SyncVSR

SyncVSR: Data-Efficient Visual Speech Recognition with End-to-End Crossmodal Audio Token Synchronization (Interspeech 2024)

Visual Speech Recognition (VSR) stands at the intersection of computer vision and speech recognition, aiming to interpret spoken content from visual cues. A prominent challenge in VSR is the presence of homophenes-visually similar lip gestures that represent different phonemes. Prior approaches have sought to distinguish fine-grained visemes by aligning visual and auditory semantics, but often fell short of full synchronization. To address this, we present SyncVSR, an end-to-end learning framework that leverages quantized audio for frame-level crossmodal supervision. By integrating a projection layer that synchronizes visual representation with acoustic data, our encoder learns to generate discrete audio tokens from a video sequence in a non-autoregressive manner. SyncVSR shows versatility across tasks, languages, and modalities at the cost of a forward pass. Our empirical evaluations show that it not only achieves state-of-the-art results but also reduces data usage by up to ninefold.

Overview of SyncVSR

Frame-level crossmodal supervision with quantized audio tokens for enhanced Visual Speech Recognition.

Overview of SyncVSR	Performance of SyncVSR on LRS3

class Model(nn.Module):
    """
    - audio_alignment: Ratio of audio tokens per video frame
    - vq_groups: Number of quantized audio groups (i.e. audio channels number in the output of the codec)
    - audio_vocab_size: Vocabulary size of quantized audio tokens of neural audio codec
    - audio_projection: Linear projection layer for audio reconstruction
    """
    def __init__(self, config):
        ...
        self.audio_projection = nn.Linear(config.hidden_size, audio_alignment * vq_groups * audio_vocab_size)
        self.lambda_audio = 10.0 # Larger the better, recommending at least 10 times larger loss coefficient of the VSR objective

    def forward(self, videos, audio_tokens, ...):
        # Get traditional VSR objective loss such as Word classification loss, CTC loss, and LM loss
        loss_objective = ...

        # Obtain the latent representation from the encoder for input video frames of length seq_len
        # with a special token inserted at the start.
        last_hidden_state = self.encoder(videos) # [B, seq_len+1, hidden_size]

        # Get audio reconstruction loss
        logits_audio = self.audio_projection(last_hidden_state[:, 1:, :]) # [B, seq_len, audio_alignment * vq_groups * audio_vocab_size]
        logits_audio = logits_audio.reshape(B, seq_len, audio_alignment * vq_groups, audio_vocab_size) # [B, seq_len, audio_alignment * vq_groups, audio_vocab_size]
        # For each encoded video frame, it should predict combination of (audio_alignment * vq_groups) audio tokens
        loss_audio = F.cross_entropy(
            logits_audio.reshape(-1, self.audio_vocab_size), # [B * seq_len * (audio_alignment * vq_groups), audio_vocab_size]
            audio_tokens.flatten(), # [B * seq_len * (audio_alignment * vq_groups),]
        )

        # Simply add audio reconstruction loss to the objective loss. That's it!
        loss_total = loss_objective + loss_audio * self.lambda_audio
        ...

Audio Tokens Preparation

We uploaded tokenized audio for LRW, LRS2, LRS3 at the release section. Without installing the fairseq environment, you may load the tokenized audio from the files as below:

# download from the release section below
# https://github.com/KAIST-AILab/SyncVSR/releases/

# and untar the folder.
tar -xf audio-tokens.tar.gz

""" access to the tokenized audio files """
import os
from glob import glob

benchname = "LRW" # or LRS2, LRS3
split = "train"
dataset_path = os.path.join("./data/audio-tokens", benchname)
audio_files = glob(os.path.join(dataset_path, "**", split, "*.pkl"))

""" load the dataset """
import random
import torch

tokenized_audio_sample = torch.load(random.choice(audio_files)) # dictionary type
tokenized_audio_sample.keys() # 'vq_tokens', 'wav2vec2_tokens'

Video Dataset Preparation

1. Get authentification for Lip Reading in the Wild Dataset via https://www.bbc.co.uk/rd/projects/lip-reading-datasets
2. Download dataset using the shell command below

wget --user <USERNAME> --password <PASSWORD> https://thor.robots.ox.ac.uk/~vgg/data/lip_reading/data1/lrw-v1-partaa
wget --user <USERNAME> --password <PASSWORD> https://thor.robots.ox.ac.uk/~vgg/data/lip_reading/data1/lrw-v1-partab
wget --user <USERNAME> --password <PASSWORD> https://thor.robots.ox.ac.uk/~vgg/data/lip_reading/data1/lrw-v1-partac
wget --user <USERNAME> --password <PASSWORD> https://thor.robots.ox.ac.uk/~vgg/data/lip_reading/data1/lrw-v1-partad
wget --user <USERNAME> --password <PASSWORD> https://thor.robots.ox.ac.uk/~vgg/data/lip_reading/data1/lrw-v1-partae
wget --user <USERNAME> --password <PASSWORD> https://thor.robots.ox.ac.uk/~vgg/data/lip_reading/data1/lrw-v1-partaf
wget --user <USERNAME> --password <PASSWORD> https://thor.robots.ox.ac.uk/~vgg/data/lip_reading/data1/lrw-v1-partag

3. Extract region of interest and convert mp4 file into pkl file with the commands below.

python ./src/preprocess_roi.py
python ./src/preprocess_pkl.py

Repository Structure

LRS is for sentence-level lipreading and LRW is for word-level lipreading. In each of the tasks, the repository is organized into two main directories: config and src.

