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/Generating Embeddings for Audio Files and Metadata of Songs
Generating Embeddings for Audio Files and Metadata of Songs
Learn how to generate embeddings for songs metadatas and audios.
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Metadata embeddings with BERT embedding model
To use BERT for generating metadata embedding, we need to convert the tabular metadata information (attributes and values) of a song into a string, which we call the textual_description of the song. For example:
"The song Infinity Edge has a danceability of 0.528, energy of 0.847, loudness of -4.741, speechiness of 0.0307, acousticness of 0.00674, instrumentalness of 0.814, liveness of 0.12, valence of 0.389, tempo of 143.997."
To do this, we read the CSV file containing metadata about songs and create descriptive text for each song using its attributes and values. We add this text to the DataFrame and then save the updated DataFrame back to the CSV file.
import pandas as pdmetadata_file_path="/content/drive/MyDrive/vector-databases-course/music-recommendation-system/dataset/reduced_80_fer2013_music_dataset_with_youtube_URLS.csv"# Loading metadata into DataFramemetadata_df = pd.read_csv(metadata_file_path)# Extracting relevant numeric attribute and creating textual description using themmetadata_df['textual_description'] = metadata_df.apply(lambda row: f"The song {row['song_name']} has a danceability of {row['danceability']}, "f"energy of {row['energy']}, "f"loudness of {row['loudness']}, "f"speechiness of {row['speechiness']}, "f"acousticness of {row['acousticness']}, "f"instrumentalness of {row['instrumentalness']}, "f"liveness of {row['liveness']}, "f"valence of {row['valence']}, "f"tempo of {row['tempo']}",axis=1)# Saving the updated DataFrame with the textual descriptions for each song back to the CSV filemetadata_df.to_csv(metadata_file_path, index=False)print("The 'textual_description' column has been added to the CSV file.")
The textual_description column added to the metadata_song.csv file is shown in the illustration below:
In the following ...