Diffusion Posterior Sampling for Informed Single-channel Dereverberation

This code repository contains the official PyTorch implementation for the paper

• Diffusion Posterior Sampling for Informed Single-Channel Dereverberation, 2023

Audio examples and supplementary materials are available on our project page.

Installation

• Create a new virtual environment with Python 3.8 (we have not tested other Python versions, but they may work).
• Install the package dependencies via pip install -r requirements.txt.
• Your logs will be stored as local TensorBoard logs. Run tensorboard --logdir logs/ to see them.

Pretrained checkpoints

• We provide pretrained checkpoints for the unconditional score model here Usage:
• For resuming training, you can use the --resume_from_checkpoint option of train.py.
• For evaluating these checkpoints, use the --ckpt option of enhancement.py (see section Evaluation below).

Training

Training is done by executing train.py. A minimal running example with default settings (as in our paper) can be run with

python train.py --format <your_format>--base_dir <your_base_dir> --gpus 0,

where

• your_base_dir should be a path to a folder containing subdirectories train/ and valid/ (optionally test/ as well). The subdirectory structure depends on your_format:
  • your_format=wsj0: Each subdirectory must itself have two subdirectories clean/ and noisy/, with the same filenames present in both.
  • Add formats on your own, correpsonding to your data structure

To see all available training options, run python train.py --help. These include options for the backbone DNN, the SDE parameters, the PytorchLightning Trainer usual parameters such as max_epochs, limit_train_batches and so on.

Evaluation

To run the model on a test set, run

and replace test_dir rir_dir and ckpt_score with the appropriate paths. Check the paper and different options in enhancement.py for different posterior/predictor/corrector/diffusion parameters.

Data Creation

• In preprocessing/, you will find the data generation script used to create all the datasets used in the paper. Minimal example is:

    cd preprocessing;
    python3 create_data.py

Please check the script for other options

Citations / References

We kindly ask you to cite our papers in your publication when using any of our research or code:

@article{lemercier2023derevdps,
  title={Diffusion Posterior Sampling for Informed Single-Channel Dereverberation},
  author={Lemercier, Jean-Marie and Welker, Simon and Gerkmann, Timo},
  journal={IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
  year={2023}
}