Update README.md

README.md

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 ---
 license: apache-2.0
+pipeline_tag: audio-to-audio
 ---
+# StuPASE: Studio-Quality PASE
+
+StuPASE is a state-of-the-art generative speech enhancement model trained to remove noise and reverberation while preserving linguistic content and speaker identity, and achieving studio-level perceptual quality. It operates on 16 kHz mono audio.
+
+---
+
+## Model Details
+
+### Model Description
+
+
+<img src="framework_stupase.png" alt="High-level system design" width="80%">
+
+StuPASE contains three main components:
+
+- **DeWavLM-R:** Performs low-hallucination phonetic enhancement, fine‑tuned from DeWavLM using dry targets for improved dereverberation.
+  - **Inputs**: Noisy speech
+  - **Outputs**: Enhanced phonetic representations
+
+- **CFM:** Performs phonetic-guided acoustic enhancement. 
+  - **Inputs**: Noisy Mel features + Enhanced phonetic representations
+  - **Outputs**: Enhanced Mel features
+
+- **Mel Vocoder:** Reconstructs enhanced wavforms.
+  - **Inputs**: Enhanced Mel features
+  - **Outputs**: Enhanced speech
+
+**Developed by:** Copyright © 2026 by Cisco Systems, Inc. All rights reserved.  
+**Cisco product group**: Collaboration AI: Xiaobin Rong, Mansur Yesilbursa, Kamil Wojcicki  
+**Model type:** Generative Speech Enhancement  
+**License:** Apache 2.0  
+**Finetuned from:** [WavLM-Large](https://github.com/microsoft/unilm/tree/master/wavlm), [DeWavLM](https://github.com/cisco-open/pase)
+
+
+---
+
+### Model Sources
+
+- **Repository:** https://github.com/cisco-open/pase
+- **Paper:** https://arxiv.org/abs/2603.09234
+- **Demo:** https://xiaobin-rong.github.io/stupase_demo/
+---
+## Uses
+### Direct Use
+- Enhance noisy or reverberant speech recordings  
+- Improve perceptual quality and intelligibility  
+- Preserve speaker identity and linguistic content  
+- Supports **16 kHz mono audio**
+### Out-of-Scope Use
+- Medical, legal, or safety‑critical decisions  
+- Voice conversion or identity manipulation  
+- Non‑speech audio enhancement
+---
+## How to Get Started
+Refer to the repository for quick-start code and examples:  
+https://github.com/cisco-open/pase
+
+---
+## Training Details
+### Training Data
+We release a StuPASE checkpoint that has been trained on an updated list of datasets. For this release, training used:
+
+- Clean speech:
+  - DNS5 Challenge clean-speech resources derived from the LibriVox public-domain subset
+  - [LibriSpeech](https://www.openslr.org/12/)
+  - [LibriTTS](https://www.openslr.org/60/)
+  - [VCTK](https://datashare.ed.ac.uk/handle/10283/3443)
+- Noise:
+  - DNS5 Challenge noise resources
+- Room impulse responses:
+  - [OpenSLR26](https://www.openslr.org/26/)
+  - [OpenSLR28](https://www.openslr.org/28/)
+
+These source datasets were used to prepare training mixtures and train the released model. The model card and repository do not redistribute the underlying dataset contents; please refer to the original dataset pages and licenses below.
+
+### Dataset Attribution
+- DNS5 Challenge clean speech (LibriVox subset): clean-speech material prepared from [LibriVox](https://librivox.org/) through the [DNS Challenge](https://github.com/microsoft/DNS-Challenge). The LibriVox recordings used for this portion are [public domain](https://librivox.org/pages/public-domain/) and were used as clean-speech training data for the released checkpoint.
+- LibriSpeech: [LibriSpeech](https://www.openslr.org/12/) by Vassil Panayotov et al., licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). It was used as clean-speech training data for the released checkpoint.
+- LibriTTS: [LibriTTS](https://www.openslr.org/60/) by Heiga Zen et al., licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). It was used as clean-speech training data for the released checkpoint.
+- VCTK Corpus: the [VCTK dataset](https://datashare.ed.ac.uk/handle/10283/3443) from the Centre for Speech Technology Research, University of Edinburgh, licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). It was used as clean-speech training data for the released checkpoint.
