Write Code to Build Your Own Playlist and Throw Away Spotify

I have been an enthusiastic user of MPD and mopidy for nearly two decades. I have already on how to leverage mopidy (with its tons of integrations, including Spotify, Tidal, YouTube, Bandcamp, Plex, TuneIn, SoundCloud, etc.), Snapcast (with its multi-room listening experience out of the box), and Platypush (with its automation hooks that allow you to easily create if-this-then-that rules for your music events) to take your listening experience to the next level while using open protocols and easily extensible open-source software. written an article There is a feature that I haven't yet covered in my previous articles, and that's the automation of your music collection. Spotify, Tidal and other music streaming services offer you features such as or playlists, filled with tracks that you may like, or newly released tracks that you may be interested in. Discovery Weekly Release Radar The problem is that these services come with heavy trade-offs: Their algorithms are closed. You don't know how Spotify figures out which songs should be picked in your smart playlists. In the past months, Spotify would often suggest me tracks from the same artists that I had already listened to or skipped in the past. There's no transparent way to tell the algorithm "hey, actually I'd like you to suggest me more of this kind of music - or maybe calculate suggestions only based on the music I've listened to in this time range, or maybe weigh this genre more". Those features are tightly coupled with the service you use. If you cancel your Spotify subscription, you lose those smart features as well. Companies like Spotify use such features as a lock-in mechanism - you can check out any time you like, but if you do then nobody else will provide you with their clever suggestions. After migrating from Spotify to Tidal in the past couple of months (TL;DR: Spotify f*cked up their developer experience multiple times over the past decade and their killing of libspotify without providing any alternatives was the last nail in the coffin for me) I felt like missing their smart mixes, discovery and new releases playlists - and, on the other hand, Tidal took a while to learn my listening habits, and even when it did it often generated smart playlists that were an inch below Spotify's. I asked myself why on earth my music discovery experience should be so tightly coupled to one single cloud service. And I decided that the time had come for me to automatically generate my service-agnostic music suggestions: it's not rocket science anymore, there are plenty of services that you can piggyback on to get artists or tracks similar to some music given as input, and there's just no excuses to feel locked in by Spotify, Google, Tidal or some other cloud music provider. In this article we'll cover how to: Use Platypush to automatically keep track of the music you listen to from any of your devices; Calculate the suggested tracks that may be similar to the music you've recently listened to by using the API; Last. FM Generate a playlist similar to Spotify's without relying on Spotify; Discover Weekly Get the newly released albums and singles by subscribing to an RSS feed; Generate a weekly playlist with the new releases by filtering those from artists that you've listened to at least once. Ingredients We will use Platypush to handle the following features: Store our listening history to a local database, or synchronize it with a scrobbling service like . last. fm Periodically inspect our newly listened tracks, and use the API to retrieve similar tracks. last.fm Generate a discover weekly playlist based on a simple score that ranks suggestions by matching scores against the tracks listened to on a certain period, and increases the weight of suggestions that occur multiple times. Monitor new releases from the RSS feed, and create a weekly playlist containing the items from artists that we have listened to at least once. newalbumreleases.net Release Radar This tutorial will require: A database to store your listening history and suggestions. The database initialization script has been tested against Postgres, but it should be easy to adapt it to MySQL or SQLite with some minimal modifications. A machine (it can be a RaspberryPi, a home server, a VPS, an unused