Development/Summer of Code/2023/ListenBrainz: Difference between revisions
RobertKaye (talk | contribs) No edit summary |
|||
| Line 26: | Line 26: | ||
This project could be a little tricky -- the quality of the playlists generated by this project depend very heavily on the quality of the datasets that feed into it. In particular, the artist and recording similarity data sets will play a very important role, but these datasets may not be up to the needed standards to create good playlists. However, this does not invalidate this project nor would it cause us to fail the student -- it is understood that the output of this project will improve as the underlying data improves. |
This project could be a little tricky -- the quality of the playlists generated by this project depend very heavily on the quality of the datasets that feed into it. In particular, the artist and recording similarity data sets will play a very important role, but these datasets may not be up to the needed standards to create good playlists. However, this does not invalidate this project nor would it cause us to fail the student -- it is understood that the output of this project will improve as the underlying data improves. |
||
=== Integrate music streaming from Funkwhale Pods === |
|||
=== Create a 'Release Radar' plugin for the Troi toolkit === |
|||
Proposed mentors: '' |
Proposed mentors: ''lucifer''<br> |
||
Languages/skills: Python, |
Languages/skills: Python/Flask, Typescript/React<br> |
||
Estimated Project Length: 175 hours<br> |
Estimated Project Length: Can be 175 or 350 hours depending on the integration/service chosen.<br> |
||
Difficulty: Easy<br> |
|||
Expected outcomes: One or more finished, debugged and tested plugins for Troi. |
|||
Expected Outcomes: A new music service integration for users to play and record listens on ListenBrainz. |
|||
LB has a number of music discovery features that use BrainzPlayer to facilitate track playback. BrainzPlayer (BP) is a custom React component in LB that uses multiple data sources to search and play a track. As of now, it supports Spotify, Youtube and Soundcloud as a backend. LB would like to be able to stream music from a private [Funkwhale pod https://funkwhale.audio/pod] and to do that the following major tasks need to be completed. 1) Add Funkwhale as a service to our music services backend; this will require some schema changes/extensions in order to have user specify the URL of their pod as part of the music service configuration. 2) Add the react based front-end UI to that allows users to setup their own Funkwhale pods as a service. 3) Add support to BrainzPlayer for logging into the Funkwhale OAuth API and streaming music from it. |
|||
Our [https://github.com/metabrainz/troi-recommendation-playground troi recommendation toolkit] is our playground for developing recommendation algorithms. The toolkit already knows how to fetch data from ListenBrainz for stats, collaborative filtered recommended tracks, similar artists and similar recordings. From MusicBrainz it can fetch needed metadata such as genres and tags. This project should generate a playlist every Friday that is a collection of selected tracks that have been recently released (last 2 weeks or so) by artists that are in a given users top artists list. We will have an API endpoint that will list recent releases for a given user, which will be implemented by a MetaBrainz team member, and your Troi plugin should select tracks from these releases and make an exploration playlist from these tracks. |
|||
=== Integrate music streaming from Navidrome === |
|||
However, some care must be taken to not select ALL the tracks from a new release, but instead to pick some tracks that we think might be interesting to the user. How would you do this? This question is hard to answer on your own -- you will be required to engage with the ListenBrainz team in IRC to discuss this feature in detail before you make your proposal. Any proposal that does not engage the community to design this feature will not be considered for acceptance, due to the nature of this project. |
|||
| ⚫ | |||
Proposed mentors: ''lucifer''<br> |
Proposed mentors: ''lucifer''<br> |
||
| Line 45: | Line 44: | ||
Expected Outcomes: A new music service integration for users to play and record listens on ListenBrainz. |
Expected Outcomes: A new music service integration for users to play and record listens on ListenBrainz. |
||
LB has a number of music discovery features that use BrainzPlayer to facilitate track playback. BrainzPlayer (BP) is a custom React component in LB that uses multiple data sources to search and play a track. As of now, it supports Spotify, Youtube and Soundcloud as a backend. LB |
LB has a number of music discovery features that use BrainzPlayer to facilitate track playback. BrainzPlayer (BP) is a custom React component in LB that uses multiple data sources to search and play a track. As of now, it supports Spotify, Youtube and Soundcloud as a backend. LB would like to be able to stream music from a private [https://www.navidrome.org/ Navidrome instance] and to do that the following major tasks need to be completed. 1) Add Navidrome as a service to our music services backend; this will require some schema changes/extensions in order to have user specify the URL of their pod as part of the music service configuration. 2) Add the react based front-end UI to that allows users to setup their own Navidrome instance as a service. 3) Add support to BrainzPlayer for logging into the Navidrom streaming API and playing music from it. |
||
| ⚫ | |||
=== Coalesce feature in ListenBrainz === |
