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---
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language:
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- en
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license: apache-2.0
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tags:
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- speech
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- self-supervised learning
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- model compression
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- neural architecture search
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- LightHuBERT
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datasets:
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- librispeech_asr
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- superb
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---
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# LightHuBERT
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[**LightHuBERT**](https://arxiv.org/abs/2203.15610): **Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT**
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Authors: Rui Wang, Qibing Bai, Junyi Ao, Long Zhou, Zhixiang Xiong, Zhihua Wei, Yu Zhang, Tom Ko and Haizhou Li
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| [**Github**](https://github.com/mechanicalsea/lighthubert) | [**Huggingface**](https://huggingface.co/mechanicalsea/lighthubert) |
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The authors' PyTorch implementation and pre-trained models of LightHuBERT.
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- March 2022: release preprint in [arXiv](https://arxiv.org/abs/2203.15610) and checkpoints in [huggingface](https://huggingface.co/mechanicalsea/lighthubert).
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## Pre-Trained Models
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| Model | Pre-Training Dataset | Download Link |
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|---|---|---|
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|LightHuBERT Base| [960 hrs LibriSpeech](http://www.openslr.org/12) | huggingface: [lighthubert/lighthubert_base.pt](https://huggingface.co/mechanicalsea/lighthubert/resolve/main/lighthubert_base.pt) |
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|LightHuBERT Small| [960 hrs LibriSpeech](http://www.openslr.org/12) | huggingface: [lighthubert/lighthubert_small.pt](https://huggingface.co/mechanicalsea/lighthubert/resolve/main/lighthubert_small.pt) |
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|LightHuBERT Stage 1| [960 hrs LibriSpeech](http://www.openslr.org/12) | huggingface: [lighthubert/lighthubert_stage1.pt](https://huggingface.co/mechanicalsea/lighthubert/resolve/main/lighthubert_stage1.pt) |
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## Load Pre-Trained Models for Inference
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```python
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import torch
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from lighthubert import LightHuBERT, LightHuBERTConfig
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wav_input_16khz = torch.randn(1,10000).cuda()
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# load the pre-trained checkpoints
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checkpoint = torch.load('/path/to/lighthubert.pt')
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cfg = LightHuBERTConfig(checkpoint['cfg']['model'])
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cfg.supernet_type = 'base'
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model = LightHuBERT(cfg)
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model = model.cuda()
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model = model.eval()
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print(model.load_state_dict(checkpoint['model'], strict=False))
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# (optional) set a subnet
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subnet = model.supernet.sample_subnet()
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model.set_sample_config(subnet)
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params = model.calc_sampled_param_num()
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print(f"subnet (Params {params / 1e6:.0f}M) | {subnet}")
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# extract the the representation of last layer
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rep = model.extract_features(wav_input_16khz)[0]
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# extract the the representation of each layer
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hs = model.extract_features(wav_input_16khz, ret_hs=True)[0]
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print(f"Representation at bottom hidden states: {torch.allclose(rep, hs[-1])}")
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```
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### Profiling LightHuBERT
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As mentioned in [Profiling Tool for SLT2022 SUPERB Challenge](https://github.com/B06901052/DeepSpeed/tree/superb-challenge), we profiling the `lighthubert` in s3prl.
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```sh
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cd DeepSpeed
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# lighthubert_small
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python testing/s3prl_profiling_test.py -u lighthubert_small --libri_root "libri_root"
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# lighthubert_base
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python testing/s3prl_profiling_test.py -u lighthubert_base --libri_root "libri_root"
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# lighthubert_stage1
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python testing/s3prl_profiling_test.py -u lighthubert_stage1 --libri_root "libri_root"
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```
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### Reference
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If you find our work is useful in your research, please cite the following paper:
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```bibtex
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@article{wang2022lighthubert,
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title={{LightHuBERT}: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit {BERT}},
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author={Rui Wang and Qibing Bai and Junyi Ao and Long Zhou and Zhixiang Xiong and Zhihua Wei and Yu Zhang and Tom Ko and Haizhou Li},
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journal={arXiv preprint arXiv:2203.15610},
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year={2022}
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}
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```
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### Contact Information
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For help or issues using LightHuBERT models, please submit a GitHub issue.
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For other communications related to LightHuBERT, please contact Rui Wang (`rwang@tongji.edu.cn`).
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