tablet, etc.) to run the Platypush automation. A Spotify or Tidal account. The reported examples will generate the playlists on a Tidal account by using the Platypush plugin, but it should be straightforward to adapt them to Spotify by using the plugin, or even to YouTube by using the YouTube API, or even to local M3U playlists. music.tidal music.spotify Setting up the software Start by installing Platypush with , , and integrations: Tidal RSS Last. fm [sudo] pip install 'platypush[tidal,rss,lastfm]' If you want to use Spotify instead of Tidal then just remove from the list of extra dependencies - no extra dependencies are required for the . tidal Spotify plugin If you are planning to listen to music through mpd/mopidy, then you may also want to include in the list of extra dependencies, so Platypush can directly monitor your listening activity over the MPD protocol. mpd Let's then configure a simple configuration under : ~/.config/platypush/config.yaml music.tidal: # No configuration required # Or, if you use Spotify, create an app at https://developer.spotify.com and # add its credentials here # music.spotify: # client_id: client_id # client_secret: client_secret lastfm: api_key: your_api_key api_secret: your_api_secret username: your_user password: your_password # Subscribe to updates from newalbumreleases.net rss: subscriptions: - https://newalbumreleases.net/category/cat/feed/ # Optional, used to send notifications about generation issues to your # mobile/browser. You can also use Pushbullet, an email plugin or a chatbot if # you prefer. ntfy: # No configuration required if you want to use the default server at # https://ntfy.sh # Include the mpd plugin and backend if you are listening to music over # mpd/mopidy music.mpd: host: localhost port: 6600 backend.music.mopidy: host: localhost port: 6600 Start Platypush by running the command. The first time it should prompt you with a link required to authenticate your user. Open it in your browser and authorize the app - the next runs should no longer ask you to authenticate. platypush tidal.com Once the Platypush dependencies are in place, let's move to configure the database. Database configuration I'll assume that you have a Postgres database running somewhere, but the script below can be easily adapted also to other DBMS's. Database initialization script: -- New listened tracks will be pushed to the tmp_music table, and normalized by -- a trigger. drop table if exists tmp_music cascade; create table tmp_music( id serial not null, artist varchar(255) not null, title varchar(255) not null, album varchar(255), created_at timestamp with time zone default CURRENT_TIMESTAMP, primary key(id) ); -- This table will store the tracks' info drop table if exists music_track cascade; create table music_track( id serial not null, artist varchar(255) not null, title varchar(255) not null, album varchar(255), created_at timestamp with time zone default CURRENT_TIMESTAMP, primary key(id), unique(artist, title) ); -- Create an index on (artist, title), and ensure that the (artist, title) pair -- is unique create unique index track_artist_title_idx on music_track(lower(artist), lower(title)); create index track_artist_idx on music_track(lower(artist)); -- music_activity holds the listened tracks drop table if exists music_activity cascade; create table music_activity( id serial not null, track_id int not null, created_at timestamp with time zone default CURRENT_TIMESTAMP, primary key(id) ); -- music_similar keeps track of the similar tracks drop table if exists music_similar cascade; create table music_similar( source_track_id int not null, target_track_id int not null, match_score float not null, primary key(source_track_id, target_track_id), foreign key(source_track_id) references music_track(id), foreign key(target_track_id) references music_track(id) ); -- music_discovery_playlist keeps track of the generated discovery playlists drop table if exists music_discovery_playlist cascade; create table music_discovery_playlist( id serial not null, name varchar(255), created_at timestamp with time zone default CURRENT_TIMESTAMP, primary key(id) ); -- This table contains the track included in each discovery playlist drop table if exists music_discovery_playlist_track cascade; create table music_discovery_playlist_track( id serial not