annas-archive.li/blog, 2025-12-20
We backed up Spotify (metadata and music files). It’s distributed in bulk torrents (~300TB), grouped by popularity.
This release includes the largest publicly available music metadata database with 256 million tracks and 186 million unique ISRCs.
It’s the world’s first “preservation archive” for music which is fully open (meaning it can easily be mirrored by anyone with enough disk space), with 86 million music files, representing around 99.6% of listens.
Anna’s Archive normally focuses on text (e.g. books and papers). We explained in “The critical window of shadow libraries” that we do this because text has the highest information density. But our mission (preserving humanity’s knowledge and culture) doesn’t distinguish among media types. Sometimes an opportunity comes along outside of text. This is such a case.
A while ago, we discovered a way to scrape Spotify at scale. We saw a role for us here to build a music archive primarily aimed at preservation.
Generally speaking, music is already fairly well preserved. There are many music enthusiasts in the world who digitized their CD and LP collections, shared them through torrents or other digital means, and meticulously catalogued them.
However, these existing efforts have some major issues:
- Over-focus on the most popular artists. There is a long tail of music which only gets preserved when a single person cares enough to share it. And such files are often poorly seeded.
- Over-focus on the highest possible quality. Since these are created by audiophiles with high end equipment and fans of a particular artist, they chase the highest possible file quality (e.g. lossless FLAC). This inflates the file size and makes it hard to keep a full archive of all music that humanity has ever produced.
- No authoritative list of torrents aiming to represent all music ever produced. An equivalent of our book torrent list (which aggregate torrents from LibGen, Sci-Hub, Z-Lib, and many more) does not exist for music.
This Spotify scrape is our humble attempt to start such a “preservation archive” for music. Of course Spotify doesn’t have all the music in the world, but it’s a great start.
Before we dive into the details of this collection, here is a quick overview:
- Spotify has around 256 million tracks. This collection contains metadata for an estimated 99.9% of tracks.
- We archived around 86 million music files, representing around 99.6% of listens. It’s a little under 300TB in total size.
- We primarily used Spotify’s “popularity” metric to prioritize tracks. View the top 10,000 most popular songs in this HTML file (13.8MB gzipped).
- For
popularity>0, we got close to all tracks on the platform. The quality is the original OGG Vorbis at 160kbit/s. Metadata was added without reencoding the audio (and an archive of diff files is available to reconstruct the original files from Spotify, as well as a metadata file with original hashes and checksums). - For
popularity=0, we got files representing about half the number of listens (either original or a copy with the same ISRC). The audio is reencoded to OGG Opus at 75kbit/s — sounding the same to most people, but noticeable to an expert. - The cutoff is 2025-07, anything released after that date may not be present (though in some cases it is).
- This is by far the largest music metadata database that is publicly available. For comparison, we have 256 million tracks, while others have 50-150 million. Our data is well-annotated: MusicBrainz has 5 million unique ISRCs, while our database has 186 million.
- This is the world’s first “preservation archive” for music which is fully open (meaning it can easily be mirrored by anyone with enough disk space).
The data will be released in different stages on our Torrents page:
- [X] Metadata (Dec 2025)
- [ ] Music files (releasing in order of popularity)
- [ ] Additional file metadata (torrent paths and checksums)
- [ ] Album art
- [ ] .zstdpatch files (to reconstruct original files before we added embedded metadata)
For now this is a torrents-only archive aimed at preservation, but if there is enough interest, we could add downloading of individual files to Anna’s Archive. Please let us know if you’d like this.
Please help preserve these files:
- Donate to Anna’s Archive. Any amount helps!
- Seed these torrents (on the Torrents page of Anna’s Archive). Even a seeding a few torrents helps!
With your help, humanity’s musical heritage will be forever protected from destruction by natural disasters, wars, budget cuts, and other catastrophes.
In this blog we will analyze the data and look at details of the release. We hope you enjoy.
— Volunteer “ez” of Anna’s Archive team
♫ ♫ ♫ ♫ ♫
Data Exploration
Let’s dive into the data! Here's some high-level statistics pulled from the metadata:
Songs / Tracks
Spotify has around 256 million tracks.
The most convenient available way to sort songs on Spotify is using the popularity metric, defined as follows:
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.
