metadata

language: en
tags:
  - speech-enhancement
  - dereverberation
  - diffusion-models
  - generative-models
  - pytorch
  - audio-processing
license: mit
datasets:
  - VoiceBank-DEMAND
  - WSJ0-CHiME3
  - WSJ0-REVERB
  - EARS-WHAM
  - EARS-Reverb
model_name: speech-enhancement-dereverberation-diffusion
model_type: diffusion-based-generative-model
library_name: pytorch
key_files:
  - model.py
  - train.py
  - inference.py
inference: false
pipeline_tag: audio-to-audio

Speech Enhancement and Dereverberation with Diffusion-based Generative Models

Diffusion process on a spectrogram: In the forward process noise is gradually added to the clean speech spectrogram x0, while the reverse process learns to generate clean speech in an iterative fashion starting from the corrupted signal xT.

This repository contains the official PyTorch implementations for the papers:

Simon Welker, Julius Richter, Timo Gerkmann, "Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain", ISCA Interspeech, Incheon, Korea, Sept. 2022. [bibtex]
Julius Richter, Simon Welker, Jean-Marie Lemercier, Bunlong Lay, Timo Gerkmann, "Speech Enhancement and Dereverberation with Diffusion-Based Generative Models", IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 2351-2364, 2023. [bibtex]
Julius Richter, Yi-Chiao Wu, Steven Krenn, Simon Welker, Bunlong Lay, Shinji Watanabe, Alexander Richard, Timo Gerkmann, "EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation", ISCA Interspecch, Kos, Greece, Sept. 2024. [bibtex]
Julius Richter, Danilo de Oliveira, Timo Gerkmann, "Investigating Training Objectives for Generative Speech Enhancement" (preprint), 2024. [bibtex]

Audio examples and supplementary materials are available on our SGMSE project page, EARS project page, and Investigating training objectives project page.

Key Files

The following files are essential for this model:

model.py: Defines the model architecture and training processes.
train.py: Script for training the model.
enhancement.py: Used for evaluating and enhancing audio files.
requirements.txt: Lists the necessary dependencies.
calc_metrics.py: Script for calculating evaluation metrics.

Follow-up work

Please also check out our follow-up work with code available:

Jean-Marie Lemercier, Julius Richter, Simon Welker, Timo Gerkmann, "StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation", IEEE/ACM Transactions on Audio, Speech, Language Processing, vol. 31, pp. 2724 -2737, 2023. [github]
Bunlong Lay, Simon Welker, Julius Richter, Timo Gerkmann, "Reducing the Prior Mismatch of Stochastic Differential Equations for Diffusion-based Speech Enhancement", ISCA Interspeech, Dublin, Ireland, Aug. 2023. [github]

Installation

Create a new virtual environment with Python 3.11 (we have not tested other Python versions, but they may work).
Install the package dependencies via pip install -r requirements.txt.
- Let pip resolve the dependencies for you. If you encounter any issues, please check requirements_version.txt for the exact versions we used.
If using W&B logging (default):
- Set up a wandb.ai account
- Log in via wandb login before running our code.
If not using W&B logging:
- Pass the option --nolog to train.py.
- Your logs will be stored as local CSVLogger logs in lightning_logs/.

Pretrained checkpoints

For the speech enhancement task, we offer pretrained checkpoints for models that have been trained on the VoiceBank-DEMAND and WSJ0-CHiME3 datasets, as described in our journal paper [2]. You can download them here.
- SGMSE+ trained on VoiceBank-DEMAND: gdown 1_H3EXvhcYBhOZ9QNUcD5VZHc6ktrRbwQ
- SGMSE+ trained on WSJ0-CHiME3: gdown 16K4DUdpmLhDNC7pJhBBc08pkSIn_yMPi
For the dereverberation task, we offer a checkpoint trained on our WSJ0-REVERB dataset. You can download it here.
- SGMSE+ trained on WSJ0-REVERB: gdown 1eiOy0VjHh9V9ZUFTxu1Pq2w19izl9ejD
- Note that this checkpoint works better with sampler settings --N 50 --snr 0.33.
For 48 kHz models [3], we offer pretrained checkpoints for speech enhancement, trained on the EARS-WHAM dataset, and for dereverberation, trained on the EARS-Reverb dataset. You can download them here.
- SGMSE+ trained on EARS-WHAM: gdown 1t_DLLk8iPH6nj8M5wGeOP3jFPaz3i7K5
- SGMSE+ trained on EARS-Reverb: gdown 1PunXuLbuyGkknQCn_y-RCV2dTZBhyE3V
For the investigating training objectives checkpoints [4], we offer the pretrained checkpoints here
- M1: wget https://www2.informatik.uni-hamburg.de/sp/audio/publications/icassp2025_gense/checkpoints/m1.ckpt
- M2: wget https://www2.informatik.uni-hamburg.de/sp/audio/publications/icassp2025_gense/checkpoints/m2.ckpt
- M3: wget https://www2.informatik.uni-hamburg.de/sp/audio/publications/icassp2025_gense/checkpoints/m3.ckpt
- M4: wget https://www2.informatik.uni-hamburg.de/sp/audio/publications/icassp2025_gense/checkpoints/m4.ckpt
- M5: wget https://www2.informatik.uni-hamburg.de/sp/audio/publications/icassp2025_gense/checkpoints/m5.ckpt
- M6: wget https://www2.informatik.uni-hamburg.de/sp/audio/publications/icassp2025_gense/checkpoints/m6.ckpt
- M7: wget https://www2.informatik.uni-hamburg.de/sp/audio/publications/icassp2025_gense/checkpoints/m7.ckpt
- M8: wget https://www2.informatik.uni-hamburg.de/sp/audio/publications/icassp2025_gense/checkpoints/m8.ckpt

