Upload speech emotion recognition model

README.md

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+# SentimentSound
+
+## Overview
+This is a deep learning model for Speech Emotion Recognition that can classify audio clips into different emotional states. The model is trained on a dataset of speech samples and can identify emotions such as neutral, calm, happy, sad, angry, fearful, disgust, and surprised.
+
+## Model Details
+- **Model Type:** Hybrid Neural Network (CNN + LSTM)
+- **Input:** Audio features extracted from 3-second wav files
+- **Output:** Emotion classification
+
+### Supported Emotions
+- Neutral
+- Calm
+- Happy
+- Sad
+- Angry
+- Fearful
+- Disgust
+- Surprised
+
+## Installation
+
+### Clone the Repository
+```bash
+git clone https://github.com/Vishal-Padia/SentimentSound.git
+```
+
+### Dependencies
+```bash
+pip install -r requirements.txt
+```
+
+### Usage Example
+
+```bash
+python emotion_predictor.py
+```
+
+
+## Model Performance
+- **Accuracy:** 85%
+- **Evaluation Metrics:** Confusion matrix below
+
+![Image](confusion_matrix.png)
+
+## Training Details
+- **Feature Extraction:** 
+  - MFCC
+  - Spectral Centroid
+  - Chroma Features
+  - Spectral Contrast
+  - Zero Crossing Rate
+  - Spectral Rolloff
+- **Augmentation:** Random noise and scaling applied
+- **Training Techniques:**
+  - Class weighted loss
+  - AdamW optimizer
+  - Learning rate scheduling
+  - Gradient clipping
+
+## Limitations
+- Works best with clear speech recordings
+- Optimized for 3-second audio clips
+- Performance may vary with different audio sources
+
+## Acknowledgments
+- Dataset used for training (https://www.kaggle.com/datasets/uwrfkaggler/ravdess-emotional-speech-audio)