If we group songs by popularity, we see that there is an extremely large tail end:
≥70% of songs are ones almost no one ever listens to (stream count < 1000). To see some detail, we can plot this on a logarithmic scale:
The top 10,000 songs span popularities 70-100. You can view them all in this HTML file (13.8MB gzipped).
Additionally, we can estimate the number of listens per track and total number per popularity. The stream count data is estimated since it is difficult to fetch at scale, so we sampled it randomly.
As we can see, most of the listens come from songs with a popularity between 50 and 80, even though there's only 210.000 songs with popularity ≥50, around 0.1% of songs. Note the huge (subjectively estimated) error bar on pop=0 — the reason for this is that Spotify does not publish stream counts for songs with < 1000 streams.
We can also estimate that the top three songs (as of writing) have a higher total stream count than the bottom 20-100 million songs combined:
| Artists | Name | Popularity | Stream Count |
|---|---|---|---|
| Lady Gaga, Bruno Mars | Die With A Smile | 100 | 3.075 Billion |
| Billie Eilish | BIRDS OF A FEATHER | 98 | 3.137 Billion |
| Bad Bunny | DtMF | 98 | 1.124 Billion |
SQLite Query
select json_group_array(artists.name), tracks.name, tracks.popularity
from tracks
join track_artists on track_rowid = tracks.rowid
join artists on artist_rowid = artists.rowid
where tracks.id in (select id from tracks order by popularity desc limit 3)
group by tracks.id;Note that the popularity is very time-dependent and not directly translatable into stream counts, so these top songs are basically arbitrary.
Songs
We have archived around 86 million songs from Spotify, ordering by popularity descending. While this only represents 37% of songs, it represents around 99.6% of listens:
Put another way, for any random song a person listens to, there is a 99.6% likelihood that it is part of the archive. We expect this number to be higher if you filter to only human-created songs. Do remember though that the error bar on listens for popularity 0 is large.
For popularity=0, we ordered tracks by a secondary importance metric based on artist followers and album popularity, and fetched in descending order.
We have stopped here due to the long tail end with diminishing returns (700TB+ additional storage for minor benefit), as well as the bad quality of songs with popularity=0 (many AI generated, hard to filter).
Torrents
Before diving into more fun stats, let’s look at how the collection itself is structured. It’s in two parts: metadata and music files, both of which are distributed through torrents.
The metadata torrents contain, based on statistical analysis, around 99.9% of artists, albums, tracks. The metadata is published as compact queryable SQLite databases. Care was taken, by doing API response reconstruction, that there is (almost) no data loss in the conversion from the API JSON.
The metadata for artists, albums, tracks is less than 200 GB compressed. The secondary metadata of audio analysis is 4TB compressed.
We look at more detail at the structure of the metadata at the end of this blog post.
Music Files
The data itself is distributed in the Anna’s Archive Containers (AAC) format. This is a standard which we created a few years ago for distributing files across multiple torrents. It is not to be confused with the Advanced Audio Coding (AAC) encoding format.
Since the original files contain zero metadata, as much metadata as possible was added to the OGG files, including title, url, ISRC, UPC, album art, replaygain information, etc. The invalid OGG data packet Spotify prepends to every track file was stripped — it is present in the track_files db.
For popularity>0, the quality is the original OGG Vorbis at 160kbit/s. Metadata was added without reencoding the audio (and an archive of diff files is available to reconstruct the original files from Spotify).
For popularity=0, the audio is reencoded to OGG Opus at 75kbit/s — sounding the same to most people, but noticeable to an expert.
There is a known bug where the REPLAYGAIN_ALBUM_PEAK vorbiscomment tag value is a copy-paste of REPLAYGAIN_ALBUM_GAIN instead of the correct value for many files.
The True Shuffle
Many people complain about how Spotify shuffles tracks. Since we have metadata for 99.9+% of tracks on Spotify, we can create a true shuffle across all songs on Spotify!
