The Dataset's Purpose: This dataset's goal is to give a complete collection of music facts and lyrics for study and development. It aspires to be a useful resource for a variety of applications such as music analysis, natural language processing, sentiment analysis, recommendation systems, and others. This dataset, which combines song information and lyrics, can help academics, developers, and music fans examine and analyse the link between listeners' preferences and lyrical content.

Dataset Description:

The music dataset contains around 660 songs, each with its own set of characteristics. The following characteristics are included in the dataset:

Name: The title of the song. Lyrics: The lyrics of the song. Singer: The name of the singer or artist who performed the song. Movie: The movie or album associated with the song (if applicable). Genre: The genre or genres to which the song belongs. Rating: The rating or popularity score of the song from Spotify.

The dataset is intended to give a wide variety of songs from various genres, performers, and films. It includes popular songs from numerous ages and places, as well as a wide spectrum of musical styles. The lyrics were obtained from publically accessible services such as Spotify and Soundcloud, and were converted from audio to text using speech recognition algorithms. While every attempt has been taken to assure correctness, please keep in mind that owing to the limits of the data sources and voice recognition algorithms, there may be inaccuracies or missing lyrics encountered upon transcribing.

Use Cases in Research and Development:

This music dataset has several research and development applications. Among the possible applications are:

1. Music Analysis: By analysing the links between song elements such as genre, vocalist, and rating, researchers can acquire insights into the features and patterns of various music genres.
2. Natural Language Processing (NLP): NLP researchers may use the lyrics to create language models, sentiment analysis algorithms, topic modelling approaches, and other text-based music studies.
3. Recommendation Systems: Using the information, developers may create recommendation systems that offer music based on user preferences, lyrics sentiment, or genre similarities.
4. Music Generating Machine Learning Models: The dataset may be used to train machine learning models for generating new lyrics or making music compositions.
5. Music Sentiment Analysis: To get insights into the emotional components of music and its influence on listeners, researchers might analyse the feelings conveyed in song lyrics.
6. Movie Soundtracks Analysis: Researchers can explore the association between song attributes and their use in movie soundtracks by investigating the movie attribute.

Overall, the goal of this music dataset is to provide a rich resource for academics, developers, and music fans to investigate the complicated relationships between song features, lyrics, and numerous research and development applications in the music domain.

This dataset provides a list of lyrics from 1950 to 2019 describing music metadata as sadness, danceability, loudness, acousticness, etc. We also provide some informations as lyrics which can be used to natural language processing.

The audio data was scraped using Echo Nest® API integrated engine with spotipy Python’s package. The spotipy API permits the user to search for specific genres, artists,songs, release date, etc. To obtain the lyrics we used the Lyrics Genius® API as baseURL for requesting data based on the song title and artist name.

YouTube Music Hits Dataset

A collection of YouTube music video data sourced from Wikidata, focusing on videos with significant viewership metrics.

  Dataset Description





  Overview

24,329 music videos View range: 1M to 5.5B views Temporal range: 1977-2024

  Features

youtubeId: YouTube video identifier itemLabel: Video/song title performerLabel: Artist/band name youtubeViews: View count year: Release year genreLabel: Musical genre(s)

  View… See the full description on the dataset page: https://huggingface.co/datasets/akbargherbal/youtube-music-hits.

Dataset Card for Music Genre

The Default dataset comprises approximately 1,700 musical pieces in .mp3 format, sourced from the NetEase music. The lengths of these pieces range from 270 to 300 seconds. All are sampled at the rate of 22,050 Hz. As the website providing the audio music includes style labels for the downloaded music, there are no specific annotators involved. Validation is achieved concurrently with the downloading process. They are categorized into a total of 16… See the full description on the dataset page: https://huggingface.co/datasets/ccmusic-database/music_genre.

🎵 Worldwide Music Artists Dataset (with image) 🎤

Welcome to the Worldwide Music Artists Dataset—your go-to resource for exploring the global music scene! 🌍🎶

This dataset features 100,000+ music artists from around the world, complete with: 📝 Name: Discover artists from every genre and corner of the globe. 🎸 Genres: Whether it's pop, rock, jazz, or classical, find your favorite styles. 📸 Profile Image: Visualize each artist with their unique profile picture. 📍 Location: See where your favorite artists hail from.

Whether you're a music enthusiast, a data scientist or a developer this dataset is perfect for your next project! 🚀

MusicOSet is an open and enhanced dataset of musical elements (artists, songs and albums) based on musical popularity classification. Provides a directly accessible collection of data suitable for numerous tasks in music data mining (e.g., data visualization, classification, clustering, similarity search, MIR, HSS and so forth). To create MusicOSet, the potential information sources were divided into three main categories: music popularity sources, metadata sources, and acoustic and lyrical features sources. Data from all three categories were initially collected between January and May 2019. Nevertheless, the update and enhancement of the data happened in June 2019.

