Music_Gen_Streamlit
"Music Generation using Neural Networks" Streamlit App
TO DO:
-
Make a run_app.sh
-
Introduction [~5 min] (Sohaib)
- Team Member names/intro (WS 2019/2020, course name)
- Outline
- Introduction
- Data
- Phase 1
- Phase 2
- Neural DJ
- Literature review
- Refer to the literature slides
-
Data Analysis [~10 min] (Shivaani)
- General Info about data
- Add charts and sources about data (Lakh and RedditPop)
- DataProcessing Pipeline Graph (streamlit graph docs/ Abdallah's choice)
- Raw MIDI data
- Music21 intro and applications
- show raw midi (without explanation, button to visualize raw midi (Sohaib))
- Tokenized MIDI
- Show Tokenized with variable length
- Expand Tokens (musicautobot, go through section and see if its too explained)
- Play MIDI (Sohaib)
- Phase 1
- Play original
- Play extracted
- Phase 2
- Play sample (Lakh)
- Phase 1
- General Info about data
-
Add Model() (Abdallah)
- Phase 1
- Modeling (Hugging Face OpenAI GPT2)
- Data
- Only Piano (extracction process)
- Tokenization problem
- Phase 2
- Modeling (Architecture, Transformer XL)
- Data
- Used piano, but then ran into problems
- handle big files problem
- musicautobot API for data
- Phase 1
-
Add Prediction() (Code: Sohaib)
- Overview of pred process
- Play
- Play original
- Play predicted
- Visualize
- Note sheet original
- Note sheet predicted
- [?] Metrics
-
Technical Service/Deployment Pipeline (Code: Sohaib)
- make a requirements.txt
- Docker and heroku deployment
- Make container
- Check functionalities for each functional part of interface
Use Docker to run
make sure you have docker installed ./run_app.sh (this will take around 7-10 mins) then go to : localost:8501