• The config directory contains the configurations for training and inference on the benchmarks we evaluated.
• The src directory holds the source code for modeling, preprocessing, data pipelining, and training.

$ tree
.
├── LRS
│   ├── landmark
│   └── video
│       ├── config
│       │   ├── lrs2.yaml
│       │   └── lrs3.yaml
│       ├── datamodule
│       │   ├── av_dataset.py
│       │   ├── data_module.py
│       │   ├── transforms.py
│       │   └── video_length.npy
│       ├── espnet
│       ├── lightning.py
│       ├── main.py
│       ├── preprocess
│       │   ├── prepare_LRS2.py
│       │   ├── prepare_LRS3.py
│       │   ├── prepare_Vox2.py
│       │   ├── transcribe_whisper.py
│       │   └── utils.py
│       ├── setup.sh
│       ├── spm
│       └── utils.py
├── LRW
│   ├── landmark
│   │   ├── README.md
│   │   ├── config
│   │   │   ├── transformer-8l-320d-1000ep-cmts10-lb0-wb.sh
│   │   │   ├── transformer-8l-320d-1000ep-cmts10-lb0.sh
│   │   │   └── ...
│   │   ├── durations.csv
│   │   ├── setup.sh
│   │   └── src
│   │       ├── dataset.py
│   │       ├── main.py
│   │       ├── modeling.py
│   │       ├── training.py
│   │       ├── transform.py
│   │       └── utils.py
│   └── video
│       ├── config
│       │   ├── bert-12l-512d_LRW_96_bf16_rrc_WB.yaml
│       │   ├── bert-12l-512d_LRW_96_bf16_rrc_noWB.yaml
│       │   └── dc-tcn-base.yaml
│       ├── durations.csv
│       ├── labels.txt
│       ├── setup.sh
│       └── src
│           ├── augment.py
│           ├── data.py
│           ├── inference.py
│           ├── lightning.py
│           ├── preprocess_pkl.py
│           ├── preprocess_roi.py
│           ├── tcn
│           └── train.py
└── README.md

Installation

For the replicating state-of-the-art results from the scratch, please follow the instructions below.

# Install depedency through apt-get
apt-get update 
apt-get -yq install ffmpeg libsm6 libxext6 
apt install libturbojpeg tmux -y

# Install dependencies for sentence-level VSR
git clone https://github.com/KAIST-AILab/SyncVSR.git
cd ./SyncVSR/LRS/video
bash setup.sh 

# Or install dependencies for word-level VSR
cd ./SyncVSR/LRW/video
bash setup.sh

# You may also install dependencies for landmark VSR, trainable on TPU devices.
cd ./SyncVSR/LRW/landmark
bash setup.sh

# (Optional) Install fairseq to use vq-wav2vec audio quantizer. 
# We recommend to use quantized audio tokens at https://github.com/KAIST-AILab/SyncVSR/releases/tag/weight-audio-v1
# Or use wav2vec 2.0's audio quantizer to avoid using fairseq.
git clone https://github.com/pytorch/fairseq
cd fairseq
pip install --editable ./
cd ..
pip install -r requirements.txt
wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/vq-wav2vec_kmeans.pt -P ./

Inference

Word-Level VSR

cd ./SyncVSR/LRW/video
python ./src/inference.py ./config/bert-12l-512d_LRW_96_bf16_rrc_WB.yaml devices=[0]

Sentence-Level VSR

cd ./SyncVSR/LRS/video
python main.py config/lrs2.yaml # evaluating on lrs2 
python main.py config/lrs3.yaml # evaluating on lrs3

Train

Word-Level VSR

After preprocessing the dataset using preprocess_roi.py and preprocess_pkl.py, please change configurations in yaml files in LRW/video/config.

python ./src/train.py ./config/bert-12l-512d_LRW_96_bf16_rrc_WB.yaml devices=[0]

Sentence-Level VSR

After preprocessing the dataset using LRS/video/preprocess, please change configurations in yaml files in LRS/video/config.

cd ./SyncVSR/LRS/video
python main.py config/lrs2.yaml
python main.py config/lrs3.yaml

Acknowledgement

Thanks to the TPU Research Cloud program for providing resources. Models are trained on the TPU v4-64 or TPU v3-8 pod slice.

Citation

If you find SyncVSR useful for your research, please consider citing our paper:

@inproceedings{ahn2024syncvsr,
  author={Young Jin Ahn, Jungwoo Park, Sangha Park, Jonghyun Choi, Kee-Eung Kim},
  title={SyncVSR: Data-Efficient Visual Speech Recognition with End-to-End Crossmodal Audio Token Synchronization},
  year={2024},
  booktitle={Proc. Interspeech 2024},
  doi={10.21437/Interspeech.2024-432}
}