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DiffTransfer

TIMBRE TRANSFER USING IMAGE-TO-IMAGE DENOISING DIFFUSION IMPLICIT MODELS

Accompanying code to the paper Timbre transfer using image-to-image denoising diffusion implicit models [1].

For any question, please write at luca.comanducci@polimi.it.

Dependencies

Tensorflow (>2.11), librosa, pretty_midi, os, numpy, essentia, frechet_audio_distance

Data generation

The model is trained using the StarNet dataset, freely available on Zenodo link

Network training

  • audio_utils.py --> contains shared audio utilities and functions
  • params.py --> Contains parameters shared along scripts
  • network_lib_attention.py --> Contains Denoising Diffusion Implicit Model Implementation
  • DiffTransfer.py --> Actually runs the training, takes the following arguments:
    • dataset_train_path: String, path to training data
    • desired_instrument: String, name of desired output instrument
    • conditioning_instrument: String, name of input instrument
    • GPU: number of GPU, in case you have multiple ones

Results computation

  • compute_eval_tracks_mixture.py
  • compute_eval_tracks_separate.py
  • compute_frechet.py
  • compute_jaccard.py
  • compute_listening_test_results.py
  • preprocess_tracks_listening_test.py

If using this code please cite the following paper

@inproceedings{comanducci2023timbre, author = {Luca Comanducci and Fabio Antonacci and Augusto Sarti}, title = {Timbre Transfer Using Image-to-Image Denoising Diffusion Implicit Models}, booktitle = {Proceedings of the 24th International Society for Music Information Retrieval Conference, {ISMIR} 2023, Milan, Italy, November 5-9, 2023}, pages = {257--263}, year = {2023}, }

References

[1] Comanducci, Luca, Fabio Antonacci, and Augusto Sarti. "Timbre transfer using image-to-image denoising diffusion models. ISMIR International Society for Music Information Retrieval Conference arXiv