I’m pleased to announce that Kartik Ohri, AKA Lucifer, a very active contributor since his Code-in days in 2018, has become the latest staff member of the MetaBrainz Foundation!
Kartik has been instrumental in rewriting our Android app and more recently has been helping us with a number of tasks, including new features for ListenBrainz, AcousticBrainz as well as breathing some much needed life into the CritiqueBrainz project.
These three projects (CritiqueBrainz, ListenBrainz and AcousticBrainz) will be his main focus while working for MetaBrainz. Each of these projects has not had enough engineering time recently to sufficiently move new features forward. We hope that with Kartik’s efforts we can deliver more features faster.
Just in time for Christmas we are pleased to announce a new feature in our most recent release of ListenBrainz, the ability to create and share your own playlists! We created two playlists for each user who used ListenBrainz containing music that you listened to in 2020. Check out your lists at https://listenbrainz.org/my/recommendations. Read on for more details…
With our continuing work on using data in ListenBrainz to generate recommendations, we realised that we needed a place to store lists of music. That sounded like playlists to us, so we added them to ListenBrainz. As always, we did this work in the public ListenBrainz repository. You can now create your own playlists with the web interface or by using the API. Recordings in playlists map to MusicBrainz identifiers. If you’re trying to add something and can’t find it, make sure that it’s in MusicBrainz first.
Once you have a playlist, you can listen to it using our built-in BrainzPlayer, or export it to Spotify if you have an account there. If you have already linked your Spotify account to ListenBrainz you may have to re-authenticate and give us permission to create playlists on your behalf. Playlists can also be exported in the open JSPF format using the ListenBrainz API.
Over the last year we’ve started thinking about how to use data in MetaBrainz projects to generate recommendations of new music for people to listen to. For this reason, we started the Troi recommendation framework. This python package allows developers to build pipelines that take data from different sources and combine it in order to generate recommendations of music to listen to. We have already developed data sources using MusicBrainz, ListenBrainz, and AcousticBrainz. If you are a developer interested in working on recommendations in the context of ListenBrainz we encourage you to check it out.
Now that we can store playlists we needed some content to fill them with. Luckily we have some great projects worked on by students over the last few years as part of MetaBrainz’ participation in the Google Summer of Code project, including this year’s work on statistics and summary information by Ishaan. Using Troi and ListenBrainz statistics, we got to work. Every user who has been contributing data to ListenBrainz recently now has two brand new 2020 playlists based on the top recordings that you listened to in 2020 and the recordings that you first listened to in 2020. If you’re interested in the code behind these playlists, you can see the code for each (top tracks, first tracks) in the troi repository.
If you’re a long-time user of ListenBrainz you may be familiar with the problem of matching your listens to content in MusicBrainz to be able to do things with it. We’ve been working hard on a solution to this problem and have built a new tool using typesense to provide a quick and easy way to search for items in the MusicBrainz database. You are using this tool when you create a playlists using the web interface and search for a recording to add. This is still a tech preview, but in our experience it works really well. Thanks to the team at typesense for helping us with our questions over the last few weeks!
This work is still in its early days. We thought that this was such a great feature that we wanted to get it out in front of you now. We’re happy to take your feedback, or hear if you are having any problems. Open a ticket on our bug tracker, come and talk to us on IRC, or @ us. Did we give you a bad jam? Sorry about that! We’d love to have a conversation about what went well and what didn’t in order to improve our systems. In 2021 we will start generating weekly and daily playlists for users based on your recent listens using our collaborative filtering recommendations system.
Hey! My name is Shivam Kapila (shivam-kapila on IRC) and I am a final year undergrad at National Institute of Technology Hamirpur. I have been working on the ListenBrainz project this Summer as a participant of the Google Summer of Code program. The past four months were full of fun, hacking and loads of music!!