Example True Shuffle Playlist
$ sqlite3 spotify_clean.sqlite3
sqlite> .mode table
sqlite> with random_ids as (select value as inx, (abs(random())%(select max(rowid) from tracks)) as trowid from generate_series(0)) select inx,tracks.id,tracks.popularity,tracks.name from random_ids join tracks on tracks.rowid=trowid limit 20;
+-----+------------------------+------------+--------------------------------------------------------------+
| inx | id | popularity | name |
+-----+------------------------+------------+--------------------------------------------------------------+
| 0 | 7KS7cm2arAGA2VZaZ2XvNa | 0 | Just Derry |
+-----+------------------------+------------+--------------------------------------------------------------+
| 1 | 1BkLS2tmxD088l2ojUW5cv | 0 | Kapitel 37 - Aber erst wird gegessen - Schon wieder Weihnach |
| | | | ten mit der buckligen Verwandtschaft |
+-----+------------------------+------------+--------------------------------------------------------------+
| 2 | 5RSU7MELzCaPweG8ALmjLK | 0 | El Buen Pastor |
+-----+------------------------+------------+--------------------------------------------------------------+
| 3 | 1YNIl8AKIFltYH8O2coSoT | 0 | You Are The One |
+-----+------------------------+------------+--------------------------------------------------------------+
| 4 | 1GxMuEYWs6Lzbn2EcHAYVx | 0 | Waorani |
+-----+------------------------+------------+--------------------------------------------------------------+
| 5 | 4NhARf6pjwDpbyQdZeSsW3 | 0 | Magic in the Sand |
+-----+------------------------+------------+--------------------------------------------------------------+
| 6 | 7pDrZ6rGaO6FHk6QtTKvQo | 0 | Yo No Fui |
+-----+------------------------+------------+--------------------------------------------------------------+
| 7 | 15w4LBQ6rkf3QA2OiSMBRD | 25 | 你走 |
+-----+------------------------+------------+--------------------------------------------------------------+
| 8 | 5Tx7jRLKfYlay199QB2MSs | 0 | Soul Clap |
+-----+------------------------+------------+--------------------------------------------------------------+
| 9 | 3L7CkCD9595MuM0SVuBZ64 | 1 | Xuân Và Tuổi Trẻ |
+-----+------------------------+------------+--------------------------------------------------------------+
| 10 | 4S6EkSnfxlU5UQUOZs7bKR | 1 | Elle était belle |
+-----+------------------------+------------+--------------------------------------------------------------+
| 11 | 0ZIOUYrrArvSTq6mrbVqa1 | 0 | Kapitel 7.2 - Die Welt der Magie - 4 in 1 Sammelband: Weiße |
| | | | Magie | Medialität, Channeling & Trance | Divination & Wahrs |
| | | | agen | Energetisches Heilen |
+-----+------------------------+------------+--------------------------------------------------------------+
| 12 | 4VfKaW1X1FKv8qlrgKbwfT | 0 | Pura energia |
+-----+------------------------+------------+--------------------------------------------------------------+
| 13 | 1VugH5kD8tnMKAPeeeTK9o | 10 | Dalia |
+-----+------------------------+------------+--------------------------------------------------------------+
| 14 | 6NPPbOybTFLL0LzMEbVvuo | 4 | Teil 12 - Folge 2: Arkadien brennt |
+-----+------------------------+------------+--------------------------------------------------------------+
| 15 | 1VSVrAbaxNllk7ojNGXDym | 3 | Bre Petrunko |
+-----+------------------------+------------+--------------------------------------------------------------+
| 16 | 4NSmBO7uzkuES7vDLvHtX8 | 0 | Paranoia |
+-----+------------------------+------------+--------------------------------------------------------------+
| 17 | 7AHhiIXvx09DRZGQIsbcxB | 0 | Sand Underfoot Moments |
+-----+------------------------+------------+--------------------------------------------------------------+
| 18 | 0sitt32n4JoSM1ewOWL7hs | 0 | Start Over Again |
+-----+------------------------+------------+--------------------------------------------------------------+
| 19 | 080Zimdx271ixXbzdZOqSx | 3 | Auf all euren Wegen |
+-----+------------------------+------------+--------------------------------------------------------------+Or, filtering to only somewhat popular songs
