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.
@article{shi2022avhubert,
author = {Bowen Shi and Wei-Ning Hsu and Kushal Lakhotia and Abdelrahman Mohamed},
title = {Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction},
journal = {arXiv preprint arXiv:2201.02184}
year = {2022}
}
@article{shi2022avsr,
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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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 ./
$ 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]
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. Thelabel_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.
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/ \
common.user_dir=`pwd`
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`
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
.
task.normalize
needs to be consistent with the value used during fine-tuning.
Decoding results will be saved at
/path/to/experiment/decode/s2s/test
.
$ 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 your test data are stored in a different directory with the training data, append the following to the above command.
+override.data=/path/to/test +override.label_dir=/path/to/test
, where /path/to/test
contains test.{tsv,wrd}
. This is useful when you want to test with the fine-tuned checkpoints we provide.
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.