As a GSoC applicant, I proposed to add various Listen Management features like love/hate (aka feedback) and deleting individual listens in ListenBrainz. I also proposed a new design for the Listens page. This involved a lot of designing and research, going through UI/UX design guidelines and tuning colors, shades and shadows till we arrived at a presentable and subtle design.
And finally I onboarded the GSoC train 🙂 .
Bonding with the community
I had been a part of the community since January so I was familiar with how things work in ListenBrainz. So I decided to contribute to the TimescaleDB migration where we moved our primary listen store from InfluxDB to TimescaleDB, opening up a ton of features for us to work on. Here is the final migration PR containing the commits of my contribution.
As the official coding period began, I started working on my proposed tasks. The first question was: how to store the feedback? So I began implementing the database changes to store the recording feedback and applying the necessary changes in production. Following this I added a Python module to interact with the database and implemented a Pydantic model to validate the feedback records before they are stored in the database or served over the API. Then I added the necessary APIs to store and fetch the feedback for a given user or recording. This was followed by improving the efficiency of the DB module.
Now that the base was ready for us to work on, I started working on the React components so that the feedback and deletion feature could actually be presented on the website. Around the same time, the Timescale release day was also getting near, so I helped with a few tests and finished up the work for deleting listens. The front end components also started looking good and we were ready to associate the back end with them.
Rectifying & Reactifying
It’s high time and the final phase started. Now that we were ready with a few components we needed some tweaks in some production components to make them subtle. Hence I shot an improvement PR to tweak some shadows, adjust some fonts, adjust heights of the components, sticking the footer to the bottom, and reactify the loading spinner. Then came the Listen Count Card denoting the number of listens for a user. Following this we moved to Card based design for displaying listens.
This was followed by the much awaited feedback controls and now we can love/hate the songs from our listen collection. Isn’t this amazing! There were some needed minor tweaks needed to handle the ‘playing now’ listens correctly. At the same time, following the MetaBrainz guidelines to write quality code, I worked on making the SQL queries more readable. Then came the much awaited Delete a Listen feature and now we can finally get rid of the embarrassing listens!!
Oh, now comes the time when we talk about the current scenario. The tasks currently on my radar are adding cover art support so that the page looks more alive and improving the Spotify imports to only import listens that were listened by the user after the latest Spotify listen we have for them.
After this I aim to work on the recommendation stuff that’s being actively pursued by the team. Also Mr_Monkey and me had been working on some design concepts for the All New ListenBrainz. I am pretty excited to work on it. Wanna take a sneak peek?
A new fam
The journey with MetaBrainz has been so amazing, that I am so tempted to stick here. I feel ecstatic to be a part of GSoC with the best org 🙂 . The best part is – it’s never all about code. There’s a lot to gain. Each day marked gaining maturity and thinking more and more like a real developer. I started feeling at ease with the communicate → code → integrate chain. It really feels fortunate to be a part of the MetaBrainz family where everyone is a ping away ❤ .
GSoC marks the kickstart of my journey with MetaBrainz and I will be here lurking on IRC, shooting PRs to make the projects more and more awesome.
Robert Kaye (ruaok) for being a mentor and a companion, guiding me through the dev life and real life.
Param Singh (iliekcomputers) for always keeping the spirits high.
Hey everyone! I am Ishaan Shah (ishaanshah), a sophomore at International Institute of Information Technology – Hyderabad, India. This summer, I worked on ListenBrainz as a participant in Google Summer of Code ’20. My project involved generating statistics and visualisations for users using Apache Spark. This blog is an overview about the work I did and my experience working with ListenBrainz.
I started contributing to ListenBrainz in January 2020. My first PR was for LB-179, a small Quality of Life improvement to the LastFM importer. My first major contribution was porting the LastFM importer to ReactJS. Over the next two months, I continued working on the frontend, where I mainly worked on improving the frontend infrastructure by adding support for automated testing, porting the codebase to TypeScript and standardising the frontend code using ESLint and Prettier.
