Hi! I am Jason Dao, aka jasondk on IRC. I’m a third year undergrad at University of California, Davis. This past summer, I’ve been working with the MetaBrainz team to add some neat features to the project ListenBrainz.Continue reading “GSoC 2021: Pin Recordings and CritiqueBrainz Integration in ListenBrainz”
I am Akshat Tiwari (akshaaatt on IRC), an undergraduate student from Delhi Technological University, India.
It has been an exhilarating experience for me, right from submitting a proposal for GSoC to becoming a part of a fantastic community.
The Google Summer of Code 2021 Edition finally comes to an end after the 3-Month long journey. I will be detailing the journey of working towards my summer of code project today. This blog is a summary of all the work done.Continue reading “GSoC’21: MusicBrainz Android App: Dawn of Showdown”
Hi everyone, I am Akash Gupta, currently pursuing my undergraduate from Kalinga Institute of Industrial Technology. This summer, I participated in Google Summer of Code and developed a new feature — Series Entity— for the project BookBrainz.
I was mentored by Nicolas Pelletier (monkey on IRC) during this period. This post summarizes my contributions to the project and the experiences that I had throughout the summer.Continue reading “GSoC 2021: Series Entity for BookBrainz”
Hi Everyone! I am Ritiek Malhotra (ritiek on IRC) and recently completed my undergraduate degree in Computer Science and Engineering. I participated in Google Summer of Code ’21 and worked on
musicbrainz_rs – a library wrapper on the MusicBrainz Web API written for the Rust programming language.
Hello everyone, I’m Yang Yang (aka yyoung), an undergraduate student from Shanghai Jiao Tong University, China. I am honored to be accepted as a student of Google Summer of Code 2021 in MetaBrainz Foundation to work on the improvements of external links editor. I had a good time with the MusicBrainz dev team this summer, and it was a valuable experience for me. This is a final report and overview of my work.Continue reading “GSoC 2021: Push the URL relationship editor to the next level”
The MusicBrainz Mobile App developers have been working at full capacity, improving the user experience, incorporating more features and functionalities, while making sure the core purpose of the app remains as promised.
Since its inception in 2010, the MusicBrainz Official App has come a long way. The App currently is highly maintained and has been actively open for contributions. A systematic approach is being followed and updates are being made on a regular basis.
The most important revamp which has been worked on for the past few months is the Tagger feature available in the MusicBrainz Android App.
Functionalities like fetching the local album arts, searching through all your local music files at one go, retrieving the cover art from the server, and heading to the recording directly are some of the key highlights of the upcoming Tagger.
Picard has finally made an official entry to the MusicBrainz App where users can now send their releases to the original Picard desktop app with the click of a button. This has been worked on in collaboration with the Picard team and proper documentation on its usage will be shared soon.
The completely new addition of Listen and Critique showcases the functionalities of ListenBrainz and CritiqueBrainz websites natively from the app. Currently, these will be available as advanced features on the app.
A well-prepared Onboarding and About section will take you through every important detail on the app and make sure you are aware of all the functionalities in the best and optimized way possible.
Proper documentation of every feature is being prepared. The App is finally out in Production, do head to the stores and give it a try!
We are really excited to make the MusicBrainz App as user-friendly as possible for you, while we take care of all the wonder behind it!
Play Store: MusicBrainz – Apps on Google Play
I am Rohit Dandamudi, more commonly known as diru1100 in IRC and all other sites. I am currently doing my final year in Computer Science and Engineering at Chaitanya Bharathi Institute of Technology, Hyderabad. This summer, I had the wonderful opportunity to work with MetaBrainz Foundation and it’s my first time participating in GSoC. I worked on the SpamBrainz project under the guidance of yvanzo to make a step forward on eliminating spam in MusicBrainz.
How it started
I started looking for some cool projects to apply for GSoC, eventually, after going through some which were involved in the web development side, I finally got to know about the MetaBrainz Foundation, and it was already pretty late (around 2½ weeks before the proposal deadline), most of my fellow GSoCers were already in good rapport with the community by then. After looking through the project ideas, I wanted to do my project on CritiqueBrainz, but later I found out that it’s not considered for this year. In the end, I liked the concept of SpamBrainz and how it involves a good combination (Deep Learning and Web Development) of technologies. After browsing through the project I understood what I could and tried to make some changes to the codebase and was successfully able to run the model and add some documentation. Finally, I submitted the proposal, which got accepted.
My proposal was focused on extending the work done by Leo as part of GSoC 2018. It mainly involved the following:
- Do the research and implement online learning to:
- Update the model dynamically as new variations of editor spam accounts appear.
- Make the model self-sufficient without depending on a particular file or a batch of data.
- Explore different types of learnings that are applicable to enhance LodBrok and for better performance in production.
- Complete SpamBrainz API to:
- Use and update the model with API calls.
- Connect LodBrok with MusicBrainz Server.
- Do detailed documentation to make the project more public and involve more contributors
LodBrok model improvements
- Challenges faced initially were:
- There was less documentation.
- No access to real data.
- This made it a little difficult to understand the model, how it works, and what parameters are present, what is considered as spam or not, etc.
- To overcome this obstacle in the future, I have written a dummy data generation script.
- Have predicted using the model trained with generated data and got 100% accuracy against test data.
- Retrained the model to simulate online learning after doing a lot of research and considering the use case of LodBrok in MusicBrainz.
- Retrained the model with the simulation of taking spam as a non_spam account and it was able to predict the new learnings while still being able to remember the original non_spam accounts.
- Added detailed documentation about the model covering its usage, internal working, how to replicate locally with the help of images, helper functions.
- Finally, with all these included, the spambrainz_ml repo has released it’s first release v-0.1 with necessary binaries.
