During information technology development in most fields, music also develops rapidly and has many diverse genres. With the increasing number of songs, finding a favorite song becomes more and more complicated when we need help to remember the name or genre of that song clearly. Our study aims to seek songs based on analyzing the characteristics of an audio clip with lyrics or analyzing data about words-key (lyrics) provided by the user. More specifically, this study has attempted three approaches. The first method uses Google Speech to Text Application Programming Interface (API) with the speech recognition library to output text from the user’s audio inputs or to directly enter text to search. Then, we applied the Inverted Index structure to process and store the original lyrics text. The second method is to extract audio features, Mel Frequency Cepstral Coefficients (MFCC), and then leverage the audio-Approximate Nearest Neighbors (ANN) algorithm to support neighborhood search. The third approach is Audio Fingerprint, used to identify and classify audio segments by converting the audio signal into a unique data string, also known as a hash function. The experiments are evaluated on Vietnamese Song. The proposed approach is expected to provide a potential method for Vietnamese music search engines.