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Score-based Generative Models (Diffusion Models) for Speech Enhancement and Dereverberation
| Date | Stars |
|---|---|
| 2026-07-31 | 763 |
| 2026-08-01 | 764 |
| 2026-08-06 | 764 |
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# Speech Enhancement and Dereverberation with Diffusion-based Generative Models
<img src="https://raw.githubusercontent.com/sp-uhh/sgmse/main/diffusion_process.png" width="500" alt="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"*](https://www.isca-speech.org/archive/interspeech_2022/welker22_interspeech.html), ISCA Interspeech, Incheon, Korea, Sept. 2022. [[bibtex]](#citations--references)
- Julius Richter, Simon Welker, Jean-Marie Lemercier, Bunlong Lay, Timo Gerkmann, [*"Speech Enhancement and Dereverberation with Diffusion-Based Generative Models"*](https://ieeexplore.ieee.org/abstract/document/10149431), IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 2351-2364, 2023. [[bibtex]](#citations--references)
- Jean-Marie Lemercier, Julius Richter, Simon Welker, Timo Gerkmann, [*"Analysing diffusion-based generative approaches versus discriminative approaches for speech restoration"*](https://ieeexplore.ieee.org/abstract/document/10095258), ICASSP, Rhodes Island, Greece, 2023. [[bibtex]](#citations--references)
- 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"*](https://www.isca-archive.org/interspeech_2024/richter24_interspeech.html), ISCA Interspecch, Kos, Greece, Sept. 2024. [[bibtex]](#citations--references)
- Julius Richter, Danilo de Oliveira, Timo Gerkmann, [*"Investigating Training Objectives for Generative Speech Enhancement"*](https://ieeexplore.ieee.org/document/10887784), IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Hyderabad, India, April 2025. [[bibtex]](#citations--references)
Audio examples and supplementary materials are available on our [SGMSE project page](https://www.inf.uni-hamburg.de/en/inst/ab/sp/publications/sgmse), [EARS project page](https://sp-uhh.github.io/ears_dataset/), and [Investigating training objectives project page](https://sp-uhh.github.io/gen-se/).
An interactive demo of generative speech enhancement using a juypter notebook can be found [here](https://github.com/sp-uhh/gen-se-demo/).
## 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"*](https://ieeexplore.ieee.org/document/10180108), IEEE/ACM Transactions on Audio, Speech, Language Processing, vol. 31, pp. 2724 -2737, 2023. [[github]](https://github.com/sp-uhh/storm)
- Bunlong Lay, Simon Welker, Julius Richter, Timo Gerkmann, [*"Reducing the Prior Mismatch of Stochastic Differential Equations for Diffusion-based Speech Enhancement"*](https://www.isca-archive.org/interspeech_2023/lay23_interspeech.html), ISCA Interspeech, Dublin, Ireland, Aug. 2023. [[github]](https://github.com/sp-uhh/sgmse-bbed)
## 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](https://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 CSVExcerpt of 13,298 characters
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matched fp:aafd1238a9f999bc, llm:Repository description: 'Score-based Generative Models (Diffusion Models) for Speech Enhancement and Dereverberation' (language: Python).
matched fp:aafd1238a9f999bc, llm:Repository description: 'Score-based Generative Models (Diffusion Models) for Speech Enhancement and Dereverberation' (language: Python).