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README.md
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---
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license: mit
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library_name: pytorch
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tags:
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- visual-speech-recognition
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- lip-reading
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- discrete-diffusion
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- lrs3
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datasets:
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- lrs3
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---
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# DLLM-VSR: Diffusion Large Language Models for Visual Speech Recognition
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Paper checkpoints for **DLLM-VSR** — adapting the Dream-7B discrete-diffusion LLM to Visual Speech Recognition (VSR) on LRS3.
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- Paper: [arxiv.org/abs/XXXX.XXXXX](http://arxiv.org/abs/XXXX.XXXXX)
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- Code: [github.com/jh-y/dllm-vsr](https://github.com/jh-y/dllm-vsr)
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- Authors: Jeong Hun Yeo, Chae Won Kim, Hyeongseop Rha, Yong Man Ro
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## Contents
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| Path | Description | Size |
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|---|---|---|
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| `usr2/dream_stage2/` | USR 2.0 + Dream-7B stage 2 (LoRA + adapter) | 117 MB |
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| `usr2/len_pred/` | Length predictor for USR 2.0 features | 8.2 MB |
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| `avhubert/dream_stage2/` | AV-HuBERT + Dream-7B stage 2 | 102 MB |
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| `avhubert/len_pred/` | Length predictor for AV-HuBERT features | 8.0 MB |
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Each `dream_stage2/` holds `trainable_model.safetensors` (LoRA adapters + visual-feature projector). Each `len_pred/` holds `trainable_model.pt` (small Transformer over visual features).
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**Note**: Visual encoder weights (USR 2.0 Huge, AV-HuBERT Large) are **not** redistributed here. Download them from the original repos:
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- AV-HuBERT: https://github.com/facebookresearch/av_hubert
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- USR 2.0: https://github.com/ahaliassos/usr2
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## Results on LRS3 test (WER, %)
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All entries are trained on **LRS3 (433h)** only.
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| Decoding | USR 2.0 | AV-HuBERT |
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|---|:---:|:---:|
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| Direct | 20.5 | 23.1 |
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| Length-guided candidate decoding (paper main) | **19.5** | **21.9** |
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| Oracle-length (upper-bound reference) | 17.7 | 20.2 |
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## Usage
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```bash
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huggingface-cli download jh-y/dllm-vsr --local-dir ckpt
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```
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Then follow the [code repo's README](https://github.com/jh-y/dllm-vsr) for environment setup, preprocessing (auto-avsr pipeline), and inference scripts.
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## Citation
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```bibtex
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@article{yeo2026dllmvsr,
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title={Diffusion Large Language Models for Visual Speech Recognition},
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author={Yeo, Jeong Hun and Kim, Chae Won and Rha, Hyeongseop and Ro, Yong Man},
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journal={arXiv preprint arXiv:XXXX.XXXXX},
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year={2026}
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}
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```
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