After making a few patches, I understood how ListenBrainz worked and got comfortable with the codebase. I decided to make a proposal for adding statistics to ListenBrainz using Apache Spark. While writing the proposal, I referred to many other websites, blogs, as well as community discussions for different ideas about statistics which could be added. After some research, I narrowed down on the specific graphs and statistics that I wanted to calculate during GSoC.
Community Bonding Period
Since I had been working with the MetaBrainz community since January, I was familiar with how things worked in the community. So we decided to use the Community Bonding Period for fixing and updating the Top Artists charts for a user. The first task that I took up was to add an API endpoint for fetching the Top Artists data for a user programmatically. Until then, I had mostly spent my time working on the frontend, this task helped me in getting familiar with the backend architecture. Next, I worked on porting the Top Artist graph from d3 to nivo – a charting library built with ReactJS and d3. The Top Artists graph only supported All Time statistics before. I worked on adding support for more time ranges. This was the first time I worked with Apache Spark and the PR for this took quite some time, but it was essential that we got it right as most of the statistics we built further would use a similar workflow. After we were satisfied with the overall flow of the data from our Spark cluster to the web server, I started working on showing the stats for different time ranges on the website. Although this task seemed easy at first, it took much longer than expected. We encountered some bugs and received some user feedback when we deployed the graph to production. The rest of this period was spent on incorporating the user feedback and fixing the bugs.
First Coding Period
We now had a somewhat stable pipeline for calculating the stats and sending them to the server. I started working on the backend for Top Releases stats for a user. We ran into memory issues when calculating these stats on the cluster, so I spent some time finding the cause of the issue and realised that we were collecting the results all at once which was causing the driver to run out of memory. I fixed this by collecting the results for each user separately and tweaking some RabbitMQ parameters to make sure that messages aren’t dropped while sending them to the server (PR #897). After this, I added Top Recordings for a user. Now we had a brand new Charts page that displayed the user’s Top Artists/Releases/Recordings for different time ranges. Next I started working on temporal statistics for a user i.e, number of listens in a past time range. The query that I wrote for calculating this data turned out to be pretty inefficient for larger datasets. So I ended up writing two versions of the same query: one for large datasets and one for smaller ones. While working on displaying these stats on the frontend, I tried various representations of the data. I finally settled on displaying the data as bar graphs, as shown on this report view.
Second Coding Period
I added two more graphs in this period: Daily Activity and Artist Origins. The Daily Activity graph shows the number of listens a user has at a particular time of the day. I implemented the query for calculating this data in a slightly different way compared to the Listening Activity query. This change improved the query speed significantly. I had some trouble finding a correct way to represent this data. My mentor helped me in this by suggesting the usage of a Heatmap, and the results turned out to be pretty good.
Next, we worked on the Artist Origins graph, which provides an insight into the geographical diversity of a user’s musical taste. I had a lot of help from the ListenBrainz team for this graph and I couldn’t have done this graph without their help. This was by far the most interesting stat that I worked on during the project. Furthermore it laid a general framework to calculate statistics using the data from MusicBrainz. After deploying this map on production, we received feedback from the users that the map looked plain for most of them and there wasn’t much colour difference between different regions. This happened because people generally tend to listen more songs from their home country, so there is a huge difference between the country with maximum artists and average number artists from other countries. We fixed this issue by changing the colour scale from linear to logarithmic.
Final Coding Period
We now turned our attention towards calculating some stats for the whole website. We decided to make a graph for the Top Artists over different time ranges. We thought that this would be relatively easy given that we had already done something similar for individual users before. However we hit an unexpected bump; the data we were calculating was not accurate, mainly because of various different sources of the artists and some minor changes in the artists’ name or metadata resulted in a different entry with a different listen count for the same artist. Moreover, we found a couple of users spamming our website for self promotion and we did not have a solid way to deal with this. Around this time, my college resumed and the amount of time I could dedicate to LB reduced severely. So we decided to use the remaining time to work on improving the frequency at which stats are updated. I have an open PR (#1052) for doing this at the time of me writing this blog and we should be able to implement this functionality in the near future.
