
• File Storage: Save original and summarized text as files (e.g., TXT, HTML).
• Advanced Search: Search keywords with highlighted results.
• Automatic Podcast Downloads from Online Sources
• Archive Management: Browse podcasts with search and filter options.

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🛠️ Technologies and Tools

Backend

• Programming Language: Golang
• Dependency Management: Go Modules
• Database: PostgreSQL
• NLP Tools: NLTK, spaCy
• Audio Processing: FFmpeg, SpeechRecognition API

Frontend

• Framework: React.js
• Charting Libraries: Chart.js or D3.js for dashboards

DevOps

• Deployment: Docker, Docker Compose
• Monitoring: Prometheus, Grafana
• Web Server: Nginx

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📥 Installation and Setup

1. Clone the repository:

   git clone https://github.com/hadirezaei1377/podanal.git
   cd podanal  

2. Install Docker and run the project using Docker Compose:

   docker-compose up --build  

3. The service will be available at:

   http://localhost:3000  
   

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📋 How to Use

1. Upload Audio Files: Use the UI to upload an audio file.
2. Select Operation:
   • Convert Audio to Text
   • Automated Summarization
   • Topic and Sentiment Analysis
3. Download Results: Download processed results as text or HTML files.

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🧪 Testing and Validation

Run tests:

go test ./...  

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👥 Contributors

this project is open source and you can contribute, It makes me proud.

📜 License

This project is licensed under the MIT License.