ozefe/spotify_audio_features

Spotify Tracks & Audio Features Dataset

Overview

This dataset contains a comprehensive collection of Spotify tracks, combining rich audio feature analysis with track metadata. It is formatted as a high-performance Parquet dataset (ZStandard compressed), optimized for large-scale tabular analysis, machine learning, and recommender system research.

Data Source

The raw data for this dataset was originally gathered and hosted by Anna's Archive.

• Original Blog Post: Anna's Archive: Backing up Spotify
• Torrent: spotify_2024 (Anna's Archive)

This repository serves as a cleaned, merged, and technically optimized version of that raw dump, provided for convenience to researchers who wish to analyze the data without managing the original 100GB+ SQLite files.

Technical Construction

The original release consisted of multiple disjoint SQLite 3 databases. This dataset was constructed using DuckDB to perform an out-of-core merge of two massive tables:

1. track_metadata.sqlite3 (Table: tracks)
2. audio_features.sqlite3 (Table: track_audio_features)

Processing Details

• Merge Key: The tables were joined on the Spotify Track ID (id from metadata and track_id from audio features).
• Type Safety: The original SQLite files contained weak typing (e.g., floats stored in integer columns). During the merge, all columns were explicitly cast to strict types (BIGINT for counts/enums, DOUBLE for continuous metrics) to ensure schema consistency.
• Format: The data is stored in Apache Parquet format.
• Compression: ZStandard (ZSTD) compression was applied to minimize disk usage while maintaining fast read speeds.

Dataset Structure

Audio Features

Official API Documentation: https://developer.spotify.com/documentation/web-api/reference/get-audio-features

• track_id: The Spotify ID (Base62) for the track.
• null_response: true if the API returned null for the whole request
• duration_ms: The duration of the track in milliseconds.
• time_signature: An estimated time signature. The time signature (meter) is a notational convention to specify how many beats are in each bar (or measure). The time signature ranges from 3 to 7 indicating time signatures of "3/4" to "7/4".
• tempo: The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.
• key: The key the track is in. Integers map to pitches using standard Pitch Class (https://en.wikipedia.org/wiki/Pitch_class) notation. E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value is -1.
• mode: Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0.
• danceability: Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.
• energy: Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.
• loudness: The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typically range between -60 and 0 db.
• speechiness: Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.
• acousticness: A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.
• instrumentalness: Predicts whether a track contains no vocals. "Ooh" and "aah" sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly "vocal". The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.
• liveness: Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.
• valence: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).

Track Metadata

Official API Documentation: https://developer.spotify.com/documentation/web-api/reference/get-track

• id: The Spotify ID (Base62) for the track.
• name: The name of the track.
• popularity: The popularity of the track. The value will be between 0 and 100, with 100 being the most popular. The popularity of a track is a value between 0 and 100, with 100 being the most popular. The popularity is calculated by algorithm and is based, in the most part, on the total number of plays the track has had and how recent those plays are. Generally speaking, songs that are being played a lot now will have a higher popularity than songs that were played a lot in the past. Duplicate tracks (e.g. the same track from a single and an album) are rated independently. Artist and album popularity is derived mathematically from track popularity. Note: the popularity value may lag actual popularity by a few days: the value is not updated in real time.

Potential Use Cases

This dataset is suitable for large-scale data science projects, including:

1. Tabular Regression: Predicting a song's popularity based on its audio characteristics (e.g., "Are faster songs more popular?").
2. Tabular Classification: Predicting the Key, Mode, or Time Signature of a song based on its acoustic profile.
3. Recommender Systems: Content-based filtering to find "nearest neighbors" for songs. By using features like valence, energy, and danceability as vectors, you can calculate cosine similarity to recommend tracks that "sound" the same.
4. Music Analysis: Analyzing trends in music production over time (e.g., "Has music become louder or sadder?").

Legal & Disclaimer

License: Unspecified / Proprietary (Research Use Only)

This dataset is a derived work provided for educational and research purposes. While the compilation, cleaning, and format conversion represent a "sweat of the brow" effort to make these files accessible, the underlying data points, ID systems, and acoustic metrics are the intellectual property of Spotify AB.

We make no claim of ownership over the content. This dataset is provided "as-is" for convenience, to save researchers the computational cost of merging the original raw archives. Users should adhere to Spotify's Terms of Use regarding data handling.