license: mit
Selection of trained models for the RhythGen project: https://github.com/efraimdahl/RhythGen
Models
• LAS: In-attention conditioning with syncopation labels on the Lieder dataset.
• LMR2: Attention modulation with spectral weight profiles profiles and a learnable scale (initialized at 10) on the Lieder dataset.
• LB: Baseline NotaGen (small) model without any conditioning, finetuned on the Lieder dataset.
• HAS2: In-attention conditioning with syncopation labels on the RAG-RH dataset.
• RAS2: In-attention conditioning with syncopation scores and voice masking on the RAG- collection.
• RB: Baseline NotaGen model without any conditioning, finetuned on the RAG-collection.
Base Model
All models are fintuned from NotaGen small.
Datasets
We fine-tuned NotaGen-small on a corpus of 1000-1500 pieces from either the Lieder Dataset which is public or the RAG Collection which is available on request.