Duplicate from cisco-ai/pase

Co-authored-by: Mansur Yesilbursa <myesilbursa@users.noreply.huggingface.co>

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DeWavLM.tar

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README.md

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+---
+license: apache-2.0
+pipeline_tag: audio-to-audio
+---
+# PASE: Phonologically Anchored Speech Enhancer
+
+PASE is a state-of-the-art generative speech enhancement model trained to remove noise and reverberation while preserving linguistic content and speaker identity. It operates on 16 kHz mono audio.
+
+---
+
+## Model Details
+
+### Model Description
+
+
+<img src="framework_all.png" alt="High-level system design" width="80%">
+
+PASE contains two main components:
+
+- **Denoising WavLM (DeWavLM)**  
+  Fine‑tuned from WavLM‑Large using denoising representation distillation (DRD).  
+  Performs robust noise supression while effectively mitigating linguistic hallucinations by leveraging the phonological prior from self-supervised WavLM.
+
+- **Dual‑Stream Vocoder**  
+  Reconstructs audio using DeWavLM's dual-stream representations:
+  - **Phonetic representation**: high-level linguistic structure  
+  - **Acoustic representation**: speaker identity and prosody
+
+**Developed by:** Copyright © 2026 by Cisco Systems, Inc. All rights reserved.  
+**Cisco product group**: Collaboration AI: Xiaobin Rong, Qinwen Hu, 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)
+
+
+---
+
+### Model Sources
+
+- **Repository:** https://github.com/cisco-open/pase
+- **Paper:** https://arxiv.org/abs/2511.13300
+- **Demo:** https://xiaobin-rong.github.io/pase_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 PASE 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
+  - [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.
+- 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:** 100k steps, LR 1e‑4, batch size 4  
+- **Vocoder:** 200k steps, LR 2e‑4, batch size 12  
+- Optimizer: AdamW with warmup + cosine decay  
+- Hardware: 4 × NVIDIA RTX 4090 GPUs
+#### Speeds, Sizes, Times
+- Total parameters: ~382M  
+- Inference compute: ~21.4 GMAC/s
+---
+## Evaluation
+### Testing Data
+- Simulated [LibriTTS](https://www.openslr.org/60/) 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 (RawNet3)  
+- WER (OWSM v3.1)
+
+### Results
+
+The performance of the released version compared to the paper's results:
+| Model | DNSMOS | UTMOS | SBS | LPS | SpkSim | WER (%) |
+|:-----:|:------:|:-----:|:---:|:---:|:------:|:-------:|
+| Vocoder-L24 (paper) | 3.23 | 3.40 | 0.94 | 0.97 | 0.65 | 2.86 |
+| **Vocoder-L24 (released)** | 3.29 | 3.30 | 0.94 | 0.96 | 0.59 | 3.46 |
+| DeWavLM (paper) | 3.26 | 3.42 | 0.88 | 0.93 | 0.57 | 7.62 |
+| **DeWavLM (released)** | 3.31 | 3.39 | 0.88 | 0.93 | 0.52 | 7.25
+| PASE (paper) | 3.12   | 3.09  |0.90 |0.93 |0.80    | 7.49    |
+| **PASE (released)** | 3.08 | 3.21 | 0.91 | 0.94 | 0.80 | 6.76 |
+
+It can be seen that the released version achieves performance very close to that of the paper's results on our simulated test set.
+
+Overall, PASE achieves:
+- Lowest WER among evaluated generative and discriminative baselines  
+- Highest speaker similarity (SpkSim)  
+- Strong 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 PASE in your research, please cite:
+```bibtex
+@article{PASE, 
+    title={{PASE: Leveraging the Phonological Prior of WavLM for Low-Hallucination Generative Speech Enhancement}},
+    volume={40},
+    DOI={10.1609/aaai.v40i39.40562}, 
+    number={39}, 
+    journal={Proceedings of the AAAI Conference on Artificial Intelligence}, 
+    author={Rong, Xiaobin and Hu, Qinwen and Yesilbursa, Mansur and Wojcicki, Kamil and Lu, Jing}, 
+    year={2026},
+    month={Mar.}, 
+    pages={32826-32834}
+}
+```
+Copyright © 2026 by Cisco Systems, Inc. All rights reserved.
+## Model Card Authorship & Contact
+- Mansur Yesilbursa: myesilbu@cisco.com
+  

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