null, playlist_id int not null, track_id int not null, primary key(id), unique(playlist_id, track_id), foreign key(playlist_id) references music_discovery_playlist(id), foreign key(track_id) references music_track(id) ); -- This table contains the new releases from artist that we've listened to at -- least once drop table if exists new_release cascade; create table new_release( id serial not null, artist varchar(255) not null, album varchar(255) not null, genre varchar(255), created_at timestamp with time zone default CURRENT_TIMESTAMP, primary key(id), constraint u_artist_title unique(artist, album) ); -- This trigger normalizes the tracks inserted into tmp_track create or replace function sync_music_data() returns trigger as $$ declare track_id int; begin insert into music_track(artist, title, album) values(new.artist, new.title, new.album) on conflict(artist, title) do update set album = coalesce(excluded.album, old.album) returning id into track_id; insert into music_activity(track_id, created_at) values (track_id, new.created_at); delete from tmp_music where id = new.id; return new; end; $$ language 'plpgsql'; drop trigger if exists on_sync_music on tmp_music; create trigger on_sync_music after insert on tmp_music for each row execute procedure sync_music_data(); -- (Optional) accessory view to easily peek the listened tracks drop view if exists vmusic; create view vmusic as select t.id as track_id , t.artist , t.title , t.album , a.created_at from music_track t join music_activity a on t.id = a.track_id; Run the script on your database - if everything went smoothly then all the tables should be successfully created. Synchronizing your music activity Now that all the dependencies are in place, it's time to configure the logic to store your music activity in your database. If most of your music activity happens through mpd/mopidy, then storing your activity in the database is as simple as creating a hook on events that inserts any new played track on . Paste the following content to a new Platypush user script (e.g. ): NewPlayingTrackEvent tmp_music ~/.config/platypush/scripts/music/sync.py # ~/.config/platypush/scripts/music/sync.py from logging import getLogger from platypush.context import get_plugin from platypush.event.hook import hook from platypush.message.event.music import NewPlayingTrackEvent logger = getLogger('music_sync') # SQLAlchemy connection string that points to your database music_db_engine = 'postgresql+pg8000://dbuser:dbpass@dbhost/dbname' # Hook that react to NewPlayingTrackEvent events @hook(NewPlayingTrackEvent) def on_new_track_playing(event, **_): track = event.track # Skip if the track has no artist/title specified if not (track.get('artist') and track.get('title')): return logger.info( 'Inserting track: %s - %s', track['artist'], track['title'] ) db = get_plugin('db') db.insert( engine=music_db_engine, table='tmp_music', records=[ { 'artist': track['artist'], 'title': track['title'], 'album': track.get('album'), } for track in tracks ] ) Alternatively, if you also want to sync music activity that happens on other clients (such as the Spotify/Tidal app or web view, or over mobile devices), you may consider leveraging . (or its open alternative ) is a service compatible with most of the music players out there. Both Spotify and Tidal support scrobbling, the can grab any music activity on your phone and scrobble it, and there are even that allow you to keep track of any music activity from any browser tab. Last. fm Last.fm Libre.fm scrobbling Android app browser extensions So an alternative approach may be to send both your mpd/mopidy music activity, as well as your in-browser or mobile music activity, to / . The corresponding hook would be: last.fm libre.fm # ~/.config/platypush/scripts/music/sync.py from logging import getLogger from platypush.context import get_plugin from platypush.event.hook import hook from platypush.message.event.music import NewPlayingTrackEvent logger = getLogger('music_sync') # Hook that react to NewPlayingTrackEvent events @hook(NewPlayingTrackEvent) def on_new_track_playing(event, **_): track = event.track # Skip if the track has no artist/title specified if not (track.get('artist') and track.get('title')): return lastfm = get_plugin('lastfm') logger.info( 'Scrobbling track: %s - %s', track['artist'], track['title'] ) lastfm.scrobble( artist=track['artist'], title=track['title'], album=track.get('album'), ) If you go for the scrobbling way, then you may want to periodically synchronize your scrobble history to your local database - for example, through a cron that runs every 30 seconds: # ~/.config/platypush/scripts/music/scrobble2db.py import logging from datetime import datetime from platypush.context import get_plugin, Variable from platypush.cron import cron logger = logging.getLogger('music_sync') music_db_engine = 'postgresql+pg8000://dbuser:dbpass@dbhost/dbname' # Use this stored variable to keep track of the time of the latest # synchronized scrobble last_timestamp_var = Variable('LAST_SCROBBLED_TIMESTAMP') # This cron executes every 30 seconds @cron('* * * * * */30') def sync_scrobbled_tracks(**_): db = get_plugin('db') lastfm = get_plugin('lastfm') # Use the last.fm plugin to retrieve all the new tracks scrobbled since # the last check last_timestamp = int(last_timestamp_var.get() or 0) tracks = [ track for track in lastfm.get_recent_tracks().output if track.get('timestamp', 0) > last_timestamp ] # Exit if we have no new music activity if not tracks: return # Insert the new tracks on the database db.insert( engine=music_db_engine, table='tmp_music', records=[ { 'artist': track.get('artist'), 'title': track.get('title'), 'album': track.get('album'), 'created_at': ( datetime.fromtimestamp(track['timestamp']) if track.get('timestamp') else None ), } for track in tracks ] ) # Update the LAST_SCROBBLED_TIMESTAMP variable with the timestamp of the # most recent played track last_timestamp_var.set(max( int(t.get('timestamp', 0)) for t in tracks )) logger.info('Stored %d new scrobbled track(s)', len(tracks)) This cron will synchronize your scrobbling history to your local database, so we can use the local database as the source of truth for the next steps - no matter where the music was played from. To test the logic, simply restart Platypush, play some music from your favorite player(s), and check that everything gets inserted into the database - even if we are inserting tracks on the table, the listening history should be automatically normalized on the appropriate tables by the trigger that we created at initialization time. tmp_music Updating the suggestions Now that all the plumbing to get all of your listening histories in one data source is in place, let's move to the logic that recalculates the suggestions based on your listening history. We will again use the API to get tracks that are similar to those we listened to recently - I find suggestions are often more relevant than those of Spotify. last.fm last.fm For sake of simplicity, let's map the database tables to some SQLAlchemy ORM classes, so the upcoming SQL interactions can be notably simplified. The ORM model can be stored under e.g. : ~/.config/platypush/music/db.py # ~/.config/platypush/scripts/music/db.py from sqlalchemy import create_engine from sqlalchemy.ext.automap import automap_base from sqlalchemy.orm import sessionmaker, scoped_session music_db_engine = 'postgresql+pg8000://dbuser:dbpass@dbhost/dbname' engine = create_engine(music_db_engine) Base = automap_base() Base.prepare(engine, reflect=True) Track = Base.classes.music_track TrackActivity = Base.classes.music_activity TrackSimilar = Base.classes.music_similar DiscoveryPlaylist = Base.classes.music_discovery_playlist DiscoveryPlaylistTrack = Base.classes.music_discovery_playlist_track NewRelease = Base.classes.new_release def get_db_session(): session = scoped_session(sessionmaker(expire_on_commit=False)) session.configure(bind=engine) return session() Then create a new user script under e.g. with the following content: ~/.config/platypush/scripts/music/suggestions.py # ~/.config/platypush/scripts/music/suggestions.py import logging from sqlalchemy import tuple_ from sqlalchemy.dialects.postgresql import insert from sqlalchemy.sql.expression import bindparam from platypush.context import get_plugin, Variable from platypush.cron import cron from scripts.music.db import ( get_db_session, Track, TrackActivity, TrackSimilar ) logger = logging.getLogger('music_suggestions') # This stored variable will keep track of the latest activity ID for which the # suggestions were calculated last_activity_id_var = Variable('LAST_PROCESSED_ACTIVITY_ID') # A cronjob