update readme

Author: haroldeustaquio

Melody-Generation/README.md

@@ -141,16 +141,17 @@ This function:
 
 - **Root Mean Square Error (RMSE)**: Measures the average magnitude of error between the predicted and actual values, particularly useful for numeric features like `duration` and `step`. Lower RMSE values indicate more accurate predictions. It is calculated as:
 
-$$
+```math
 RMSE = \sqrt{\frac{1}{N} \sum_{i=1}^{N} (y_i - \hat{y}_i)^2}
-$$
+```
 
 
 - **Coefficient of Determination (R²)**: Evaluates the proportion of variance in the actual values explained by the model, R² values close to 1 indicate the model captures the underlying patterns well. It is calculated as:
 
-$$
+
+```math
 R^2 = 1 - \frac{\sum_{i=1}^N (y_i - \hat{y}_i)^2}{\sum_{i=1}^N (y_i - \bar{y})^2}
-$$
+```
 
 
 ### Evaluation Process

README.md

@@ -2,26 +2,41 @@
 
 ## Overview
 
-[In process...]
-
+**Melody-Lyric-Generator** is designed to combine melody generation and lyric generation for creating cohesive musical content. It processes, analyzes, and generates melodies using MIDI data, while simultaneously handling lyric extraction and fine-tuning for generating Spanish-language text. This project employs advanced datasets, custom preprocessing functions, and machine learning models to produce high-quality musical and lyrical outputs.
 
 **Content:**
 
 - [Lyrics Generation](#lyrics-generation)
 - [Melody Generation](#melody-generation)
-- [Usage](#usage)
 
 ---
 
 ## Lyrics Generation
 
-[In process...]
+**Lyrics Generation** processes and analyzes song lyrics using the LyricsGenius API, cleaning the data and identifying emotional content through a lexicon-based approach. Additionally, it fine-tunes the pre-trained [`datificate/gpt2-small-spanish`](https://huggingface.co/datificate/gpt2-small-spanish) model for text generation.
+
+**Key Features:**
+- **Data Cleaning**: Removes unnecessary elements such as metadata, punctuation, and stopwords.
+- **Emotional Analysis**: Identifies emotions in lyrics using a specialized lexicon.
+- **Fine-Tuning GPT**: Trains the model across multiple datasets, optimizing for coherent and emotionally rich text.
+
+---
 
 ## Melody Generation
 
-[In process...]
+**Melody Generation** processes, analyzes, and generates melodies using the MAESTRO dataset and custom MIDI handling functions. A machine learning model is trained to predict musical notes, evaluate performance, and produce cohesive melodies.
+
+**Key Features:**
+- **Custom MIDI Functions**: Includes tools for note extraction, sequence creation, and MIDI playback.
+- **Neural Network Architecture**: Trains on features like pitch, step, and duration to predict melodies.
+- **Evaluation Metrics**: Uses RMSE and R² to measure the quality of generated melodies.
 
 
-## Usage
+---
 
+<div align="center"> 
+  <em> 
+    We believe in the power of collaboration. If you have ideas, suggestions, or improvements, feel free to open an issue or submit a pull request. Let’s make this project even better—your contributions are always welcome! 
+  </em> 
+</div>