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 |
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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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