sqlite> with random_ids as (select value as inx, (abs(random())%(select max(rowid) from tracks)) as trowid from generate_series(0)) select inx,tracks.id,tracks.popularity,albums.name as album_name,tracks.name from random_ids join tracks on tracks.rowid=trowid join albums on albums.rowid = album_rowid
where tracks.popularity >= 10 limit 20;
+-----+------------------------+------------+--------------------------------------+-------------------------------+
| inx | id | popularity | album_name | name |
+-----+------------------------+------------+--------------------------------------+-------------------------------+
| 32 | 1om6LphEpiLpl9irlOsnzb | 23 | The Essential Widespread Panic | Love Tractor |
| 47 | 2PCtPCRDia6spej5xcxbvW | 20 | Desatinos Desplumados | Sirena |
| 65 | 5wmR10WloZqVVdIpYhdaqq | 20 | Um Passeio pela Harpa Cristã - Vol 6 | As Santas Escrituras |
| 89 | 5xCuYNX3QlPsxhKLbWlQO9 | 11 | No Me Amenaces | No Me Amenaces |
| 96 | 2GRmiDIcIwhQnkxakNyUy4 | 16 | Very Bad Truth (Kingston Universi... | Kapitel 8.3 - Very Bad Truth |
| 98 | 5720pe1PjNXoMcbDPmyeLW | 11 | Kleiner Eisbär: Hilf mir fliegen! | Kapitel 06: Hilf mir fliegen! |
| 109 | 1mRXGNVsfD9UtFw6r5YtzF | 11 | Lunar Archive | Outdoor Seating |
| 110 | 5XOQwf6vkcJxWG9zgqVEWI | 19 | Teenage Dream | Firework |
| 125 | 0rbHOp8B4CpPXXZSekySvv | 15 | Previa y Cachengue 2025 | Debi tirar mas fotos |
| 145 | 4RGj8KyWGMjrUEseDTc3MO | 19 | High Noon over Camelot | "The Hierophant" |
| 158 | 1MebBcPcUNgdVRMSfzJIyS | 21 | RBS | Estar Vivo |
| 176 | 0E6h47PjbHJFno9IImwFFm | 17 | The Raga Guide | Bilaskhani Todi |
| 196 | 1QcziEkM8mZSm0hJ1rC2Ft | 14 | Meu Abraço | Meu Abraço |
| 204 | 33vRjP0CI7krO2KQ6YS1u7 | 14 | Joan Shelley | Pull Me Up One More Time |
| 231 | 3rnTIldZ0uHr5aooIwJjvF | 12 | Stjörnulífið | Illuminati |
| 246 | 6aVxXv5ywGL2xc2dg0I5jT | 10 | Family | Hana no Youni |
| 252 | 3ESGm5fRIOtzA7BfKlNIZy | 10 | Out Of Control | Let's Try Love Again |
| 297 | 4jZmhTVjIWBmFfnolYLmD5 | 18 | Blood Brothers | Faster and Louder |
| 298 | 0ebW1CJ4tYRx3VHfqbWzUh | 19 | Vibe da Faixa Rosa | Vibe da Faixa Rosa |
| 299 | 5xuK0SlWkAqs0w1sq6BZSk | 15 | Swingin Hammers | Hangman |
+-----+------------------------+------------+--------------------------------------+-------------------------------+More Stats
Here's some more statistics:
Tracks
We're curious about the peaks at whole minutes (particularly 2:00, 3:00, 4:00). If you know why this is, please let us know!
Some songs, especially popular songs, have 2, 3, or even 20 different versions. We can quantify this by counting number of songs per ISRC:
Each song on Spotify is only available on a specific set of markets. Most songs are available on most markets, but if we filter to popular songs, you can see a difference in availability (without filtering, the graph is almost flat):
Artists
Spotify provides a list of genres per artist (not per song). If we count the artists for each genre, we get this result:
Since each genre is very specific, we can also group genres and count the results:
We can also group artists by popularity. The resulting graph looks very similar to the tracks popularity graph:
The same graph for albums also looks the same.
Albums
If we group albums by release year, we see that more and more new music is added to Spotify, a lot of it likely automatically generated:
The amount of procedurally and AI generated content makes it hard to find what is actually valuable.
You can see that most songs on Spotify are singles, not part of an album.
Audio Features
We also scraped audio features generated by Spotify. They can be analyzed to find interesting trends.
This chart contains a lot of information. You can for example see that loudness correlates with energy and that BPM is normally distributed with a mean around 120.
A scrape of the three main APIs of Spotify: artists, albums, tracks. Each track exists in exactly one album, but each track and each album can have multiple artists.
The tables are an almost lossless representation of Spotify API JSON responses, care was taken during creation by always reconstructing the original JSON based on each inserted row, with minor exceptions.
