Extending musicBERT for octuple generation and style based remix
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 20/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python
- Domain
- ai, machine-learning
Research direction
Start by reviewing the repository's MusicBERT implementation and how TOP-MAGD data and class labels are handled; the issue does not name specific files or tests. Investigate whether genre/style conditioning and a BERT-to-BERT encoder-decoder fit the existing entry points. Done would require an agreed design and demonstrated octuple generation or style-based remixing, but the issue provides no acceptance criteria.
Written by the indexing model from the issue text.
Description
Hii!
Recently, I have been working creating user friendly remixing tool with my friend @tripathiarpan20 known as midiformers. I am wanting to further extend the tool by :
- Firstly, I would be integrating genre and style based remixing which allows a user to remix the midi in particular style like jazz, pop, rock etc. I was thinking of prepending a class token similar to Jukebox (they prepend artist and genre embedding) would it be enough to condition the octuples and would fine-tuning on TOP-MAGD with class labels work ? I would be glad to know your thoughts on this.
- Secondly I would like to train a BERT-to-BERT encoder decoder model to perform autoregressive task again I if the above task succeeds, I will include genre conditioning. But I am not sure if this is possible with musicBERT. Can you help me through ?
Thank you so much for your amazing work. It is really inspiring for us!
- Dominant language
- Python
- Stars
- 5k
- Forks
- 499
- PR merge metrics
- No merged PRs in 30d
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