📝 README

🎙️ Voice to Text Data using NLP

👤 Author

Arpita N Sheelvanth

📖 Project Overview

This project converts voice/audio input into text and then applies Natural Language Processing (NLP) techniques to extract meaningful insights.
It is designed as a web-based application with a simple, modern, and responsive interface.

The application is useful for:

• Students taking lecture notes
• Professionals transcribing meetings
• Analyzing speech data for patterns and sentiment

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🚀 Features

• 🎤 Voice Input: Record audio directly from the browser or upload an audio file (.mp3, .wav).
• 📝 Speech-to-Text Conversion: Transcribes audio into text in real time.
• 🧠 NLP Processing:
  • Text cleaning & tokenization
  • Stopword removal
  • Word frequency analysis
  • Sentiment analysis (Positive / Negative / Neutral)
• 📊 Visualization & Insights:
  • Word frequency charts/word cloud
  • Sentiment distribution graphs
  • Summary statistics (word count, unique words, sentence length)
• 💾 Export Options:
  • Download transcription as .txt
  • Export analytics as .pdf or .csv

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⚙️ Tech Stack

• Frontend: React + Tailwind CSS
• Speech-to-Text: Web Speech API / Speech-to-Text API
• NLP: NLP libraries for tokenization & sentiment analysis
• Charts: Recharts / Chart.js
• Build Tool: Vite

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🖥️ How to Run Locally

1. Clone the repository:

   git clone https://github.com/your-username/voice-to-text-nlp.git
   
   

2. Navigate into the project folder:

cd voice-to-text-nlp

4. Install dependencies:

npm install

3. Start the development server:

npm run dev

📸 Screenshots

🔗 Demo Link

https://voice-to-text-nlp-ap-vhha.bolt.host

📌 Future Improvements

Multi-language speech recognition

Advanced sentiment analysis with deep learning

Named Entity Recognition (NER) to detect people, places, etc.

Automatic summarization of transcribed text