Usage:

For resuming training, you can use the --ckpt 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 [2]) can be run with

python train.py --base_dir <your_base_dir>

where your_base_dir should be a path to a folder containing subdirectories train/ and valid/ (optionally test/ as well). Each subdirectory must itself have two subdirectories clean/ and noisy/, with the same filenames present in both. We currently only support training with .wav files.

To see all available training options, run python train.py --help. Note that the available options for the SDE and the backbone network change depending on which SDE and backbone you use. These can be set through the --sde and --backbone options.

Note:

Our journal [2] uses --backbone ncsnpp.
For the 48 kHz model [3], use --backbone ncsnpp_48k --n_fft 1534 --hop_length 384 --spec_factor 0.065 --spec_abs_exponent 0.667 --sigma-min 0.1 --sigma-max 1.0 --theta 2.0
Our Interspeech paper [1] uses --backbone dcunet. You need to pass --n_fft 512 to make it work.
- Also note that the default parameters for the spectrogram transformation in this repository are slightly different from the ones listed in the first (Interspeech) paper (--spec_factor 0.15 rather than --spec_factor 0.333), but we've found the value in this repository to generally perform better for both models [1] and [2].
For the investigating training objectives paper [4], we use --backbone ncsnpp_v2.
For the Schrödinger bridge model [4], we use e.g. --backbone ncsnpp_v2 --sde sbve --loss_type data_prediction --pesq_weight 5e-4.

Evaluation

To evaluate on a test set, run

python enhancement.py --test_dir <your_test_dir> --enhanced_dir <your_enhanced_dir> --ckpt <path_to_model_checkpoint>

to generate the enhanced .wav files, and subsequently run

python calc_metrics.py --test_dir <your_test_dir> --enhanced_dir <your_enhanced_dir>

to calculate and output the instrumental metrics.

Both scripts should receive the same --test_dir and --enhanced_dir parameters. The --cpkt parameter of enhancement.py should be the path to a trained model checkpoint, as stored by the logger in logs/.

Citations / References

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

@inproceedings{welker22speech,
  author={Simon Welker and Julius Richter and Timo Gerkmann},
  title={Speech Enhancement with Score-Based Generative Models in the Complex {STFT} Domain},
  year={2022},
  booktitle={Proc. Interspeech 2022},
  pages={2928--2932},
  doi={10.21437/Interspeech.2022-10653}
}

@article{richter2023speech,
  title={Speech Enhancement and Dereverberation with Diffusion-based Generative Models},
  author={Richter, Julius and Welker, Simon and Lemercier, Jean-Marie and Lay, Bunlong and Gerkmann, Timo},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
  volume={31},
  pages={2351-2364},
  year={2023},
  doi={10.1109/TASLP.2023.3285241}
}

@inproceedings{richter2024ears,
  title={{EARS}: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation},
  author={Richter, Julius and Wu, Yi-Chiao and Krenn, Steven and Welker, Simon and Lay, Bunlong and Watanabe, Shinjii and Richard, Alexander and Gerkmann, Timo},
  booktitle={ISCA Interspeech},
  pages={4873--4877},
  year={2024}
}

@article{richter2024investigating,
  title={Investigating Training Objectives for Generative Speech Enhancement},
  author={Richter, Julius and de Oliveira, Danilo and Gerkmann, Timo},
  journal={arXiv preprint arXiv:2409.10753},
  year={2024}
}

[1] Simon Welker, Julius Richter, Timo Gerkmann. "Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain", ISCA Interspeech, Incheon, Korea, Sep. 2022.

[2] Julius Richter, Simon Welker, Jean-Marie Lemercier, Bunlong Lay, Timo Gerkmann. "Speech Enhancement and Dereverberation with Diffusion-Based Generative Models", IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 2351-2364, 2023.

[3] Julius Richter, Yi-Chiao Wu, Steven Krenn, Simon Welker, Bunlong Lay, Shinji Watanabe, Alexander Richard, Timo Gerkmann. "EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation", ISCA Interspeech, Kos, Greece, 2024.

[4] Julius Richter, Danilo de Oliveira, Timo Gerkmann. "Investigating Training Objectives for Generative Speech Enhancement", arXiv preprint arXiv:2409.10753, 2024.