Update README.md

README.md

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+# EcoSoundNet 🔥
+
+Welcome to EcoSoundNet, a sound classification application built using Streamlit. This project leverages audio data from the **UrbanSound8K** dataset and implements machine learning models for sound classification. Check out the details below!
+
+## Overview
+
+EcoSoundNet uses state-of-the-art techniques for audio classification, aiming to detect various environmental sounds. The model is trained on the **UrbanSound8K** dataset, which contains 8,732 labeled sound excerpts from urban environments. The project includes preprocessing steps, model training, and validation with accuracy comparisons.
+
+## Dataset
+
+The dataset used for training the model is the [UrbanSound8K](https://www.kaggle.com/datasets/chrisfilo/urbansound8k) dataset. This dataset consists of sound clips from 10 different urban sound classes, making it suitable for environmental sound classification tasks.
+
+## Notebooks for Preprocessing, Training, and Accuracy Comparisons
+
+For detailed insights into the preprocessing, training, and validation steps, please refer to the following notebook:
+
+[Training and Validation Notebook](https://colab.research.google.com/drive/1bOEBYO6emJOO20LYviUWK6krXbvoziIo?usp=sharing)
+
+This notebook covers:
+- Data preprocessing steps
+- Model training
+- Accuracy comparisons of different models
+
+## Streamlit App
+
+The main application is built using **Streamlit**, a powerful framework for creating data science applications. You can run the app using the file `app.py`.
+
+### Configuration
+
+- **SDK**: Streamlit
+- **SDK Version**: 1.41.1
+- **App File**: `app.py`
+- **Pinned**: No
+
+### App Configuration
+
+```yaml
 title: EcoSoundNet
 emoji: 🔥
 colorFrom: pink
@@ -7,6 +43,3 @@ sdk: streamlit
 sdk_version: 1.41.1
 app_file: app.py
 pinned: false
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-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference