Era  Koelpin

Era Koelpin


Let's Build a Music Recommender!

Let’s Build a Music Recommender!


  • 0:00 Overview
  • 2:21 AudiSet Dataset
  • 6:15 Embedding Generator
  • 9:46 Code: Generate Embedding from WAV
  • 16:30 Code: AudioSet Processing
  • 24:55 ANNOY Explained
  • 34:33 Recommendation Engine Code with ANNOY


[1] ANNOY Explained in more detail:
[2] AudioSet Download:
[3] MAX Audio Embedding Generator:
[4] Annoy slidedeck:

#machine-learning #data-science

Let's Build a Music Recommender!

How to Build A Flexible Movie Recommender Chatbot In Python

Follow our steps to discover what it takes to create a web-app that recommends movies based on open-ended user inputs!

Our working final product can be tested here.

Have you ever wondered what a chatbot is and how to build one?

In this three-part series, we will teach you everything you need to build and deploy your Chatbot. By “we” here, I mean my team members (Ahmed, Dennis, Pedro, and Steven), four data science students at the Minerva Schools at KGI. The series will cover the following topics:

We use a Jupyter Python 3 notebook as a collaborative coding environment for this project, and other bits of code for the web app development and deployment. All the code for this series is available in this GitHub repository.


Businesses integrate chatbots into many processes and applications. You might need to interact with one while buying an item from Sephora, booking a flight from British Airways, or even customizing your cup of coffee from Starbucks. Developers build chatbots to understand customers’ needs and assist them without needing human help, making chatbots very useful for many customer-facing businesses. So how does a chatbot work?

Generally, there are three types of chatbots:

  1. Rule-based Chatbots: these bots can answer customers’ requests based on pre-defined rules that we created. These bots are suitable to handle simple, repetitive, and predicted tasks but might fail to address complex ones.
  2. **Retrieval-based Chatbots: **these bots utilize advanced algorithms to select the most suitable response from a pool of diverse answers to accommodate customer’s needs. The retrieval approach is more intelligent than the rule-based fixed algorithm as it considers the message and the context of the conversation in answering customer’s requests.
  3. **Generative Chatbots: **these bots use Machine Learning algorithms to simulate how humans understand and respond to customers’ requests. Like humans, they can generate new and different responses based on the context and wording of customer’s questions. Although Generative bots are the most intelligent type of chatbots, it is challenging to build and train them.

The chatbot we settled on creating is retrieval-based. Our bot can take a diverse set of responses, which are only slightly structured and output tailored recommendations. We had two main challenges to making this work: first, to build a flexible recommendation system in Python capable of taking in written requests by users and outputting decent recommendations. Second, implementing that algorithm in a web-app that is user-friendly and easy to use.

#movie-recommendation #towards-data-science #recommendation-system #chatbots #how to build a flexible movie recommender chatbot in python #chatbot in python

How to Build A Flexible Movie Recommender Chatbot In Python

The Best Way to Build a Chatbot in 2021

A useful tool several businesses implement for answering questions that potential customers may have is a chatbot. Many programming languages give web designers several ways on how to make a chatbot for their websites. They are capable of answering basic questions for visitors and offer innovation for businesses.

With the help of programming languages, it is possible to create a chatbot from the ground up to satisfy someone’s needs.

Plan Out the Chatbot’s Purpose

Before building a chatbot, it is ideal for web designers to determine how it will function on a website. Several chatbot duties center around fulfilling customers’ needs and questions or compiling and optimizing data via transactions.

Some benefits of implementing chatbots include:

  • Generating leads for marketing products and services
  • Improve work capacity when employees cannot answer questions or during non-business hours
  • Reducing errors while providing accurate information to customers or visitors
  • Meeting customer demands through instant communication
  • Alerting customers about their online transactions

Some programmers may choose to design a chatbox to function through predefined answers based on the questions customers may input or function by adapting and learning via human input.

#chatbots #latest news #the best way to build a chatbot in 2021 #build #build a chatbot #best way to build a chatbot

The Best Way to Build a Chatbot in 2021

Create Music Streaming App Like Spotify

Interested in music application development like Spotify? We at AppClues Infotech help to build online music streaming and podcast apps like Spotify for iOS and Android. Hire our best designers & developers to build your own music streaming app like Spotify with customized features & functionality.

For more info:
Email: [email protected]
Call: +1-978-309-9910

#create music streaming app like spotify #create music streaming app like spotify #create music streaming app like spotify #hire music streaming app developers #cost to make a music streaming app #cost to make an app like spotify

Create Music Streaming App Like Spotify
Tia  Gottlieb

Tia Gottlieb


WSDM — KKBox’s Music Recommendation Challange


Music is the pleasure the human mind experiences from counting without being aware that it is counting_” — _Leibniz

Music helps anybody to connect with what you are doing. It elevates mood and rejuvenates the waves of thoughts. People are fond of listening to music every time whether it’s commute time, work time or focus time. Different people have different flavours of music. Music has served its users with various platforms like waves of Victrola, a culture of Cassette, Walkman era, i-pods, FM-Radios and now latest musical apps like Spotify, Amazon Prime Music, Deezer, SoundCloud, Gaana, etc.