+- DNS5 Challenge noise resources: noise data prepared through the [DNS Challenge](https://github.com/microsoft/DNS-Challenge) and used to synthesize noisy training mixtures for the released checkpoint. For this release, the DNS5 noise resources draw on [AudioSet](https://research.google.com/audioset/index.html) material licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/), selected [Freesound](https://freesound.org/) files licensed under [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/), and [DEMAND](https://zenodo.org/record/1227121#.XRKKxYhKiUk) environmental recordings licensed under [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/deed.en_CA).
+- OpenSLR26 and OpenSLR28: [OpenSLR26](https://www.openslr.org/26/) and [OpenSLR28](https://www.openslr.org/28/) room impulse response resources, both licensed under Apache 2.0, were used to add reverberation during training.
+
+All audio was resampled to 16 kHz.
+
+### Training Procedure
+#### Preprocessing
+- Mixtures generated dynamically  
+- SNR sampled from –5 to 15 dB  
+- Reverberation applied with 50% probability
+#### Training Hyperparameters
+- **DeWavLM-R:** 50k steps, LR 2e-5, batch size 20  
+- **CFM:** 100k steps, LR 1e‑4, batch size 60
+- **Mel Vocoder:** 200k steps, LR 2e-4, batch size 60  
+- Optimizer: AdamW with warmup + cosine decay  
+- Hardware: 2 × NVIDIA RTX 4090 GPUs
+#### Speeds, Sizes, Times
+- Total parameters: ~561M  
+- Inference compute: ~104 GMAC/s
+---
+## Evaluation
+### Testing Data
+- Simulated [LibriSpeech](https://www.openslr.org/12/) test set (using test split) 
+- [DNS1 test set](https://github.com/microsoft/DNS-Challenge/tree/interspeech2020/master/datasets/test_set/synthetic) with/without reverberation
+### Metrics
+- DNSMOS, UTMOS  
+- LPS, SpeechBERTScore (SBS)  
+- Speaker Similarity, using [WavLM-Large based TCAPA-TDNN (finetuned)](https://github.com/microsoft/UniSpeech/tree/main/downstreams/speaker_verification)
+- WER, using Whisper-Large-v3
+
+### Results
+
+The performance of the retrained version compared to the original one:
+| Model | DNSMOS | UTMOS | SBS | LPS | SpkSim | WER (%) |
+|:-----:|:------:|:-----:|:---:|:---:|:------:|:-------:|
+| DeWavLM-R (orig.) | 3.35 | 3.94 | 0.84 | 0.88 | 0.49 | 13.22 |
+| **DeWavLM-R (retrained)** | 3.38 | 3.62 | 0.85 | 0.89 | 0.41 | 12.27 |
+| StuPASE (orig.) | 3.37  | 4.08  | 0.85 |0.90 |0.68    | 11.57|
+| **StuPASE (retrained)** | 3.36 | 4.02 | 0.85 | 0.89 | 0.66 | 12.06 |
+
+It can be seen that the retrained version achieves performance very close to that of the original version on our simulated test set.
+
+Overall, StuPASE achieves:
+- Lowest WER among evaluated generative and discriminative baselines  
+- Highest speaker similarity (SpkSim)  
+- Highest perceptual quality with low hallucination rates  
+- Consistent performance across noisy and reverberant conditions
+
+---
+## Bias, Risks, and Limitations
+- Model trained primarily on English speech; performance may degrade for other languages.  
+- Very strong noise or mismatched reverberation conditions can introduce artifacts.  
+- Speaker characteristics are preserved but not guaranteed perfectly.
+
+---
+### Recommendations
+Evaluate outputs for your specific use case. Avoid deployments where misunderstanding enhanced speech could have safety or legal consequences.
+
+---
+## Citation
+If you use StuPASE in your research, please cite:
+```bibtex
+@misc{StuPASE,
+      title={{StuPASE: Towards Low-Hallucination Studio-Quality Generative Speech Enhancement}}, 
+      author={Xiaobin Rong and Jun Gao and Zheng Wang and Mansur Yesilbursa and Kamil Wojcicki and Jing Lu},
+      year={2026},
+      eprint={2603.09234},
+      archivePrefix={arXiv},
+      primaryClass={eess.AS},
+      url={https://arxiv.org/abs/2603.09234}, 
+}
+```
+Copyright © 2026 by Cisco Systems, Inc. All rights reserved.
+## Model Card Authorship & Contact
+- Mansur Yesilbursa: myesilbu@cisco.com
+