Speech Emotion Recognition
🚀 Take your AI-powered audio analytics to the next level with this Real-Time Speech Emotion Recognition (SER) Web Application built in Python using Flask! This advanced, paid-level project not only detects human emotions in real-time from audio but also segments and detects languages present in different parts of the audio file. A smart solution for managing and analyzing large sets of audio content effectively! 🎧🧠
📋 Project Information
| Field | Details |
|---|---|
| Project Name | Real-Time Speech Emotion Recognition Web App |
| Language/s Used | Python |
| Database | Not Required |
| Type | Web Application |
| Developer | UPDATEGADH |
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🔧 Technical Details
- Language/s Used: Python
- Framework: Flask
- Database: Not Required
- Python Version: 3.11+
- Machine Learning Libraries: TensorFlow, scikit-learn
- Audio Processing: NumPy, SciPy
- Web Server: Gunicorn (Production Ready)
- Dependencies: Flask, SQLAlchemy, TensorFlow, scikit-learn, NumPy, SciPy, Werkzeug
🌟 Available Features
🎤 Real-Time Speech Emotion Recognition – Detects emotions such as happy, sad, angry, and neutral from audio.
🌐 Web Interface with Flask – Simple and user-friendly interface for uploading and analyzing speech.
📂 Audio File Upload – Supports uploading audio files for processing directly via the web app.
🔄 Audio Segmentation – Splits audio into segments to detect multiple languages or emotion states.
🧠 TensorFlow-Based Emotion Model – Integrated machine learning model for accurate emotion prediction.
📈 Log Output via Console – Prints processing steps and results for real-time tracking.
🛠️ Modular Code Structure – Easy-to-read and extend Python files like audio_processor.py, emotion_model.py, etc.
🚀 No Database Required – Lightweight design makes it easy to deploy and test.
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