| .idea | ||
| .VSCodeCounter | ||
| assets | ||
| documentation | ||
| src | ||
| website | ||
| .gitignore | ||
| build.sh | ||
| contribute.md | ||
| LICENSE | ||
| notes.md | ||
| README.md | ||
| requirements.txt | ||
| setup.py | ||
| test.db | ||
| version | ||
Music Kraken
Installation
You can find and get this project from either PyPI as a Python-Package, or simply the source code from GitHub. Note that even though everything SHOULD work cross-platform, I have only tested it on Ubuntu. If you enjoy this project, feel free to give it a star on GitHub.
# Install it with
pip install music-kraken
# and simply run it like this:
music-kraken
Notes for Python 3.9
Unfortunately I use features that newly git introduced in Python 3.10. So unfortunately you CAN'T run this programm with python 3.9. #10
Notes for WSL
If you choose to run it in WSL, make sure ~/.local/bin is added to your $PATH #2
Quick-Guide
Genre: First, the cli asks you to input a genre you want to download to. The options it gives you (if it gives you any) are all the folders you have in the music directory. You can also just input a new one.
What to download: After that it prompts you for a search. Here are a couple examples how you can search:
> #a <any artist>
searches for the artist <any artist>
> #a <any artist> #r <any releas>
searches for the release (album) <any release> by the artist <any artist>
> #r <any release> Me #t <any track>
searches for the track <any track> from the release <any relaese>
After searching with this syntax, it prompts you with multiple results. You can either choose one of those by inputing its id int, or you can search for a new query.
After you chose either an artist, a release group, a release, or a track by its id, download it by inputting the string ok. My downloader will download it automatically for you.
CONTRIBUTE
I am happy about every pull request. To contribute look here.
Matrix Space
I decided against creating a discord server, due to piracy communities get often banned from discord. A good and free Alternative are Matrix Spaces. I reccomend the use of the Client Element. It is completely open source.
Click this link (https://matrix.to/#/#music-kraken:matrix.org) to join.
Programming Interface / Use as Library
This application is 100\% centered around Data. Thus the most important thing for working with musik kraken is, to understand how I structured the data.
Quick Overview
- explanation of the Data Model
- how to use the Data Objects
- further Dokumentation of hopefully most relevant classes
- the old implementation
---
title: Quick Overview
---
sequenceDiagram
participant pg as Page (eg. YouTube, MB, Musify, ...)
participant obj as DataObjects (eg. Song, Artist, ...)
participant db as DataBase
obj ->> db: write
db ->> obj: read
pg -> obj: find a source for any page, for object.
obj -> pg: add more detailed data from according page.
obj -> pg: if available download audio to target.
Data Model
The Data Structure, that the whole programm is built on looks as follows:
---
title: Music Data
---
erDiagram
Target {
}
Lyrics {
}
Song {
}
Album {
}
Artist {
}
Label {
}
Source {
}
Source }o--|| Song : from
Source }o--|| Lyrics : from
Source }o--|| Album : from
Source }o--|| Artist : from
Source }o--|| Label : from
Song }o--o{ Album : AlbumSong
Album }o--o{ Artist : ArtistAlbum
Song }o--o{ Artist : features
Label }o--o{ Album : LabelAlbum
Label }o--o{ Artist : LabelSong
Song ||--o{ Lyrics : contains
Song ||--o{ Target : points
Ok now this WILL look intimidating, thus I break it down quickly.
That is also the reason I didn't add all Attributes here.
The most important Entities are:
- Song
- Album
- Artist
- Label
All of them (and Lyrics) can have multiple Sources, and every Source can only Point to one of those Element.
The Target Entity represents the location on the hard drive a Song has. One Song can have multiple download Locations.
The Lyrics Entity simply represents the Lyrics of each Song. One Song can have multiple Lyrics, e.g. Translations.
Here is the simplified Diagramm without only the main Entities.
---
title: simplified Music Data
---
erDiagram
Song {
}
Album {
}
Artist {
}
Label {
}
Song }o--o{ Album : AlbumSong
Album }o--o{ Artist : ArtistAlbum
Song }o--o{ Artist : features
Label }o--o{ Album : LabelAlbum
Label }o--o{ Artist : LabelSong
Looks way more manageable, doesn't it?
