Speech-to-Text Analysis Dashboard

Overview

Real-time speech recognition and sentiment analysis web application built with Flask and Google's Speech Recognition API. Features an intuitive dashboard displaying transcription history, sentiment scores, and downloadable transcripts.

Architecture & Methodology

• Speech Recognition: Google Speech-to-Text API via SpeechRecognition library
• Sentiment Analysis: NLTK VADER (Valence Aware Dictionary for Sentiment Reasoning)
• Backend: Flask RESTful API with modular design
• Frontend: Responsive UI with TailwindCSS and vanilla JavaScript
• Data Storage: File-based transcript storage with timestamp organization
• Analysis Pipeline:

1. Audio Capture -> Speech Recognition
2. Text Processing -> Sentiment Analysis
3. Data Storage -> File System
4. Real-time Updates -> WebUI

Features

• Real-time speech-to-text conversion
• Sentiment analysis (Positive/Negative/Neutral scores)
• Transcript history with timestamps
• Downloadable transcripts
• Responsive dashboard UI
• Audio feedback and visual indicators

Technology Stack

• Python 3.8+
• Flask 3.1.0
• NLTK 3.8.1
• SpeechRecognition 3.11.0
• PyAudio 0.2.14
• TailwindCSS
• Google Speech API

Installation

git clone https://github.com/miladnasiri/Speech-to-text-.git
cd Speech-to-text-
python -m venv venv
source venv/bin/activate  # Linux/Mac
pip install -r requirements.txt
python src/app.py

Project Structure

CopySpeech-to-Text/
├── src/
│   ├── app.py          # Flask application
│   ├── templates/      # HTML templates
│   └── static/         # CSS, JS assets
├── data/
│   └── transcripts/    # Stored transcripts
└── requirements.txt    # Dependencies

API Endpoints

GET / : Dashboard interface POST /recognize : Speech recognition endpoint GET /transcripts/ : Download endpoint

Dependencies

CopyFlask==3.1.0
SpeechRecognition==3.11.0
pyaudio==0.2.14
nltk==3.8.1
pillow==11.0.0
pytest==8.3.3

Features in Detail

1.Speech Recognition

• Real-time audio capture
• Google Speech API integration
• Error handling for unclear speech

Sentiment Analysis

• VADER sentiment scoring
• Compound score calculation
• Positive/Negative/Neutral breakdown

Data Management

• Timestamp-based filing
• Downloadable transcripts
• Historical record keeping

User Interface

• Real-time feedback
• Visual sentiment indicators
• Responsive design
• Download functionality

MIT License Authors Milad Nasiri