|||
Proposed mentors: |
Proposed mentors: ''lucifer''<br> |
||
Languages/skills: Python, React |
Languages/skills: Python/Flask, Typescript/React<br> |
||
Estimated Project Length: 175 |
Estimated Project Length: Can be 175 or 350 hours depending on the integration/service chosen.<br> |
||
Difficulty: |
Difficulty: Easy<br> |
||
Expected |
Expected Outcomes: A new music service integration for users to play and record listens on ListenBrainz. |
||
LB has a number of music discovery features that use BrainzPlayer to facilitate track playback. BrainzPlayer (BP) is a custom React component in LB that uses multiple data sources to search and play a track. As of now, it supports Spotify, Youtube and Soundcloud as a backend. LB also supports linking a Spotify account to record listening history. We have looked into some other services and found that Deezer provides the music playback and recording listening history capability. Integrating these services into LB would make for a good SoC project. |
|||
Our [https://github.com/metabrainz/troi-recommendation-playground troi recommendation toolkit] is our playground for developing recommendation algorithms. The toolkit already knows how to fetch data from ListenBrainz for stats, collaborative filtered recommended tracks, similar artists and similar recordings. From MusicBrainz it can fetch needed metadata such as genres and tags. The goal of this project is to create a plugin to generate playlists based on the listening habits of two or more users (similar to the Spotify blend feature). |
|||
=== UI/UX improvements to our Fresh Releases page === |
|||
Unlike other Troi projects, this project involves a fair deal of frontend and backend work in the ListenBrainz server as well. A UI will be needed to allow multiple users to consent to creating a playlist with their combined interests. The UI should allow users to optionally configure different parameters for creating such a playlist. New database tables and API endpoints will be needed on the backend side to store these parameters and requests to generate combined playlists. |
|||
Proposed mentors: ''mayhem''<br> |
|||
To connect the Troi and the ListenBrainz server, a background running process could read the database and invoke Troi patches accordingly. Finally, the actual Troi patch to generate the playlist needs to be written. |
|||
Languages/skills: React<br> |
|||
Estimated Project Length: 175 hours, possible 350<br> |
|||
Expected outcomes: A set of PRs that improve the UI/UX of our Fresh Releases page, ready to be released into production. |
|||
Last summer we had a student implement our [https://listenbrainz.org/explore/fresh-releases Fresh Releases] page that shows what releases have been recently added to MusicBrainz. Intended as a music discovery tool, this page allows all users to browse new releases and people who have submitted listens to ListenBrainz can have a customized page that shows Fresh Releases relevant to their past listening history. We find that the page is rather quite interesting, but our student wasn't able to really get the UI/UX into a state where we are happy with it. Our UI/UX designer has worked out what the improved Fresh Releases UI should look like and now we are looking for a student to implement these UI improvements. We anticipate that this is a 100% javascript/react project with a focus on creating a slick and smooth UI for this rather interesting page. |
|||
There are a lot of parts in this project so it is fine if the contributor only wants to do some of those. These details should be discussed with the mentors beforehand so that the appropriate project schedule and length can be worked out. |
|||
Revision as of 15:59, 13 February 2023
ListenBrainz allows users to store a list of songs that they listen to and get personalised recommendations. Read more information on its homepage.
Getting started
(see also: Getting started with GSoC)
If you want to work on ListenBrainz you should show that you are able to set up the server software and understand how some of the infrastructure works. Here are some things that we might ask you about
- Show that you understand the goals that ListenBrainz wants to achieve, which are written on its homepage
- Create an oauth application on the MusicBrainz website and add the configuration information to your ListenBrainz server. Use this to log in to your server with your MusicBrainz details
- Use the import script that is part of the ListenBrainz server to load scrobbles from last.fm to your ListenBrainz server, or the main ListenBrainz server
- Use your preferred programming language to write a submission tool that can send Listen data to your local ListenBrainz server. You could make up some fake data for song names and artists. This data doesn't have to be real.
- Try and delete the ListenBrainz database on your local server to remove the fake data that you added.
- Look at the list of tickets that we have open for ListenBrainz and see if you understand what tasks the tickets involve
- If you want to, see if you can contribute to fixing a ticket. Either add a comment to the ticket or ask in IRC for clarification if you don't understand what the ticket means
We're adding a number of new social features to ListenBrainz that we hope will enable people discover more music they like and users who have similar music tastes to their own. We're working on some of these features now, but we will need to get help for other features:
Create 'More Tracks Like This' music recommendation plugin for the Troi toolkit
Proposed mentors: mayhem
Languages/skills: Python, possibly Postgres.