The reason every relation here is a n:m (many to many) relation is not, that it makes sense in the aspekt of modeling reality, but to be able to put data from many Sources in the same Data Model.
Every Service models Data a bit different, and projecting a one-to-many relationship to a many to many relationship without data loss is easy. The other way around it is basically impossible
Data Objects
Not 100% accurate yet and might change slightly
Creation
# importing the libraries I build on
from music_kraken import objects
import pycountry
song = objects.Song(
genre="HS Core",
title="Vein Deep in the Solution",
length=666,
isrc="US-S1Z-99-00001",
tracksort=2,
target=[
objects.Target(file="song.mp3", path="example")
],
lyrics_list=[
objects.Lyrics(text="these are some depressive lyrics", language="en"),
objects.Lyrics(text="Dies sind depressive Lyrics", language="de")
],
source_list=[
objects.Source(objects.SourcePages.YOUTUBE, "https://youtu.be/dfnsdajlhkjhsd"),
objects.Source(objects.SourcePages.MUSIFY, "https://ln.topdf.de/Music-Kraken/")
],
album_list=[
objects.Album(
title="One Final Action",
date=objects.ID3Timestamp(year=1986, month=3, day=1),
language=pycountry.languages.get(alpha_2="en"),
label_list=[
objects.Label(name="an album label")
],
source_list=[
objects.Source(objects.SourcePages.ENCYCLOPAEDIA_METALLUM, "https://www.metal-archives.com/albums/I%27m_in_a_Coffin/One_Final_Action/207614")
]
),
],
main_artist_list=[
objects.Artist(
name="I'm in a coffin",
source_list=[
objects.Source(
objects.SourcePages.ENCYCLOPAEDIA_METALLUM,
"https://www.metal-archives.com/bands/I%27m_in_a_Coffin/127727"
)
]
),
objects.Artist(name="some_split_artist")
],
feature_artist_list=[
objects.Artist(
name="Ruffiction",
label_list=[
objects.Label(name="Ruffiction Productions")
]
)
],
)
print(song.option_string)
for album in song.album_collection:
print(album.option_string)
for artist in song.main_artist_collection:
print(artist.option_string)
If you just want to start implementing, then just use the code example, I don't care.
For those who don't want any bugs and use it as intended (which is recommended, cuz I am only one person so there are defs bugs) continue reading.
Appending and Merging data
If you want to append for example a Song to an Album, you obviously need to check beforehand if the Song already exists in the Album, and if so, you need to merge their data in one Song object, to not loose any Information.
This is how I solve this problem:
---
title: "Collection.append(music_object: MusicObject)"
---
flowchart TD
exist("""
<b>Check if music_object already exists.</b>
<hr>
Gets all indexing values with <code>music_object.indexing_values</code>.
If any returned value exists in <code>Collection._attribute_to_object_map</code>,
the music_object exists
""")
subgraph merge["Merging"]
_merge("""merges the passed in object in the already
existing whith <code>existing.merge(new)</code>""")
_map("""In case a new source or something simmilar
has been addet, it maps the existing object again.
""")
_merge --> _map
end
subgraph add["Adding"]
__map("""map the values from <code>music_object.indexing_values</code>
to <code>Collection._attribute_to_object_map</code> by writing
those values in the map as keys, and the class I wanna add as values.
""")
_add("""add the new music object to <code>_data</code>""")
__map --> _add
end
exist-->|"if it doesn't exist"|add --> return
exist-->|"if already exists"|merge --> return
This is Implemented in music_kraken.objects.Collection.append(). The merging which is mentioned in the flowchart is explained in the documentation of DatabaseObject.merge().
The indexing values are defined in the superclass DatabaseObject and get implemented for each Object seperately. I will just give as example its implementation for the Song class:
@property
def indexing_values(self) -> List[Tuple[str, object]]:
return [
('id', self.id),
('title', self.unified_title),
('barcode', self.barcode),
*[('url', source.url) for source in self.source_collection]
]