The attractive features of MusicOSet include:

• Integration and centralization of different musical data sources
• Calculation of popularity scores and classification of hits and non-hits musical elements, varying from 1962 to 2018
• Enriched metadata for music, artists, and albums from the US popular music industry
• Availability of acoustic and lyrical resources
• Unrestricted access in two formats: SQL database and compressed .csv files

|    Data    | # Records |
|:-----------------:|:---------:|
| Songs       | 20,405  |
| Artists      | 11,518  |
| Albums      | 26,522  |
| Lyrics      | 19,664  |
| Acoustic Features | 20,405  |
| Genres      | 1,561   |

Dataset Card for MusicCaps

  Dataset Summary

The MusicCaps dataset contains 5,521 music examples, each of which is labeled with an English aspect list and a free text caption written by musicians. An aspect list is for example "pop, tinny wide hi hats, mellow piano melody, high pitched female vocal melody, sustained pulsating synth lead", while the caption consists of multiple sentences about the music, e.g., "A low sounding male voice is rapping over a fast paced drums… See the full description on the dataset page: https://huggingface.co/datasets/google/MusicCaps.

This dataset is a collection of mel-spectrogram features extracted from Indian regional music containing the following languages:
Hindi, Gujarati, Marathi, Konkani, Bengali, Oriya, Kashmiri, Assamese, Nepali, Konyak, Manipuri, Khasi & Jaintia, Tamil, Malayalam, Punjabi, Telugu, Kannada.

Five recordings are collected for each language for four artists (2Male + 2Female) each. 2 artists out of 4 for each language are old veteran performers, and the remaining 2 are contemporary performers. Overall, the dataset includes 17 languages, 68 artists (34 Males and 34 Females). There are 340 recordings in the dataset, with a total duration of 29.3 hrs.

Mel-spectrogram is extracted from a 1-second segment with a 1/2 second sliding window for each song. Extracted mel-spectrogram for each segment is annotated with language, location, local_song_index, global_song_index, language_id, location_id, artist_id, gender_id.

_

This project was funded under the grant number: ECR/2018/000204 by the Science & Engineering Research Board (SERB).

This is the dataset that I gathered from different sources for Music Generation.

There are around 30 music midi files and there is a handling code in the Notebook named "Official" at the end.

Please use the dataset wisely and only for good purposes.

Please upvote the notebook and dataset if you like this.

MSMD is a synthetic dataset of 497 pieces of (classical) music that contains both audio and score representations of the pieces aligned at a fine-grained level (344,742 pairs of noteheads aligned to their audio/MIDI counterpart). It can be used for training and evaluating multimodal models that enable crossing from one modality to the other, such as retrieving sheet music using recordings or following a performance in the score image.

Please find further information and a corresponding Python package on this Github page: https://github.com/CPJKU/msmd

If you use this dataset, please cite:
[1] Matthias Dorfer, Jan Hajič jr., Andreas Arzt, Harald Frostel, Gerhard Widmer.
Learning Audio-Sheet Music Correspondences for Cross-Modal Retrieval and Piece Identification (PDF).
Transactions of the International Society for Music Information Retrieval, issue 1, 2018.

This dataset is a collection of mel-spectrogram features extracted from Indian folk music containing the following 15 folk styles:
Bauls, Bhavageethe, Garba, Kajri, Maand, Sohar, Tamang Selo, Veeragase, Bhatiali, Bihu, Gidha, Lavani, Naatupura Paatu, Sufi, Uttarakhandi.

The number of recordings varies from 16 to 50 in the mentioned folk styles representing the scarcity of availability of given folk styles on the Internet. There are at least 4 artists and a maximum of 22. Overall there are 125 artists (34 female + 91 male) in these 15 folk styles.

There is a total of 606 recordings in the dataset, with a total duration of 54.45 hrs.
Mel-spectrogram is extracted from a 3-second segment with each song's 1/2 second sliding window. Extracted mel-spectrogram for each segment is annotated with folk_style, state, artist, gender, song, source, no_of_artists, folk_style_id, state_id, artist_id, gender_id.
_
This project was funded under the grant number: ECR/2018/000204 by the Science & Engineering Research Board (SERB).

README

Music Genre fMRI Dataset by Tomoya Nakai, Naoko Koide-Majima, and Shinji Nishimoto

References: Nakai, Koide-Majima, and Nishimoto (2021). Correspondence of categorical and feature-based representations of music in the human brain. Brain and Behavior. 11(1), e01936. https://doi.org/10.1002/brb3.1936

We measured brain activity using functional MRI while five subjects (“sub-001”, …, “sub-005”) listened to music stimuli of 10 different genres.

