.gitignore

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 # JetBrains Rider
 .idea/
 *.sln.iml
-
-# Custom
-media/
-logs/

README.md

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-# MusicGAN
-**Scored in the Top 10 most innovative artificial intelligence projects in the international TFWorld Hackathon 2020.**
+# tfworldhackathon
+GitHub repo for my Tensorflow World hackathon submission
 
-MusicGAN creates 1 second of instrumental audio at 16kHz.
+[Video demo](https://www.youtube.com/watch?time_continue=2&v=DQDYsJXLqlo&feature=emb_logo)
 
-[Piano demo](https://www.youtube.com/watch?time_continue=2&v=DQDYsJXLqlo&feature=emb_logo)
-
-[Violin demo](https://www.youtube.com/watch?v=XcESDdsb3PU)
-
-To create your own MusicGAN, just clone this repo and run `scripts/WGAN-GP.py` after modifying `DATA_DIR` and `INSTRUMENT`. I used [this](http://deepyeti.ucsd.edu/cdonahue/wavegan/data/mancini_piano.tar.gz) data after converting the files to wave. The violin data I used was just scraped from Youtube.
-
-Training expects a GPU and will take several hours to achieve resonable results. Due to time limitations for the hackathon, I only trained for a few hours, but better results are possible if trained for a longer duration.
-
-Run `tensorboard --logdir logs/train` to view generator and critic loss with Tensorboard.
+To replicate my results, just clone this repo and run `scripts/WGAN-GP.py` after modifying `DATA_DIR`. I used [this](http://deepyeti.ucsd.edu/cdonahue/wavegan/data/mancini_piano.tar.gz) data after converting the files to wave.
 
 ## Inspiration
 Since the inception of generative adversarial networks, I have been fascinated by their capacity to perform tasks of unprecedented complexity. They are a prime example of how machines can learn in a similar manner to humans - akin to reinforcement learning. I am also a huge fan of music and love to play the piano. So I thought, why not conflate my love for machine learning and my passion for music!?