nguyenvulebinh
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AV-HuBERT

Text2Text Generation

Transformers

Safetensors

speech_to_text

Inference Endpoints

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nguyenvulebinh commited on Aug 30, 2024

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 tags: []
 ---
+# Huggingface Implementation of AV-HuBERT on the MuAViC Dataset
+This repository contains a Huggingface implementation of the AV-HuBERT (Audio-Visual Hidden Unit BERT) model, specifically trained and tested on the MuAViC (Multilingual Audio-Visual Corpus) dataset. AV-HuBERT is a self-supervised model designed for audio-visual speech recognition, leveraging both audio and visual modalities to achieve robust performance, especially in noisy environments.
+Key features of this repository include:
+- Pre-trained Models: Access pre-trained AV-HuBERT models fine-tuned on the MuAViC dataset. The pre-trained model been exported from [MuAViC](https://github.com/facebookresearch/muavic) repository.
+- Inference scripts: Easily pipelines using Huggingface’s interface.
+- Data preprocessing scripts: Including normalize frame rate, extract lips and audio.
+### Inference code
+```sh
+git clone https://github.com/nguyenvulebinh/AV-HuBERT-S2S.git
+cd AV-HuBERT-S2S
+conda create -n avhuberts2s python=3.9
+conda activate avhuberts2s
+pip install -r requirements.txt
+python run_example.py
+```
+```python
+from src.model.avhubert2text import AV2TextForConditionalGeneration
+from src.dataset.load_data import load_feature
+from transformers import Speech2TextTokenizer
+import torch
+if __name__ == "__main__":
+    # Load pretrained english model
+    model = AV2TextForConditionalGeneration.from_pretrained('nguyenvulebinh/AV-HuBERT')
+    tokenizer = Speech2TextTokenizer.from_pretrained('nguyenvulebinh/AV-HuBERT')
+    # cuda
+    model = model.cuda().eval()
+    # Load normalized input data
+    sample = load_feature(
+        './example/lip_movement.mp4',
+        "./example/noisy_audio.wav"
+    )
+    # cuda
+    audio_feats = sample['audio_source'].cuda()
+    video_feats = sample['video_source'].cuda()
+    attention_mask = torch.BoolTensor(audio_feats.size(0), audio_feats.size(-1)).fill_(False).cuda()
+    # Generate output sequence using HF interface
+    output = model.generate(
+        audio_feats,
+        attention_mask=attention_mask,
+        video=video_feats,
+    )
+    # decode output sequence
+    print(tokenizer.batch_decode(output, skip_special_tokens=True))
+    # check output
+    assert output.detach().cpu().numpy().tolist() == [[  2,  16, 130, 516,   8, 339, 541, 808, 210, 195, 541,  79, 130, 317, 269,   4,   2]]
+    print("Example run successfully")
+```
+### Data preprocessing scripts
+```sh
+mkdir model-bin
+cd model-bin
+wget https://huggingface.co/nguyenvulebinh/AV-HuBERT/resolve/main/20words_mean_face.npy .
+wget https://huggingface.co/nguyenvulebinh/AV-HuBERT/resolve/main/shape_predictor_68_face_landmarks.dat .
+# raw video only support 4:3 ratio now
+cp raw_video.mp4 ./example/
+python src/dataset/video_to_audio_lips.py
+```
+### Pretrained model
+<table align="center">
+    <tr>
+        <th>Task</th>
+        <th>Languages</th>
+        <th>Huggingface</th>
+    </tr>
+    <tr>
+        <td rowspan="10">AVSR</td>
+        <th>ar</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>de</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>el</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>en</th>
+        <th><a href="nguyenvulebinh/AV-HuBERT">English Chekpoint</a></th>
+    </tr>
+    <tr>
+        <th>es</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>fr</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>it</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>pt</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>ru</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>ar,de,el,es,fr,it,pt,ru</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <td rowspan="13">AVST</td>
+        <th>en-el</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+     <tr>
+        <th>en-es</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>en-fr</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>en-it</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>en-pt</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>en-ru</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>el-en</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>es-en</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>fr-en</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>it-en</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>pt-en</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>ru-en</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+    <tr>
+        <th>{el,es,fr,it,pt,ru}-en</th>
+        <th><a href="todo">TODO</a></th>
+    </tr>
+</table>
+## Acknowledgments
+**AV-HuBERT**: A significant portion of the codebase in this repository has been adapted from the original AV-HuBERT implementation.
+**MuAViC Repository**: We also gratefully acknowledge the creators of the MuAViC dataset and repository for providing the pre-trained models used in this project
+## License
+CC-BY-NC 4.0
+## Citation
+```bibtex
+@article{anwar2023muavic,
+  title={MuAViC: A Multilingual Audio-Visual Corpus for Robust Speech Recognition and Robust Speech-to-Text Translation},
+  author={Anwar, Mohamed and Shi, Bowen and Goswami, Vedanuj and Hsu, Wei-Ning and Pino, Juan and Wang, Changhan},
+  journal={arXiv preprint arXiv:2303.00628},
+  year={2023}
+}
+```