that runs every 5 minutes and updates the suggestions @cron('*/5 * * * *') def refresh_similar_tracks(**_): last_activity_id = int(last_activity_id_var.get() or 0) # Retrieve all the tracks played since the latest synchronized activity ID # that don't have any similar tracks being calculated yet with get_db_session() as session: recent_tracks_without_similars = \ _get_recent_tracks_without_similars(last_activity_id) try: if not recent_tracks_without_similars: raise StopIteration( 'All the recent tracks have processed suggestions') # Get the last activity_id batch_size = 10 last_activity_id = ( recent_tracks_without_similars[:batch_size][-1]['activity_id']) logger.info( 'Processing suggestions for %d/%d tracks', min(batch_size, len(recent_tracks_without_similars)), len(recent_tracks_without_similars)) # Build the track_id -> [similar_tracks] map similars_by_track = { track['track_id']: _get_similar_tracks(track['artist'], track['title']) for track in recent_tracks_without_similars[:batch_size] } # Map all the similar tracks in an (artist, title) -> info data structure similar_tracks_by_artist_and_title = \ _get_similar_tracks_by_artist_and_title(similars_by_track) if not similar_tracks_by_artist_and_title: raise StopIteration('No new suggestions to process') # Sync all the new similar tracks to the database similar_tracks = \ _sync_missing_similar_tracks(similar_tracks_by_artist_and_title) # Link listened tracks to similar tracks with get_db_session() as session: stmt = insert(TrackSimilar).values({ 'source_track_id': bindparam('source_track_id'), 'target_track_id': bindparam('target_track_id'), 'match_score': bindparam('match_score'), }).on_conflict_do_nothing() session.execute( stmt, [ { 'source_track_id': track_id, 'target_track_id': similar_tracks[(similar['artist'], similar['title'])].id, 'match_score': similar['score'], } for track_id, similars in similars_by_track.items() for similar in (similars or []) if (similar['artist'], similar['title']) in similar_tracks ] ) session.flush() session.commit() except StopIteration as e: logger.info(e) last_activity_id_var.set(last_activity_id) logger.info('Suggestions updated') def _get_similar_tracks(artist, title): """ Use the last.fm API to retrieve the tracks similar to a given artist/title pair """ import pylast lastfm = get_plugin('lastfm') try: return lastfm.get_similar_tracks( artist=artist, title=title, limit=10, ) except pylast.PyLastError as e: logger.warning( 'Could not find tracks similar to %s - %s: %s', artist, title, e ) def _get_recent_tracks_without_similars(last_activity_id): """ Get all the tracks played after a certain activity ID that don't have any suggestions yet. """ with get_db_session() as session: return [ { 'track_id': t[0], 'artist': t[1], 'title': t[2], 'activity_id': t[3], } for t in session.query( Track.id.label('track_id'), Track.artist, Track.title, TrackActivity.id.label('activity_id'), ) .select_from( Track.__table__ .join( TrackSimilar, Track.id == TrackSimilar.source_track_id, isouter=True ) .join( TrackActivity, Track.id == TrackActivity.track_id ) ) .filter( TrackSimilar.source_track_id.is_(None), TrackActivity.id > last_activity_id ) .order_by(TrackActivity.id) .all() ] def _get_similar_tracks_by_artist_and_title(similars_by_track): """ Map similar tracks into an (artist, title) -> track dictionary """ similar_tracks_by_artist_and_title = {} for similar in similars_by_track.values(): for track in (similar or []): similar_tracks_by_artist_and_title[ (track['artist'], track['title']) ] = track return similar_tracks_by_artist_and_title def _sync_missing_similar_tracks(similar_tracks_by_artist_and_title): """ Flush newly calculated similar tracks to the database. """ logger.info('Syncing missing similar tracks') with get_db_session() as session: stmt = insert(Track).values({ 'artist': bindparam('artist'), 'title': bindparam('title'), }).on_conflict_do_nothing() session.execute(stmt, list(similar_tracks_by_artist_and_title.values())) session.flush() session.commit() tracks = session.query(Track).filter( tuple_(Track.artist, Track.title).in_( similar_tracks_by_artist_and_title ) ).all() return { (track.artist, track.title): track for track in tracks } Restart Platypush and let it run for a bit. The cron will operate in batches of 10 items each (it can be easily customized), so after a few