The field of music generation has witnessed remarkable advancements in recent years, thanks
to the emergence of deep learning techniques. In this paper, we present a novel music
generation system utilizing a character-level Recurrent Neural Network (char-RNN)
empowered by a hybrid architecture of Long Short-Term Memory (LSTM) and Gated
Recurrent Unit (GRU) cells. Our system is trained on a comprehensive dataset consisting of
340 tunes represented in ABC notation, a text-based format for musical compositions. By
predicting the subsequent character in a sequence of ABC notation, our model adeptly
generates new and captivating melodies. Extensive evaluations are conducted, employing a
variety of metrics, to assess the system's performance, revealing its ability to generate coherent
and musically plausible music. Furthermore, we demonstrate the versatility of our proposed
system, highlighting its potential applications in music composition, accompaniment, and real
time improvisation. The results substantiate the effectiveness of employing char-RNN with
LSTM-GRU cells for music generation, thereby opening up intriguing avenues for future
research in this evolving field.