AV-HuBERT (Audio-Visual Hidden Unit BERT)

Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction

Robust Self-Supervised Audio-Visual Speech Recognition



AV-HuBERT is a self-supervised representation learning framework for audio-visual speech. It achieves state-of-the-art results in lip reading, ASR and audio-visual speech recognition on the LRS3 audio-visual speech benchmark.

If you find AV-HuBERT useful in your research, please use the following BibTeX entry for citation.

    author  = {Bowen Shi and Wei-Ning Hsu and Kushal Lakhotia and Abdelrahman Mohamed},
    title = {Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction},
    year = {2022}

    author  = {Bowen Shi and Wei-Ning Hsu and Abdelrahman Mohamed},
    title = {Robust Self-Supervised Audio-Visual Speech Recognition},
    journal = {arXiv preprint arXiv:2201.01763}
    year = {2022}



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Pre-trained and fine-tuned models

Please find the checkpoints here


First, create a conda virtual environment and activate it:

conda create -n avhubert python=3.8 -y
conda activate avhubert

Then, clone this directory:

git clone https://github.com/facebookresearch/av_hubert.git
cd avhubert
git submodule init
git submodule update

Lastly, install Fairseq and the other packages:

pip install -r requirements.txt
cd fairseq
pip install --editable ./

Load a pretrained model

$ cd avhubert
$ python
>>> import fairseq
>>> import hubert_pretraining, hubert
>>> ckpt_path = "/path/to/the/checkpoint.pt"
>>> models, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path])
>>> model = models[0]

Train a new model

Data preparation

Follow the steps in preparation to pre-process:

  • LRS3 and VoxCeleb2 datasets

Follow the steps in clustering (pre-train only) to create:

  • {train,valid}.km frame-aligned pseudo label files.
    The label_rate is the same as the feature frame rate used for clustering,
    which is 100Hz for MFCC features and 25Hz for AV-HuBERT features by default.

Pre-train an AV-HuBERT model

Suppose {train,valid}.tsv are saved at /path/to/data, {train,valid}.km
are saved at /path/to/labels, the configuration file is saved at /path/to/conf/conf-name, and the label rate is 100Hz.

To train a model, run:

$ cd avhubert
$ fairseq-hydra-train --config-dir /path/to/conf/ --config-name conf-name \
  task.data=/path/to/data task.label_dir=/path/to/label \
  model.label_rate=100 hydra.run.dir=/path/to/experiment/pretrain/ \

Finetune an AV-HuBERT model with Seq2Seq

Suppose {train,valid}.tsv are saved at /path/to/data, {train,valid}.wrd
are saved at /path/to/labels, the configuration file is saved at /path/to/conf/conf-name.

To fine-tune a pre-trained HuBERT model at /path/to/checkpoint, run:

$ cd avhubert
$ fairseq-hydra-train --config-dir /path/to/conf/ --config-name conf-name \
  task.data=/path/to/data task.label_dir=/path/to/label \
  task.tokenizer_bpe_model=/path/to/tokenizer model.w2v_path=/path/to/checkpoint \
  hydra.run.dir=/path/to/experiment/finetune/ common.user_dir=`pwd`

Decode an AV-HuBERT model

Suppose the test.tsv and test.wrd are the video list and transcripts of
the split to be decoded, saved at /path/to/data, and the fine-tuned model is
saved at /path/to/checkpoint.

Seq2Seq decoding

task.normalize needs to be consistent with the value used during fine-tuning.
Decoding results will be saved at

$ cd avhubert
$ python -B infer_s2s.py --config-dir ./conf/ --config-name conf-name \
  dataset.gen_subset=test common_eval.path=/path/to/checkpoint \
  common_eval.results_path=/path/to/experiment/decode/s2s/test \
  override.modalities=['video'] common.user_dir=`pwd`

The command above uses the default decoding hyperparameter, which can be found
in conf/s2s_decode.yaml. override.modalities can be set to ['video'] (for lip reading),
or ['audio'] (for ASR) or ['audio','video'] (for audio-visual speech recognition).These parameters can be
configured from the command line. For example, to search with a beam size of
20, we can append the command above with generation.beam=20.
Important parameters include:

  • generation.beam
  • generation.lenpen

If you want to test your model under noisy environment, append the following to the above command.

+override.noise_wav=/path/to/noise override.noise_prob=1 override.noise_snr={snr}

{snr} is the signal-to-noise ratio (SNR) and /path/to/noise is a folder containing noise manifest files (/path/to/noise/{valid,test}.tsv). See preparation for setting up this folder.


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