minutes your the table should start getting populated. music_suggestions Generating the discovery playlist So far we have achieved the following targets: We have a piece of logic that synchronizes all of our listening histories to a local database. We have a way to synchronize / scrobbles to the same database as well. last.fm libre.fm We have a cronjob that periodically scans our listening history and fetches the suggestions through the API. last.fm Now let's put it all together with a cron that runs every week (or daily, or at whatever interval we like) that does the following: It retrieves our listening history over the specified period. It retrieves the suggested tracks associated with our listening history. It excludes the tracks that we've already listened to, or that have already been included in previous discovery playlists. It generates a discovery playlist with those tracks, ranked according to a simple score: Where ρ(i) is the ranking of the suggested -th suggested track, is the set of listened tracks that have the -th track among its similarities, and is the match score between and as reported by the API. i L(i) i m(i, j) i j last.fm Let's put all these pieces together in a cron defined in e.g. : ~/.config/platypush/scripts/music/discovery.py # ~/.config/platypush/scripts/music/discovery.py import logging from datetime import date, timedelta from platypush.context import get_plugin from platypush.cron import cron from scripts.music.db import ( get_db_session, Track, TrackActivity, TrackSimilar, DiscoveryPlaylist, DiscoveryPlaylistTrack ) logger = logging.getLogger('music_discovery') def get_suggested_tracks(days=7, limit=25): """ Retrieve the suggested tracks from the database. :param days: Look back at the listen history for the past days (default: 7). :param limit: Maximum number of track in the discovery playlist (default: 25). """ from sqlalchemy import func listened_activity = TrackActivity.__table__.alias('listened_activity') suggested_activity = TrackActivity.__table__.alias('suggested_activity') with get_db_session() as session: return [ { 'track_id': t[0], 'artist': t[1], 'title': t[2], 'score': t[3], } for t in session.query( Track.id, func.min(Track.artist), func.min(Track.title), func.sum(TrackSimilar.match_score).label('score'), ) .select_from( Track.__table__ .join( TrackSimilar.__table__, Track.id == TrackSimilar.target_track_id ) .join( listened_activity, listened_activity.c.track_id == TrackSimilar.source_track_id, ) .join( suggested_activity, suggested_activity.c.track_id == TrackSimilar.target_track_id, isouter=True ) .join( DiscoveryPlaylistTrack, Track.id == DiscoveryPlaylistTrack.track_id, isouter=True ) ) .filter( # The track has not been listened suggested_activity.c.track_id.is_(None), # The track has not been suggested already DiscoveryPlaylistTrack.track_id.is_(None), # Filter by recent activity listened_activity.c.created_at >= date.today() - timedelta(days=days) ) .group_by(Track.id) # Sort by aggregate match score .order_by(func.sum(TrackSimilar.match_score).desc()) .limit(limit) .all() ] def search_remote_tracks(tracks): """ Search for Tidal tracks given a list of suggested tracks. """ # If you use Spotify instead of Tidal, simply replacing `music.tidal` # with `music.spotify` here should suffice. tidal = get_plugin('music.tidal') found_tracks = [] for track in tracks: query = track['artist'] + ' ' + track['title'] logger.info('Searching "%s"', query) results = ( tidal.search(query, type='track', limit=1).output.get('tracks', []) ) if results: track['remote_track_id'] = results[0]['id'] found_tracks.append(track) else: logger.warning('Could not find "%s" on TIDAL', query) return found_tracks def refresh_discover_weekly(): # If you use Spotify instead of Tidal, simply replacing `music.tidal` # with `music.spotify` here should suffice. tidal = get_plugin('music.tidal') # Get the latest suggested tracks suggestions = search_remote_tracks(get_suggested_tracks()) if not suggestions: logger.info('No suggestions available') return # Retrieve the existing discovery playlists # Our naming convention is that discovery playlist names start with # "Discover Weekly" - feel free to change it playlists = tidal.get_playlists().output discover_playlists = sorted( [ pl for pl in playlists if pl['name'].lower().startswith('discover weekly') ], key=lambda