update Readme

README.md

@@ -17,4 +17,121 @@ license: apache-2.0
 Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
 
 If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+forums](https://discuss.streamlit.io).
+
+
+🎤 English Accent Analyzer
+Streamlit App
+PyTorch
+
+A tool to identify English accents from audio/video sources with optimized processing for large files.
+
+🚀 Features
+Supports local files, direct media URLs, and Loom videos
+
+Automatically splits large files into 1-minute chunks
+
+Early stopping for faster analysis
+
+Confidence-based predictions
+
+Interactive Streamlit dashboard
+
+⚙️ Installation
+Clone the repository:
+
+bash
+git clone https://github.com/your-username/accent-analyzer.git
+cd accent-analyzer
+Install dependencies:
+
+bash
+pip install -r requirements.txt
+Install FFmpeg (required for audio processing):
+
+bash
+# On Ubuntu/Debian
+sudo apt install ffmpeg
+
+# On macOS
+brew install ffmpeg
+🖥️ Usage
+Run the Streamlit app:
+
+bash
+streamlit run app.py
+The app will open in your browser at http://localhost:8501
+
+📥 Input Options
+1. Upload a file
+Supported formats:
+
+Video: .mp4, .webm, .avi, .mov, .mkv, .m4v
+
+Audio: .mp3, .wav, .m4a, .aac, .ogg, .flac
+
+2. Provide a URL
+Loom videos: https://www.loom.com/share/...
+
+Direct media links: https://example.com/video.mp4
+
+🔧 Optimizations for Large Files
+The system automatically handles large files using these techniques:
+
+Diagram
+Code
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+Chunk Processing:
+
+Audio is split into 1-minute segments
+
+Only segments >10 seconds are processed
+
+Enables parallel processing (future implementation)
+
+Early Stopping:
+
+Stops processing when 3 consecutive chunks agree with high confidence
+
+Saves processing time for long files
+
+Efficient Extraction:
+
+Uses FFmpeg for fast audio extraction
+
+Torchaudio fallback for compatibility
+
+Direct streaming for URL sources
+
+Confidence Threshold:
+
+Only predictions >60% confidence are considered
+
+Reduces false positives from noisy segments
+
+📊 Example Output
+Example Dashboard
+
+The dashboard shows:
+
+Predicted accent with confidence percentage
+
+Confidence scores per minute
+
+Accent distribution charts
+
+Processing time metrics