Update README.md

README.md

@@ -35,8 +35,9 @@ 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)
 - 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://arxiv.org/abs/2406.06185), ISCA Interspecch, Kos, Greece, Sept. 2024. [[bibtex]](#citations--references)
+- Julius Richter, Danilo de Oliveira, Timo Gerkmann, [*"Investigating Training Objectives for Generative Speech Enhancement"*](https://arxiv.org/abs/2409.10753) (preprint), 2024. [[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) and [EARS project page](https://sp-uhh.github.io/ears_dataset/).
+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/).
 
 ## Key Files
 
@@ -69,7 +70,6 @@ Please also check out our follow-up work with code available:
     - 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](https://drive.google.com/drive/folders/1CSnkhUSoiv3RG0xg7WEcVapyLuwDaLbe?usp=sharing).
     - SGMSE+ trained on VoiceBank-DEMAND: `gdown 1_H3EXvhcYBhOZ9QNUcD5VZHc6ktrRbwQ`
     - SGMSE+ trained on WSJ0-CHiME3: `gdown 16K4DUdpmLhDNC7pJhBBc08pkSIn_yMPi`
@@ -79,6 +79,15 @@ Please also check out our follow-up work with code available:
 - 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](https://drive.google.com/drive/folders/1Tn6pVwjxUAy1DJ8167JCg3enuSi0hiw5?usp=sharing).
     - 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](https://www2.informatik.uni-hamburg.de/sp/audio/publications/icassp2025_gense/checkpoints/)
+    - 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`.
@@ -97,11 +106,13 @@ where `your_base_dir` should be a path to a folder containing subdirectories `tr
 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 preprint [2] uses `--backbone ncsnpp`.
+- 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
@@ -152,9 +163,19 @@ We kindly ask you to cite our papers in your publication when using any of our r
   year={2024}
 }
 ```
+```bib
+@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.
+>[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.