The past 4 months have taught me a lot of things. I learnt new technical concepts everyday. I started writing code as a developer rather than a programmer. I understood the importance of proper unit and integration testing (even though it was my least favourite part while adding a new functionality). I also found it much easier to talk and interact with people both online and in real life. Frequent deployments of new features to production helped us a lot. We were able to catch bugs when we still had some context over the code written and also received feedback from the users about how we could improve the new features added. It also kept me motivated to keep working on new graphs and statistics and gave me a sense of satisfaction when I saw them on the production server. I also learnt that things don’t always go the way we expect them to. More often than not, you will run into some bumps while adding new features so it is better to keep some extra time to deal with these issues.
GSoC gave me a wonderful opportunity to work with some amazing people from all over the globe. I was not able to complete all the graphs that I had planned for this summer, but I do plan to continue working on ListenBrainz to add more statistics and new features.
Param Singh (iliekcomputers) for being an amazing mentor and helping me whenever I was stuck on an issue.
Robert Kaye (ruaok) for providing some really insightful feedback and the MusicBrainz data that was required for calculating the Artist Origin map.
Nicolas Pelletier (Mr_Monkey) for helping me with the frontend for the user Charts page and providing some amazing tips for ReactJS.
We are now running a copy of InfluxDB and a copy of TimescaleDB at the same time — in case we find problems with the new TimescaleDB database, we can revert to the InfluxDB database.
In the process of migrating we got rid of a pile of nasty duplicates that used to be created by importing from last.fm. We also got rid of some bad data (timestamp 0 listens) that were pretty much useless and were cluttering the data. If you find that you are missing some data besides some duplicates, please open a ticket.
The move to TimescaleDB allows us to create new features such a deleting a listen (which should be released later this summer) and various other features that because the underlying DB is much more flexible than InfluxDB. However, right this second there are no real new features for end users — more new features are coming soon, we promise!
Thank you to shivam-kapila, iliekcomputers and ishaanshah — thanks for helping with this rather large, long running project!
We’ve just finished pushing a new release to the production server for ListenBrainz. We’ve spent quite a long time working on this because we needed to completely revamp how we were generating user statistics and that process is now finally complete and live. The other good news on user statistics it that we now have a generalized framework for creating them and that should make it much easier to create more user statistics going forward. We’ve triggered the stats engine to produce updated top artist statistics for everyone and those should update for users automatically sometime later today.
This release also includes an improved importer from last.fm, moving it to react and making it more friendly on a mobile device. This particular feature hasn’t been super well tested, so if you find a problem, please submit a bug report.
Next, if your listening history is screwed up for some reason, you can now delete all listens and start over, perhaps with a clean import from last.fm.
Finally, this release includes a pile of security updates to make the overall system more secure, but users shouldn’t notice anything different.
Thank you to iliekcomputers, Mr_Monkey, ishaanshah[m], shivam-kapila, pristine__ and everyone else who was involved in creating this update!
Following up on our release from last week, we found a number of minor problems in production that were really hard to spot on our test setup. Sometimes you need to have real data flowing through your system before you can find the real problems.
The following pull requests were merged and released just now:
As promised, here is another blog post about the exciting new Follow page. The goal of this page is to finally make use of the data we collect in ListenBrainz and expose a new feature designed to let our users discover more music.
To use this new feature, you’ll need to link your Spotify account to ListenBrainz. Ideally you should give permission to record your listens and to play Spotify content. But if you’re not ready to dive into recording your listens, start with playback first. N.B. In order to really take advantage of this new feature, you’ll need a premium Spotify account.
Then head over to the recent listens page and hover over the tracks that are listed there. If the user listened on Spotify, then a play button will appear and you can listen to the track. Please note that playing from this page will interrupt whatever you’re already playing on Spotify. If you find that a user is listening to interesting music and you’d like to follow the user, head to the follow page and use the Follow Users section to add this user to your follow list.