CREATE TABLE `artists` ( `rowid` integer PRIMARY KEY NOT NULL, `id` text NOT NULL, `fetched_at` integer NOT NULL, `name` text NOT NULL, `followers_total` integer NOT NULL, `popularity` integer NOT NULL ); CREATE TABLE `artist_genres` ( `artist_rowid` integer NOT NULL, `genre` text NOT NULL, FOREIGN KEY (`artist_rowid`) REFERENCES `artists`(`rowid`) ); CREATE TABLE `artist_images` ( `artist_rowid` integer NOT NULL, `width` integer NOT NULL, `height` integer NOT NULL, `url` text NOT NULL, FOREIGN KEY (`artist_rowid`) REFERENCES `artists`(`rowid`) ); CREATE TABLE "artist_albums" ( `artist_rowid` integer NOT NULL, `album_rowid` integer NOT NULL, `is_appears_on` integer NOT NULL, `is_implicit_appears_on` integer NOT NULL, `index_in_album` integer, FOREIGN KEY (`artist_rowid`) REFERENCES `artists`(`rowid`), FOREIGN KEY (`album_rowid`) REFERENCES `albums`(`rowid`) ); CREATE TABLE `available_markets` ( `rowid` integer PRIMARY KEY NOT NULL, `available_markets` text NOT NULL ); CREATE TABLE `albums` ( `rowid` integer PRIMARY KEY NOT NULL, `id` text NOT NULL, `fetched_at` integer NOT NULL, `name` text NOT NULL, `album_type` text NOT NULL, `available_markets_rowid` integer NOT NULL, `external_id_upc` text, "external_id_amgid" text, `copyright_c` text, `copyright_p` text, `label` text NOT NULL, `popularity` integer NOT NULL, `release_date` text NOT NULL, `release_date_precision` text NOT NULL, `total_tracks` integer NOT NULL, FOREIGN KEY (`available_markets_rowid`) REFERENCES `available_markets`(`rowid`) ); CREATE TABLE "album_images" ( `album_rowid` integer NOT NULL, `width` integer NOT NULL, `height` integer NOT NULL, `url` text NOT NULL, FOREIGN KEY (`album_rowid`) REFERENCES `albums`(`rowid`) ); CREATE TABLE `tracks` ( `rowid` integer PRIMARY KEY NOT NULL, `id` text NOT NULL, `fetched_at` integer NOT NULL, `name` text NOT NULL, `preview_url` text, `album_rowid` integer NOT NULL, `track_number` integer NOT NULL, `external_id_isrc` text, `external_id_ean` text, `external_id_upc` text, `popularity` integer NOT NULL, `available_markets_rowid` integer NOT NULL, `disc_number` integer NOT NULL, `duration_ms` integer NOT NULL, `explicit` integer NOT NULL, FOREIGN KEY (`available_markets_rowid`) REFERENCES `available_markets`(`rowid`) ); CREATE TABLE `track_artists` ( `track_rowid` integer NOT NULL, `artist_rowid` integer NOT NULL, FOREIGN KEY (`track_rowid`) REFERENCES `tracks`(`rowid`), FOREIGN KEY (`artist_rowid`) REFERENCES `artists`(`rowid`) ); CREATE INDEX `artist_genres_artist_id` ON `artist_genres` (`artist_rowid`); CREATE INDEX `artist_genres_genre` ON `artist_genres` (`genre`); CREATE INDEX `artist_images_artist_id` ON `artist_images` (`artist_rowid`); CREATE UNIQUE INDEX `artists_id_unique` ON `artists` (`id`); CREATE INDEX `artists_name` ON `artists` (`name`); CREATE INDEX `artists_popularity` ON `artists` (`popularity`); CREATE INDEX `artists_followers` ON `artists` (`followers_total`); CREATE INDEX `artist_album_artist_id` ON "artist_albums" (`artist_rowid`); CREATE INDEX `artist_album_album_id` ON "artist_albums" (`album_rowid`); CREATE UNIQUE INDEX `albums_id_unique` ON `albums` (`id`); CREATE INDEX `album_name` ON `albums` (`name`); CREATE INDEX `album_popularity` ON `albums` (`popularity`); CREATE UNIQUE INDEX `available_markets_available_markets_unique` ON `available_markets` (`available_markets`); CREATE INDEX `track_artists_artist_id` ON `track_artists` (`artist_rowid`); CREATE INDEX `track_artists_track_id` ON `track_artists` (`track_rowid`); CREATE UNIQUE INDEX `tracks_id_unique` ON `tracks` (`id`); CREATE INDEX `tracks_popularity` ON `tracks` (`popularity`); CREATE INDEX `tracks_album` ON `tracks` (`album_rowid`); CREATE INDEX `album_images_album_id` ON `album_images` (`album_rowid`); CREATE INDEX tracks_isrc on tracks(external_id_isrc);A scrape of AudioFeatures objects from the Spotify API. One row per track.
