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README.md

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+# Multi Label Music Genre Classifier
+
+An extension to my project of: [Single Label Music Genre Classifier](https://github.com/Hetan07/Single-Label-Music-Classifier)
+
+I was primarily interested on how prediction (or classification) 
+would work in case if a sample can be classified into multiple classes at the same time
+which is what most of the time is the case with music
+
+Majority of the work done went into actually making the proper dataset to work and train upon.
+What I wanted was a dataset similar to the one I worked upon during single label classification.
+GTZAN has no dataset for mulit-label purposes, and so I had to make one
+
+A dataset, [MuMU](https://www.upf.edu/web/mtg/mumu) has the mulit-label tags but works on different components or features.
+So I decided to create a GTZAN-like dataset but with multi-labels
+
+---
+
+### Dataset Creation
+
+The work done for creating the dataset were:
+
+- Downloading the appropriate songs taken randomly from the MuMu dataset in sampled manner from ~80 genres (tags)
+- Data Cleaning which included to clean and replace the download songs as many of them were things such as album intros, interludes or skits
+- There were also issues where the song required was not available on any platform and so had to appropriately replaced for another proper track or I had to manually search and download
+- Each file had to properly checked to prevent any distortion or disturbances
+- Applying feature extraction on each downloaded song using the *librosa* library
+- Reducing the labels from ~80 to around ~15
+
+There was also an issue: MuMu dataset has no Classical Genre and thus it had to be added manually.
+
+In the end I decided to have feature extraction work on 3 second samples and thus have around ~24000 samples.
+
+I have linked the actual dataset created from all the steps if anyone wishes to work upon it
+
+---
+
+For this task I decided to primarily work with neural networks and experimented with various architecture
+
+The models I trained are:
+
+- ANN
+- ANN with Batch Normalization
+- CNN
+- CRNN
+
+
+The genres classifed are the following:
+
+- Metal
+- Jazz
+- Blues
+- R&B
+- Classical
+- Reggae
+- Rap & Hip-Hop
+- Punk
+- Rock 
+- Country
+- Bebop
+- Pop
+- Soul
+- Dance & Electronic
+- Folk
+
+I have also deployed this on Hugging Face using streamlit. If one wishes, he can test and play around with different music tracks.
+
+---
+
+All of this took me around 3-4 days but in retrospect, I realize that some parts have been slightly rushed. 
+An in-depth analysis of data is further required along with more data. This task as a lot of potential for beginners and experts alike
+
 ---
 title: Multi Label Music Genre Classifier
 emoji: 🐠