Intenet made life easy in terms of selecting music of users’ choice, but still, algorithms are needed to recommend favourite music to users without selecting manually.

1. Business Problem and constrains:

Our business objective is to serve users with songs of their taste! This recommendation should not take hours, seconds would be sufficient to predict the chances of listening.

  • ML Problem Formulation

We have to build the model which will predict whether a user will re-listen to the song by evaluating given features of the user and songs. We can convert this problem as a classification problem and can apply various classification algorithms.

2. Data Discussion:

Dataset source:

The problem has 6 data files:

1.train.csv: This file includes

user_id (msno), song_id, source_system_tab (where the event was triggered),

source_type (an entry point a user first plays music), source_screen_name (name of the layout user sees) and target ( 1 means there is a recurring listening event(s) triggered within a month after the user’s a very first observable listening event, target=0 otherwise ).

2. test.csv: This file includes

user_id (msno), song_id, source_system_tab (where the event was triggered),

source_type (an entry point a user first plays music) and source_screen_name (name of the layout user sees).

3. songs.csv: This file has features like

song_id, song_length, genre_id, artist_name, composer, lyricist and language.

4. members.csv: This file has msno (user_id), city, bd (may contains outliers), gender, register_via (register method), register_init_time (date) and expirartion_date (date).

5. song_extra_info.csv: This file has features like song_id, song_name and

ISRC (International Standard Recording Code) used to identify songs.

#machine-learning #classification #recommendation-system #music-recommendations #data-science #deep learning

WSDM — KKBox’s Music Recommendation Challange
Queenie  Davis

Queenie Davis


MusicBERT: Microsoft’s Large Scale Pre-Trained Model For Symbolic Music Understanding

Microsoft recently developed a large scale pre-trained model for symbolic music understanding called MusicBERT. Symbolic music understanding refers to understanding music from the symbolic data (for example, MIDI format). It covers many music applications such as emotion classification, genre classification, and music pieces matching.

For developing MusicBERT, Microsoft has used OctupleMIDI method, bar-level masking strategy, and a large scale symbolic music corpus of more than 1 million music tracks.

Why OctupleMIDI?

OctupleMIDI is a novel music encoding method that encodes each note into a tuple with eight elements, representing the different aspects of the characteristics of a musical note, including instrument, tempo, bar, position, time signature, pitch, duration, and velocity.

Here are some of the advantages of OctupleMIDI:

  • Reduces the length of a music sequence (4x shorter than REMI), thus easing the modelling of music sequences by Transformer considering that music sequences themselves are very long
  • It is ‘note’ centric. Since each note contains the same eight tuple structure and covers adequate information to express various music genres, like time signature, long note duration, etc., OctupleMIDI is much easier.
  • It is universal compared to previous encoding methods since each note contains the 8-tuple structure to express different music genres.

MusicBERT architecture

The authors of the study established that it was challenging to apply NLP directly to symbolic music because it differs greatly from natural text data. There are following challenges:

  • Music songs are more structural and diverse, making it more difficult to encode as compared to natural language.
  • Due to complicated encoding of symbolic music, there are higher chances of information leakage in pre-training
  • The pre-training for music understanding is limited due to lack of large-scale symbolic music corpora

To remediate this, researchers Mingliang Zeng, Xu Tan, Rui Wang, Zeqian Ju, Tao Qin, and Tie-Yan Liu have developed MusicBERT, a large-scale pre-trained model with music encoding and masking strategy for music understanding. This model evaluates symbolic music understanding tasks, including melody completion, accompaniment suggestion, style classification and genre classification.

Besides OctupleMIDI, MusicBERT uses a bar-level masking strategy. The masking strategy in original BERT for NLP tasks randomly masks some tokens, causing information leakage in music pre-training. However, in the bar-level masking strategy used in MusicBERT, all the tokens of the same type (for example, time signature, instruments, pitch, etc.) are masked in a bar to avoid information leakage and for representational learning.

In addition to this, MusicBERT also uses a large-scale and diverse symbolic music dataset, called the million MIDI dataset (MMD). It contains more than 1 million music songs, with different genres, including Rock, Classical, Rap, Electronic, Jazz, etc. It is one of the most extensive datasets in current literature — ten times larger than the previous largest dataset LMD in terms of the number of songs (148,403 songs and 535 million notes). MMD has about 1,524,557 songs and two billion notes. This dataset benefits representation learning for music understanding significantly.

#opinions #bert music #build music software #genre classification #machine learning and music #microsoft latest

MusicBERT: Microsoft’s Large Scale Pre-Trained Model For Symbolic Music Understanding