CREATE TABLE `track_audio_features` ( `rowid` integer PRIMARY KEY NOT NULL, `track_id` text NOT NULL, `fetched_at` integer NOT NULL, `null_response` integer NOT NULL, `duration_ms` integer, `time_signature` integer, `tempo` integer, `key` integer, `mode` integer, `danceability` real, `energy` real, `loudness` real, `speechiness` real, `acousticness` real, `instrumentalness` real, `liveness` real, `valence` real ); CREATE UNIQUE INDEX `track_audio_features_track_id_unique` ON `track_audio_features` (`track_id`);A scrape of Playlist objects from the Spotify API. Requires the spotify_clean.sqlite3 database to map track_rowid to track_id.
Most playlists with < 1000 followers were excluded. Completeness unknown.
Row count: 6.6 million playlists with 1.7 billion playlist tracks.
CREATE TABLE "playlists" ( `rowid` integer PRIMARY KEY NOT NULL, `id` text NOT NULL, `snapshot_id` text NOT NULL, `fetched_at` integer NOT NULL, `name` text NOT NULL, `description` text, `collaborative` integer NOT NULL, `public` integer NOT NULL, `primary_color` text, `owner_id` text, `owner_display_name` text, `followers_total` integer, `tracks_total` integer NOT NULL ); CREATE UNIQUE INDEX `playlists_id_unique` ON `playlists` (`id`); CREATE INDEX `playlists_name` ON `playlists` (`name`); CREATE INDEX `playlists_owner_id` ON `playlists` (`owner_id`); CREATE INDEX `playlists_snapshot_id` ON `playlists` (`snapshot_id`); CREATE INDEX `playlists_followers` ON `playlists` (`followers_total`); CREATE TABLE `playlist_images` ( `playlist_rowid` integer NOT NULL, `width` integer, `height` integer, `url` text NOT NULL, FOREIGN KEY (`playlist_rowid`) REFERENCES `playlists`(`rowid`) ); CREATE INDEX `playlist_images_playlist_id` ON `playlist_images` (`playlist_rowid`); CREATE TABLE "playlist_tracks" ( `playlist_rowid` integer NOT NULL, `position` integer NOT NULL, `is_episode` integer NOT NULL, `track_rowid` integer, `id_if_not_in_tracks_table` text, `added_at` integer NOT NULL, `added_by_id` text, `primary_color` text, `video_thumbnail_url` text, `is_local` integer NOT NULL, `name_if_is_local` text, `uri_if_is_local` text, `album_name_if_is_local` text, `artists_name_if_is_local` text, `duration_ms_if_is_local` integer, PRIMARY KEY(`playlist_rowid`, `position`), FOREIGN KEY (`playlist_rowid`) REFERENCES `playlists`(`rowid`) ) WITHOUT ROWID;This is an almost lossless representation of the original Spotify API response. JSON reconstruction was tested during creation of all tables, with minor exceptions. For example, the original response JSON for playlists can be reconstructed with code similar to the following:
Playlist Reconstruction Code (Example)
function escapeURI(s) {
return encodeURIComponent(s).replace(
/[!()*]/g,
(c) => "%" + c.charCodeAt(0).toString(16).toUpperCase().padStart(2, "0")
);
}
function booleanToTrackType(is_episode) {
return is_episode ? "episode" : "track";
}
function reconstructTrack(track, track_id) {
let innerTrack = null;
if (track.is_local) {
innerTrack = {
album: {
album_type: null,
available_markets: [],
external_urls: {},
href: null,
id: null,
images: [],
name: track.album_name_if_is_local ?? "",
release_date: null,
release_date_precision: null,
type: "album",
uri: null,
artists: [],
},
artists: [
{
external_urls: {},
href: null,
id: null,
name: track.artists_name_if_is_local ?? "",
type: "artist",
uri: null,
},
],
available_markets: [],
explicit: false,
preview_url: null,
type: "track",
disc_number: 0,
external_ids: {},
external_urls: {},
href: null,
id: null,
duration_ms: track.duration_ms_if_is_local,
name: track.name_if_is_local,
uri: track.uri_if_is_local,
popularity: 0,
track_number: 0,
is_local: true,
tags: null,
};
} else if (track_id) {
innerTrack = {
type: booleanToTrackType(track.is_episode),
id: track_id,
