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FaderNetworks (Personal Fork)

A modified implementation of Fader Networks optimized for manipulating timbral adjectives in audio files using Mel Scale Spectrograms.

Key Modifications

  • Audio Domain: Adapted the original architecture to process audio features instead of image data.
  • Target Qualities: Trained using the NSynth Dataset to control specific "Note Qualities": Bright, Dark, Distortion, and Percussive.
  • Modern Compatibility: Updated to support PyTorch v2.0.1+.
  • Execution: Optimized for CPU inference (CUDA support is currently disabled/not ported).

Attribution

This is a personal fork for experimentation. All rights and original architecture credits belong to the original authors; PyTorch implementation of Fader Networks (NIPS 2017).

Fader Networks: Manipulating Images by Sliding Attributes - G. Lample, N. Zeghidour, N. Usunier, A. Bordes, L. Denoyer, M'A. Ranzato

@inproceedings{lample2017fader,
  title={Fader Networks: Manipulating Images by Sliding Attributes},
  author={Lample, Guillaume and Zeghidour, Neil and Usunier, Nicolas and Bordes, Antoine and DENOYER, Ludovic and others},
  booktitle={Advances in Neural Information Processing Systems},
  pages={5963--5972},
  year={2017}
}