When a user in your follow list finishes listening to a track, that track will appear as a line in the Playlist. In theory, you’ll be able to keep listening to what your followed users are playing: the player will attempt to play as many tracks as it can play and to keep the music going. The player also has a previous and next track button that allows you to easily skip tracks that you don’t like. Our team has found this feature exciting and to some extent even has started DJing for each other!
We’re pushing into new territory trying to offer music discovery features and trying out new features that we’ve not seen before. Expect bugs, missing features, and reactions of “why didn’t they do X?”. To be honest, we’re not entirely happy with it and we know that there are features missing. But we felt it important to push this out in order to start getting feedback from you — and we are also excited about the Spotify integration! That said, please continue reading and if you feel that we screwed something up, please open a ticket!
Also, keep in mind that we’re pushing against the tide of the music industry. Established players want to keep everything closed, controlled and in their silo (Apple Music, Tidal, etc). Spotify is slightly more open and allows us to record user’s histories and music playback from web pages, so we focused on working on Spotify first.
This has the unfortunate side-effect of making these new features useful only if you have a premium Spotify account, and following users who are not on Spotify is useless: we don’t know how to play this content. This blows — we know it and we hate it ourselves. But we needed to start with something to show what we’re trying to do and to generate some interest. If people are interested, we can start working in supporting more services and making more of the music in our pages playable.
Finally, the recording user’s listens API endpoint at Spotify has an annoying tendency to fall behind sometimes, which means that the flow of listens from Spotify slows or stops altogether, which is… less than ideal. We’re prodding Spotify to keep the bits flowing if at all possible, but know that all of this is a work in progress.
In fact, the release has already generated a flurry of fixes that we’ll push live before too long. A lot of these sorts of fixes are for problems that you can only see when real-live data flows through the data pipelines: these are tricky features to debug!
Please play with the follow feature and tell us what you think! If you know other services that we can use to play music from the data we have available, please comment! If you find bugs or have suggestions for how we make these features better, please open a ticket!
Have fun and discover some new music,
The ListenBrainz Team
For the past few months we’ve been working on enabling ListenBrainz to record your Spotify listening history automatically and we’ve just now released this feature! If you would like ListenBrainz to record your Spotify listening history automatically (and make it public!), go here to link your Spotify account to ListenBrainz. We’ll take care of the rest!
We would like to encourage as many users as possible to record their listening histories in ListenBrainz. With the data we collect and safeguard for you, we will soon start building more music discovery features. Please help our mission and go connect your account now!
This release also adds two new pages: Recent listens and the “follow” page. The recent listens page shows the most recent listens that we’ve saved in ListenBrainz for any user. This is a convenient way for you to discover other users who are currently listening to music.
The follow page is the new feature that we’re really excited about — it allows you to listen to the music that other people are currently listening to — pick a number of users to follow and their recent listens will appear on the page. The new embedded Spotify player can start playing the music as the listens roll in. This allows you to follow your friends and learn about music that they love! We’re going to write another blog post that talks more about the follow page and how we plan to improve that going forward — stay tuned for that.
This release also re-organizes the menu layout a little, moving the most useful features so that they’re easily accessible. Behind the scenes we’ve upgraded to using Python 3.7, starting using some portions of React for our user interface and also found ourselves amazed that this release included 646 commits! We hope to go to a more regular schedule of releases from here on out — this was a big push for us with a lot of infrastructure improvements that were needed.
This release would not have been possible if Monkey (from BookBrainz) didn’t come and help us write the UI for the follow feature. Monkey, iliekcomputers and myself worked relentlessly for weeks trying to push out some exciting features that show off the first steps for what we have planned for ListenBrainz. We’re quite excited for this release and we hope that you’ll enjoy the follow page and discover new music!