The entire folder consists of subject-wise subfolders (“sub-001”,…). Each subject’s folder contains the following subfolders: 1) anat: T1-weighted structural images 2) func: functional signals (multi-band echo-planar images)

Each subject performed 18 runs consisting of 12 training runs and 6 test runs. The training and test data were assigned with the following notations: Training data: sub-00*_task-Training_run-**_bold.json Test data: sub-00*_task-Test_run-**_bold.json

Each *_event.tsv file contains following information: onset: stimulus onset Duration: stimulus duration genre: genre type (out of 10 genres) track: index to identify the original track start: onset of excerpt from the original track (second) end: offset of excerpt from the original track (second)

The duration of all stimuli is 15s.　For each clip, 2 s of fade-in and fade-out effects were applied, and the overall signal intensity was normalized in terms of the root mean square.

For the training runs, the 1st stimulus (0-15s) is the same as the last stimulus of the previous run (600-615s). For the test runs, the1st stimulus (0-15s) is the same as the last stimulus of the same run (600-615s).

The original music stimuli (GTZAN dataset) can be found here: http://marsyas.info/downloads/datasets.html

Caution This dataset can be used for research purposes only. The data were anonymized, and users shall not perform analyses to re-identify individual subjects.

AIGenLab/Music-Dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Healthydater/music-dataset-1 dataset hosted on Hugging Face and contributed by the HF Datasets community

1) Data Introduction • The Music Dataset : 1950 to 2019 is a large-scale music dataset that includes various musical metadata such as sadness, danceability, loudness, acoustics, and more, along with song-specific lyrics from 1950 to 2019.

2) Data Utilization (1) Music Dataset : 1950 to 2019 has characteristics that: • The dataset consists of more than 30 numerical and categorical variables, including artist name, song name, release year, lyrics, song length, emotion (sad, etc.), danceability, volume, acousticity, instrument use, energy, and subject matter, and provides both lyric text and musical characteristics. (2) Music Dataset : 1950 to 2019 can be used to: • Analysis of Music Trends and Emotional Changes: By analyzing changes in major music characteristics such as sadness, danceability, and volume by year in time series, you can explore music trends and emotional changes by period. • Lyrics-based Natural Language Processing and Genre Classification: Using song-specific lyrics and metadata, it can be used for various text and music data fusion analysis such as natural language processing-based emotion analysis, music genre classification, and recommendation system.

FMA: A Dataset for Music Analysis

Michaël Defferrard, Kirell Benzi, Pierre Vandergheynst, Xavier Bresson. International Society for Music Information Retrieval Conference (ISMIR), 2017.

We introduce the Free Music Archive (FMA), an open and easily accessible dataset suitable for evaluating several tasks in MIR, a field concerned with browsing, searching, and organizing large music collections. The community's growing interest in feature and end-to-end learning is however restrained… See the full description on the dataset page: https://huggingface.co/datasets/benjamin-paine/free-music-archive-full.

ACMUS YouTube Music Dataset

Annotated Music for Instrumental Format Recognition and Vocal Classification

By [source]

About this dataset

The ACMUS YouTube Music Set is an annotated collection of music from YouTube videos, designed to support the exploration of cutting-edge computational methods for two key tasks: Instrumental Format Identification and Vocal Music Classification. Encompassing a wide range of genres and eras, this multi-dimensional dataset contains information such as File Name, Title, Genre, Composer or Artist?, Sampling Rate, Channels, Bit Depth, Duration (sec), Original File (if applicable), Collection from which it was taken from , Observations made about the audio file (if any), Number of Instruments present in the audio file, Presence or absence of Guitar/Bandola/Tiple/Bass/Percussion/, Tempo and Language travelled. Additionally this dataset tackles one step further by including vocal classification based on presence or absence in Female Voice / Male Voice files. This is a great resource for anyone exploring Artificial Intelligence techniques related to music recognition and vocal classification

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Review the columns of information that are included in the dataset: These include File name, Title, Genre, Composer or artist?, Sampling rate, Channels, Bit depth, Duration (sec), Collection, Nr. of instruments, Guitar, Bandola, Tiple Bass Female Voice Male Voice Percussion Tempo Language Artist/Performer Filename Composer Original File Last called (Date) Number of instruments and Female voice and Male voice.
• Start by exploring the audio file properties first: these include File name Title Genre Sampling rate Channels Bit depth Duration (sec) Collection Nr. of instruments Guitar Bandola Tiple Bass Female Voice Male Voice Percussion Tempo Language Artist/Performer Filename Composer Original File Number of Instruments Female Voice and Male Voice
• Make sure you have a clear understanding about each column before you proceed: This includes all the features associated with each audio file such as title genre composition artist sampling rate bit depth duration in seconds number of tracks original file date uploaded collection observations guitar bandola tiple bass female voice male voice percussion tempo language artist or performer filename composer etc
• Establish relationships between different data points by using visualization tools like graphs tables scatter plots etc.: Visualize all related audio file properties like their genre type their channel compositions artist names original files last call dates collections observed noise levels at 64 Hz and 128 Hz identifying cover versions instrumental versions etc
  5 Update your research regularly with new findings by revisiting your visualizations comparing features between different formats running clustering algorithms for classification to better group music files accordingly