- Here is a navigation diagram explaining which notebooks and datasets are connected and the relationship between them.
Research for model live update
- To implement the online learning part I had to explore and test different methods with the generated dataset and LodBrok model. For this, I had to explore various resources such as Keras’ community forums, research papers, StackOverflow, courses, and blogs.
- A few of the interesting findings I have tested out were:
- Retraining the model
- This seemed to be the most obvious and easy fix to upgrade the model.
- This StackOverflow answer explains how retraining is done
- But some things to consider here are: a separate db has to be maintained to store the dataset and should be constantly updated by SpamNinja.
- This is not feasible overtime
- A lot of work is done to just transfer the data.
- Transfer Learning (TL)
- Official Keras blog explanation about the feature extraction and fine-tuning methods of TL.
- Transfer Learning mainly involves deriving a new model from a pre-existing successful model (LodBrok) known as feature extraction to tackle similar cases.
- I was inspired by the fine-tuning feature of Transfer learning which has a similar learning method as the one I implemented.
- Online Transfer Learning (OTL)
- This, as the name suggests is a combination of online learning and Transfer learning, which helps us to define models that can learn to classify similar spam accounts in MetabBrainz.
- This research explains about OTL and it’s use cases in the production environment.
- The concept of model drift:
- This article explains how the model degrades over time, the reasons for this, and how to handle it without depending on the production environment.
- This is useful to know as it is needed when the model is finally in production handling real data.
- Retraining the model
- In the end, I decided to go with refitting the model with a slow static learning rate, this seemed to be the best solution for the following reasons:
- No need to store editor details for false-positive and false-negative cases respecting MetaBrainz’s data privacy rules.
- The model won’t go through catastrophic forgetting (forget old learnings of what is spam or not) and will be able to learn new patterns in spam accounts over time.
- The structure of the data isn’t changing over time (editor account fields remain the same).
- Resources which helped me make this decision:
- Keras community help discussion about the same exact problem (online learning in Keras for an LSTM model [LodBrok])
- StackOverflow answer explaining catastrophic forgetting and role of fit function
- Machine Learning mastery blog article explaining the importance of learning rate on a model
- Reading research papers similar to this one covering online deep learning and consulting professors.
- Incorporated the above research in SpamBrainz API, which consists of 2 endpoints, namely:
/predictto return classification results by LodBrok for the editor accounts
/trainto retrain the model with incorrect results sent to SpamNinja respectively
- After discussing with Leo, I decided to implement the API using Flask and Redis combination. Going with Redis over RabbitMQ for this API is feasible as the API is pretty lightweight and has at most 2 events.
- Documented the entire API, with internal working, steps to replicate, and images to understand the results obtained.
- Completed dockerization of SpamBrainz_API for easier integration and testing with MusicBrainz docker.
- This diagram explains the current workflow of the implemented API:
Challenges ahead and future of SpamBrainz
- The API has to be integrated with MusicBrainz and should undergo more testing with real live data, currently, my focus is on this part.
- To extend online learning to other use cases in MetaBrainz through Transfer Learning and Online Transfer Learning.
- Also looking forward to writing a research paper about the work done, and eventually publish it in IEEE transactions, as I plan on using SpamBrainz as my final year major project.
Special thanks to…
- My mentor, YvanZo for being incredibly patient with me, helping me create quality commits, and overall making me a better programmer. Have always learned something new in every interaction with him.
- LeoVerto, for helping me out whenever stuck and getting me up to date with the project.
- MetaBrainz Foundation, for creating an open, inclusive, and productive environment to build some amazing stuff.
Hi everyone, I am Prabal Singh currently studying in Indian Institute of Technology, Guwahati. This summer I participated in Google Summer of Code and developed a new feature – User Collections – for the project BookBrainz.
I was mentored by Nicolas Pelletier (Mr_Monkey on IRC) during this period. This post summarizes my contributions to the project.Continue reading “GSoC 2020: User Collection for BookBrainz”
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!!
Landing into the MetaBrainz Community!
My journey with MetaBrainz began in late January this year, when I introduced myself to the community. My first PR improving the developer documentation was by adding parts connected with setting up the Spark infrastructure on a local setup along with consolidating and improving bits of documentation. I delved into real code while implementing front end components for Deleting Listens. Over the next few months, I fixed various bugs like making the Importer Modal responsive, fixing the DB setup scripts, fixing pagination issues while browsing listens, handling stat calculation errors in the Spark Reader and flushing user stats when they delete their listens.
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.
I also contributed to easing the testing infrastructure for devs to test the patches on their local setups. Following this I upgraded the postgres-client to PG12 version when we migrated to Postgres 12. I also fixed a minor font bug on the profile page.
The GSoC journey begins
Laying the base
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.
I also worked on dumping the recording feedback in the ListenBrainz public dumps. Since ListenBrainz had migrated the stats calculation infrastructure from Google BigQuery to Apache Spark I also removed the BigQuery references from the ListenBrainz website. Now that the timescale migration work became stable, I began working on Delete a Listen feature.
Pulling out the front end brushes
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!!
I also addressed some high priority tasks like giving the users an option to download their submitted feedback as JSON. We noticed some UI glitches and then came three back to back PRs to update feedback control shades, improving the listen time text and smoothing up the deletion animation. This is how the listen list looks like:
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.
- Nicolas Pelletier (Mr_Monkey) for guarding me against Cascading Snot Swab issues.
- Alastair Porter (alastairp) for fishing out the best practices from his pool of intelligence.
- Vansika Pareek (pristine___) for some awesome playlists.
- Frederik “Freso” S. Olesen (Freso) & C. “CatCat” Holm (CatQuest) for the best end user perspectives and reviews.
- GSoC & MetaBrainz for such a wonderful experience.
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.