pl: pl.get('created_at', 0) ) # Delete all the existing discovery playlists # (except the latest one). We basically keep two discovery playlists at the # time in our collection, so you have two weeks to listen to them before they # get deleted. Feel free to change this logic by modifying the -1 parameter # with e.g. -2, -3 etc. if you want to store more discovery playlists. for playlist in discover_playlists[:-1]: logger.info('Deleting playlist "%s"', playlist['name']) tidal.delete_playlist(playlist['id']) # Create a new discovery playlist playlist_name = f'Discover Weekly [{date.today().isoformat()}]' pl = tidal.create_playlist(playlist_name).output playlist_id = pl['id'] tidal.add_to_playlist( playlist_id, [t['remote_track_id'] for t in suggestions], ) # Add the playlist to the database with get_db_session() as session: pl = DiscoveryPlaylist(name=playlist_name) session.add(pl) session.flush() session.commit() # Add the playlist entries to the database with get_db_session() as session: for track in suggestions: session.add( DiscoveryPlaylistTrack( playlist_id=pl.id, track_id=track['track_id'], ) ) session.commit() logger.info('Discover Weekly playlist updated') @cron('0 6 * * 1') def refresh_discover_weekly_cron(**_): """ This cronjob runs every Monday at 6 AM. """ try: refresh_discover_weekly() except Exception as e: logger.exception(e) # (Optional) If anything went wrong with the playlist generation, send # a notification over ntfy ntfy = get_plugin('ntfy') ntfy.send_message( topic='mirrored-notifications-topic', title='Discover Weekly playlist generation failed', message=str(e), priority=4, ) You can test the cronjob without having to wait for the next Monday through your Python interpreter: >>> import os >>> >>> # Move to the Platypush config directory >>> path = os.path.join(os.path.expanduser('~'), '.config', 'platypush') >>> os.chdir(path) >>> >>> # Import and run the cron function >>> from scripts.music.discovery import refresh_discover_weekly_cron >>> refresh_discover_weekly_cron() If everything went well, you should soon see a new playlist in your collection named . Congratulations! Discover Weekly [date] Release Radar playlist Another great feature of Spotify and Tidal is the ability to provide "release radar" playlists that contain new releases from artists that we may like. We now have a powerful way of creating such playlists ourselves though. We previously configured Platypush to subscribe to the RSS feed from . Populating our release radar playlist involves the following steps: newalbumreleases.net Creating a hook that reacts to events on this feed. NewFeedEntryEvent The hook will store new releases that match artists in our collection on the the table that we created when we initialized the database. new_release A cron will scan this table every week, search the tracks on Spotify/Tidal, and populate our playlist just like we did for . Discover Weekly Let's put these pieces together in a new user script stored under e.g. : ~/.config/platypush/scripts/music/releases.py # ~/.config/platypush/scripts/music/releases.py import html import logging import re import threading from datetime import date, timedelta from typing import Iterable, List from platypush.context import get_plugin from platypush.cron import cron from platypush.event.hook import hook from platypush.message.event.rss import NewFeedEntryEvent from scripts.music.db import ( music_db_engine, get_db_session, NewRelease ) create_lock = threading.RLock() logger = logging.getLogger(__name__) def _split_html_lines(content: str) -> List[str]: """ Utility method used to convert and split the HTML lines reported by the RSS feed. """ return [ l.strip() for l in re.sub( r'( ]*>)|( )', '\n', content ).split('\n') if l ] def _get_summary_field(title: str, lines: Iterable[str]) -> str | None: """ Parse the fields of a new album from the feed HTML summary. """ for line in lines: m = re.match(rf'^{title}:\s+(.