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amphion
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Diff-SSL-G-Comp

Tasks:
Audio-to-Audio
Languages:
English
Size:
1K<n<10K
ArXiv:
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Diff-SSL-G-Comp: Towards a Large-Scale and Diverse Dataset for Virtual Analog Modeling

This is the official repository 👑 for the Diff-SSL-G-Comp dataset.

Diff-SSL-G-Comp Overview ⭐️

The Diff-SSL-G-Comp dataset is an extensive and diverse dataset with the following features:

  • we use real-world unmastered songs as input instead of noises and test signals;
  • 175 songs are selected with diverse genres, instruments, tempos, and keys;
  • comprehensive 220 parameter combinations are recorded, resulting in 2528 hours of data.

Diff-SSL-G-Comp Dataset Structure ⛪️

Diff-SSL-G-Comp is formatted using the following structure:

|-- cambridge_unmastered_songs
|   |-- 54_UnmasteredWAV.wav
|   |-- ...
|-- processed_normalized
|   |-- 54_UnmasteredWAV.wav
|   |-- ...
|-- ...
|-- processed_ground_truth
|   |-- threshold_-12_attack_10_release_0.1_ratio_2
|   |   |-- 54-exported.wav
|   |   |-- ...
|   |-- ...
|-- ...

The raw audios are in the folder "cambridge_unmastered_songs." The normalized input audios are in the folder "processed_normalized." The ground truth hardware output audios are in the folder "processed_ground_truth."

Note that we used annotated thresholds illustrated on the analog device instead of the digital workstation.

Reference 📖

If you use the Diff-SSL-G-Comp dataset, please cite the following papers:

@inproceedings{diffsslgcomp,
    author={Yicheng Gu and Runsong Zhang and Lauri Juvela and Zhizheng Wu},
    title={Diff-SSL-G-Comp: Towards a Large-Scale and Diverse Dataset for Virtual Analog Modeling},
    booktitle={arXiv:2504.04589},
    year={2025}
}
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