};
} else if (track.id_if_not_in_tracks_table) {
innerTrack = {
type: booleanToTrackType(track.is_episode),
id: track.id_if_not_in_tracks_table,
};
}
return {
added_at: new Date(track.added_at).toISOString().replace(".000Z", "Z"),
added_by: {
id: track.added_by_id,
type: "user",
uri: track.added_by_id
? `spotify:user:${escapeURI(track.added_by_id)}`
: null,
href: `https://api.spotify.com/v1/users/${track.added_by_id}`,
external_urls: {
spotify: `https://open.spotify.com/user/${track.added_by_id}`,
},
},
is_local: track.is_local,
primary_color: track.primary_color,
video_thumbnail: {
url: track.video_thumbnail_url,
},
track: innerTrack,
};
}
function reconstructPlaylist({
inserted_playlist,
inserted_images,
inserted_tracks,
}) {
return {
id: inserted_playlist.id,
snapshot_id: inserted_playlist.snapshot_id,
name: inserted_playlist.name,
description: inserted_playlist.description || "",
collaborative: inserted_playlist.collaborative,
public: inserted_playlist.public,
primary_color: inserted_playlist.primary_color,
owner: {
id: inserted_playlist.owner_id,
display_name: inserted_playlist.owner_display_name,
type: "user",
uri: inserted_playlist.owner_id
? `spotify:user:${escapeURI(inserted_playlist.owner_id)}`
: null,
href: `https://api.spotify.com/v1/users/${inserted_playlist.owner_id}`,
external_urls: {
spotify: `https://open.spotify.com/user/${inserted_playlist.owner_id}`,
},
},
followers: {
href: null,
total: inserted_playlist.followers_total,
},
images:
inserted_images.length > 0
? inserted_images.map((img) => ({
url: img.url,
height: img.height,
width: img.width,
}))
: null,
tracks: {
href: "unimportant",
total: inserted_playlist.tracks_total,
limit: 100,
offset: 0,
next: null,
previous: null,
items: inserted_tracks.map(reconstructTrack),
},
external_urls: {
spotify: `https://open.spotify.com/playlist/${inserted_playlist.id}`,
},
href: `https://api.spotify.com/v1/playlists/${inserted_playlist.id}?additional_types=episode&locale=*`,
type: "playlist",
uri: `spotify:playlist:${inserted_playlist.id}`,
};
}The link between the tracks table in spotify_clean and the actual files we have.
Row count:
CREATE TABLE IF NOT EXISTS "track_files" ( `rowid` integer PRIMARY KEY NOT NULL, `track_id` text NOT NULL, `filename` text, `status` text NOT NULL, `reencoded_kbit_vbr` integer, `fetched_at` integer, `session_country` text, `sha256_original` text, `sha256_with_embedded_meta` text, `isrc_has_download` integer, `track_popularity` integer, `secondary_priority` real, `prefixed_ogg_packet` blob, `alternatives` text, `file_id_ogg_vorbis_96` text, `file_id_ogg_vorbis_160` text, `file_id_ogg_vorbis_320` text, `file_id_aac_24` text, `file_id_mp3_96` text, `language_of_performance` text, `artist_roles` text, `has_lyrics` integer, `licensor` text, `original_title` text, `version_title` text, `content_ratings` text
);Raw JSON API responses for audiobooks on Spotify. Contains around 700 thousand rows. Incomplete.
Raw JSON API responses for audiobook chapters on Spotify. Contains around 20 million rows. Incomplete.
Raw JSON API responses for shows (podcasts) on Spotify. Contains around 5 million rows. Incomplete.
Raw JSON API responses for (podcast) episodes on Spotify. Contains around 54 million rows. Incomplete.
Redirect responses for the artist endpoint.
annas_archive_spotify_2025_07_audio_analysis.torrent/##.json.zst
Raw JSON API responses for audio analysis on Spotify. Contains around 40 million rows, fetched in descending priority order. Many songs do not have an audio analysis (404). Incomplete.
annas_archive_spotify_2025_07_coverart.tar.torrent
Album art files. Correspond to last part of url from album_images. Indexed into directories with filename prefixes (after stripping the first 16 chars from the filename).