*)$', line.strip(), re.IGNORECASE) if m: return html.unescape(m.group(1)) @hook(NewFeedEntryEvent, feed_url='https://newalbumreleases.net/category/cat/feed/') def save_new_release(event: NewFeedEntryEvent, **_): """ This hook is triggered whenever the newalbumreleases.net has new entries. """ # Parse artist and album summary = _split_html_lines(event.summary) artist = _get_summary_field('artist', summary) album = _get_summary_field('album', summary) genre = _get_summary_field('style', summary) if not (artist and album): return # Check if we have listened to this artist at least once db = get_plugin('db') num_plays = int( db.select( engine=music_db_engine, query= ''' select count(*) from music_activity a join music_track t on a.track_id = t.id where artist = :artist ''', data={'artist': artist}, ).output[0].get('count', 0) ) # If not, skip it if not num_plays: return # Insert the new release on the database with create_lock: db.insert( engine=music_db_engine, table='new_release', records=[{ 'artist': artist, 'album': album, 'genre': genre, }], key_columns=('artist', 'album'), on_duplicate_update=True, ) def get_new_releases(days=7): """ Retrieve the new album releases from the database. :param days: Look at albums releases in the past days (default: 7) """ with get_db_session() as session: return [ { 'artist': t[0], 'album': t[1], } for t in session.query( NewRelease.artist, NewRelease.album, ) .select_from( NewRelease.__table__ ) .filter( # Filter by recent activity NewRelease.created_at >= date.today() - timedelta(days=days) ) .all() ] def search_tidal_new_releases(albums): """ Search for Tidal albums given a list of objects with artist and title. """ tidal = get_plugin('music.tidal') expanded_tracks = [] for album in albums: query = album['artist'] + ' ' + album['album'] logger.info('Searching "%s"', query) results = ( tidal.search(query, type='album', limit=1) .output.get('albums', []) ) if results: album = results[0] # Skip search results older than a year - some new releases may # actually be remasters/re-releases of existing albums if date.today().year - album.get('year', 0) > 1: continue expanded_tracks += ( tidal.get_album(results[0]['id']). output.get('tracks', []) ) else: logger.warning('Could not find "%s" on TIDAL', query) return expanded_tracks def refresh_release_radar(): tidal = get_plugin('music.tidal') # Get the latest releases tracks = search_tidal_new_releases(get_new_releases()) if not tracks: logger.info('No new releases found') return # Retrieve the existing new releases playlists playlists = tidal.get_playlists().output new_releases_playlists = sorted( [ pl for pl in playlists if pl['name'].lower().startswith('new releases') ], key=lambda pl: pl.get('created_at', 0) ) # Delete all the existing new releases playlists # (except the latest one) for playlist in new_releases_playlists[:-1]: logger.info('Deleting playlist "%s"', playlist['name']) tidal.delete_playlist(playlist['id']) # Create a new releases playlist playlist_name = f'New Releases [{date.today().isoformat()}]' pl = tidal.create_playlist(playlist_name).output playlist_id = pl['id'] tidal.add_to_playlist( playlist_id, [t['id'] for t in tracks], ) @cron('0 7 * * 1') def refresh_release_radar_cron(**_): """ This cron will execute every Monday at 7 AM. """ try: refresh_release_radar() except Exception as e: logger.exception(e) get_plugin('ntfy').send_message( topic='mirrored-notifications-topic', title='Release Radar playlist generation failed', message=str(e), priority=4, ) Just like in the previous case, it's quite easy to test that it works by simply running in the Python interpreter. Just like in the case of the discovery playlist, things will work also if you use Spotify instead of Tidal - just replace the plugin references with . refresh_release_radar_cron music.tidal music.spotify If it all goes as expected, you will get a new playlist named every Monday with the new releases from artists that you have listened to. New Releases [date] Conclusions Music junkies have the opportunity to discover a lot of new music today without ever leaving their music app. However, smart playlists provided by the major music cloud providers are usually implicit lock-ins, and the way they select the tracks that should end up in your playlists may not even be transparent, or even modifiable. After reading this article, you should be able to generate your discovery and new release playlists, without relying on suggestions from a specific music cloud. This could also make it easier to change your music provider: even if you decide to drop Spotify or Tidal, your music suggestions logic will follow you whenever you decide to go. Also Published here

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