Estimated Project Length: Likely 175 hours unless we decide to expand the scope of the project.
Expected outcomes: One or more finished, debugged and tested plugins for Troi.
Our troi recommendation toolkit is our playground for developing recommendation algorithms. The toolkit already knows how to fetch data from ListenBrainz for stats, collaborative filtered recommended tracks, similar artists and similar recordings. From MusicBrainz it can fetch needed metadata such as genres and tags. The goal of this project is to take in one of more seed song MBIDs and then use the above listed data sets to attempt to find recordings that are similar enough in order to make a playlist of tracks that have a similar sound and feel to the given seed tracks.
This project could be a little tricky -- the quality of the playlists generated by this project depend very heavily on the quality of the datasets that feed into it. In particular, the artist and recording similarity data sets will play a very important role, but these datasets may not be up to the needed standards to create good playlists. However, this does not invalidate this project nor would it cause us to fail the student -- it is understood that the output of this project will improve as the underlying data improves.
Integrate music streaming from Funkwhale Pods
Proposed mentors: lucifer
Languages/skills: Python/Flask, Typescript/React
Estimated Project Length: Can be 175 or 350 hours depending on the integration/service chosen.
Difficulty: Easy
Expected Outcomes: A new music service integration for users to play and record listens on ListenBrainz.
LB has a number of music discovery features that use BrainzPlayer to facilitate track playback. BrainzPlayer (BP) is a custom React component in LB that uses multiple data sources to search and play a track. As of now, it supports Spotify, Youtube and Soundcloud as a backend. LB would like to be able to stream music from a private [Funkwhale pod https://funkwhale.audio/pod] and to do that the following major tasks need to be completed. 1) Add Funkwhale as a service to our music services backend; this will require some schema changes/extensions in order to have user specify the URL of their pod as part of the music service configuration. 2) Add the react based front-end UI to that allows users to setup their own Funkwhale pods as a service. 3) Add support to BrainzPlayer for logging into the Funkwhale OAuth API and streaming music from it.
Proposed mentors: lucifer
Languages/skills: Python/Flask, Typescript/React
Estimated Project Length: Can be 175 or 350 hours depending on the integration/service chosen.
Difficulty: Easy
Expected Outcomes: A new music service integration for users to play and record listens on ListenBrainz.
LB has a number of music discovery features that use BrainzPlayer to facilitate track playback. BrainzPlayer (BP) is a custom React component in LB that uses multiple data sources to search and play a track. As of now, it supports Spotify, Youtube and Soundcloud as a backend. LB would like to be able to stream music from a private Navidrome instance and to do that the following major tasks need to be completed. 1) Add Navidrome as a service to our music services backend; this will require some schema changes/extensions in order to have user specify the URL of their pod as part of the music service configuration. 2) Add the react based front-end UI to that allows users to setup their own Navidrome instance as a service. 3) Add support to BrainzPlayer for logging into the Navidrom streaming API and playing music from it.
Integrate Deezer music streaming service for recording listens and playing music
Proposed mentors: lucifer
Languages/skills: Python/Flask, Typescript/React
Estimated Project Length: Can be 175 or 350 hours depending on the integration/service chosen.
Difficulty: Easy
Expected Outcomes: A new music service integration for users to play and record listens on ListenBrainz.
LB has a number of music discovery features that use BrainzPlayer to facilitate track playback. BrainzPlayer (BP) is a custom React component in LB that uses multiple data sources to search and play a track. As of now, it supports Spotify, Youtube and Soundcloud as a backend. LB also supports linking a Spotify account to record listening history. We have looked into some other services and found that Deezer provides the music playback and recording listening history capability. Integrating these services into LB would make for a good SoC project.
UI/UX improvements to our Fresh Releases page
Proposed mentors: mayhem
Languages/skills: React
Estimated Project Length: 175 hours, possible 350
Expected outcomes: A set of PRs that improve the UI/UX of our Fresh Releases page, ready to be released into production.
Last summer we had a student implement our Fresh Releases page that shows what releases have been recently added to MusicBrainz. Intended as a music discovery tool, this page allows all users to browse new releases and people who have submitted listens to ListenBrainz can have a customized page that shows Fresh Releases relevant to their past listening history. We find that the page is rather quite interesting, but our student wasn't able to really get the UI/UX into a state where we are happy with it. Our UI/UX designer has worked out what the improved Fresh Releases UI should look like and now we are looking for a student to implement these UI improvements. We anticipate that this is a 100% javascript/react project with a focus on creating a slick and smooth UI for this rather interesting page.
