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  Selection of trained models for the RhythGen project: https://github.com/efraimdahl/RhythGen
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  # Models
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- • LAS*: In-attention conditioning with syncopation labels on the Lieder dataset.
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- • LMR2: Attention modulation with spectral weight profiles profiles and a learnable scale (initialized at 10) on the Lieder dataset.
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- • LB*: Baseline NotaGen (small) model without any conditioning, finetuned on the Lieder dataset.
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- • HAS2*: In-attention conditioning with syncopation labels on the RAG-RH dataset.
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- • RAS2*: In-attention conditioning with syncopation scores and voice masking on the RAG-
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  collection.
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- • RB*: Baseline NotaGen model without any conditioning, finetuned on the RAG-collection.
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  # Base Model
 
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  Selection of trained models for the RhythGen project: https://github.com/efraimdahl/RhythGen
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  # Models
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+ **LAS:** In-attention conditioning with syncopation labels on the Lieder dataset.
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+ **LMR2:** Attention modulation with spectral weight profiles profiles and a learnable scale (initialized at 10) on the Lieder dataset.
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+ **LB:** Baseline NotaGen (small) model without any conditioning, finetuned on the Lieder dataset.
13
 
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+ **HAS2:** In-attention conditioning with syncopation labels on the RAG-RH dataset.
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+ **RAS2:** In-attention conditioning with syncopation scores and voice masking on the RAG-
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  collection.
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+ **RB:** Baseline NotaGen model without any conditioning, finetuned on the RAG-collection.
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  # Base Model