Research Ideas

• Using the Instrumental Format Recognition and Vocal Music Classification tasks with Machine Learning algorithms to create an automated music labeler. The data in this dataset could be used to create a tool that can identify various instruments in an audio file and also classify music as either vocal or instrumental, which can help streamline the process of cataloguing and labeling new music tracks.
• This dataset could be used for training computer vision models for automatic instrument recognition from video files. By feeding the dataset into a convolutional neural network, algorithms can be developed to detect different types of instruments from video streams and differentiate between vocal or instrumental pieces.
• This dataset could be used for audio source separation research, which is the process of isolating individual audio sources from a mix of sounds within an audio clip or recording. Source separation research often relies on datasets such as this one for providing labeled data about instrumentation and pitch levels that allow researchers to develop algorithms capable of separating multiple sound sources within a single mixture signal

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. [See Other Inf...

Gain valuable insights into music trends, artist popularity, and streaming analytics with our comprehensive Spotify Dataset. Designed for music analysts, marketers, and businesses, this dataset provides structured and reliable data from Spotify to enhance market research, content strategy, and audience engagement.

Dataset Features

Track Information: Access detailed data on songs, including track name, artist, album, genre, and release date. Streaming Popularity: Extract track popularity scores, listener engagement metrics, and ranking trends. Artist & Album Insights: Analyze artist performance, album releases, and genre trends over time. Related Searches & Recommendations: Track related search terms and suggested content for deeper audience insights. Historical & Real-Time Data: Retrieve historical streaming data or access continuously updated records for real-time trend analysis.

Customizable Subsets for Specific Needs Our Spotify Dataset is fully customizable, allowing you to filter data based on track popularity, artist, genre, release date, or listener engagement. Whether you need broad coverage for industry analysis or focused data for content optimization, we tailor the dataset to your needs.

Popular Use Cases

Market Analysis & Trend Forecasting: Identify emerging music trends, genre popularity, and listener preferences. Artist & Label Performance Tracking: Monitor artist rankings, album success, and audience engagement. Competitive Intelligence: Analyze competitor music strategies, playlist placements, and streaming performance. AI & Machine Learning Applications: Use structured music data to train AI models for recommendation engines, playlist curation, and predictive analytics. Advertising & Sponsorship Insights: Identify high-performing tracks and artists for targeted advertising and sponsorship opportunities.

Whether you're optimizing music marketing, analyzing streaming trends, or enhancing content strategies, our Spotify Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

MGD: Music Genre Dataset

Over recent years, the world has seen a dramatic change in the way people consume music, moving from physical records to streaming services. Since 2017, such services have become the main source of revenue within the global recorded music market. Therefore, this dataset is built by using data from Spotify. It provides a weekly chart of the 200 most streamed songs for each country and territory it is present, as well as an aggregated global chart.

Considering that countries behave differently when it comes to musical tastes, we use chart data from global and regional markets from January 2017 to December 2019, considering eight of the top 10 music markets according to IFPI: United States (1st), Japan (2nd), United Kingdom (3rd), Germany (4th), France (5th), Canada (8th), Australia (9th), and Brazil (10th).

We also provide information about the hit songs and artists present in the charts, such as all collaborating artists within a song (since the charts only provide the main ones) and their respective genres, which is the core of this work. MGD also provides data about musical collaboration, as we build collaboration networks based on artist partnerships in hit songs. Therefore, this dataset contains:

Genre Networks: Success-based genre collaboration networks

Genre Mapping: Genre mapping from Spotify genres to super-genres

Artist Networks: Success-based artist collaboration networks

Artists: Some artist data

Hit Songs: Hit Song data and features

Charts: Enhanced data from Spotify Weekly Top 200 Charts

This dataset was originally built for a conference paper at ISMIR 2020. If you make use of the dataset, please also cite the following paper:

Gabriel P. Oliveira, Mariana O. Silva, Danilo B. Seufitelli, Anisio Lacerda, and Mirella M. Moro. Detecting Collaboration Profiles in Success-based Music Genre Networks. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR 2020), 2020.

@inproceedings{ismir/OliveiraSSLM20, title = {Detecting Collaboration Profiles in Success-based Music Genre Networks}, author = {Gabriel P. Oliveira and Mariana O. Silva and Danilo B. Seufitelli and Anisio Lacerda and Mirella M. Moro}, booktitle = {21st International Society for Music Information Retrieval Conference} pages = {726--732}, year = {2020} }