TalkLipPlus net

This repo is a new SoTA method by adds some features based on the official implementation of 'Seeing What You Said: Talking Face Generation Guided by a Lip Reading Expert', CVPR 2023.Paper

Author: Ironeiser.

🔥Feauture🔥

• 🛠️ Add post-processing to enhance the final performance.
• 📊 Add data pre-processing to increase the train data quality.
• 🧑‍🔬 Adjust the method that helps fix the generated face into the original video with higher quality.
• 🏆 The result achieves SoTA compared with the original TalkLip and other methods before 2023.08.
• ⏱️ Train from scratch with a 5-minute video. And infer with no limited 🎧 audio input.

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The following sections haven't been changed, just copied from the official repo.
If you want to run this repo, please try to run it.

python run_finetune_cctv.py # xxx is the data name, refer to code.
python run_info_demo_cctv.py #

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Prerequisite

1. pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html.
2. Install AV-Hubert by following his installation.
3. Install supplementary packages via pip install -r requirements.txt
4. Install ffmpeg. We adopt version=4.3.2. Please double check wavforms extracted from mp4 files. Extracted wavforms should not contain prefix of 0. If you use anaconda, you can refer to conda install -c conda-forge ffmpeg==4.2.3
5. Download the pre-trained checkpoint of face detector pre-trained model and put it to face_detection/detection/sfd/s3fd.pth. Alternative link.

Dataset and pre-processing

1. Download LRS2 for training and evaluation. Note that we do not use the pretrain set.
2. Download LRW for evaluation.
3. To extract wavforms from mp4 files:

python preparation/audio_extract.py --filelist $filelist  --video_root $video_root --audio_root $audio_root

• $filelist: a txt file containing names of videos. We provide the filelist of LRW test set as an example in the datalist directory.
• $video_root: root directory of videos. In LRS2 dataset, $video_root should contains directories like "639XXX". In LRW dataset, $video_root should contains directories like "ABOUT".
• $audio_root: root directory for saving wavforms
• other optional arguments: please refer to audio_extract.py

4. To detect bounding boxes in videos and save it:

python preparation/bbx_extract.py --filelist $filelist  --video_root $video_root --bbx_root $bbx_root --gpu $gpu

• $bbx_root: a root directory for saving detected bounding boxes

• $gpu: run bbx_extract on a specific gpu. For example, 3.

  *If you want to accelerate bbx_extract via multi-thread processing, you can use the following bash script:

  *Please revise variables in the 2-nd to the 9-th lines to make it compatible with your own machine.

sh preprocess.sh

• $file_list_dir: a directory which contains train.txt, valid.txt, test.txt of LRS2 dataset
• $num_thread: number of threads you used. Please do not let it cross 8 with a 24GB GPU, 4 with a 12GB gpu.

Checkpoints

Model	Description	Link
TalkLip (g)	TalkLip net with the global audio encoder	Link
TalkLip (g+c)	TalkLip net with the global audio encoder and contrastive learning	Link
Lip reading observer 1	AV-hubert (large) fine-tuned on LRS2	Link
Lip reading observer 2	Conformer lip-reading network	Link
Lip reading expert	lip-reading network for training of talking face generation	Link

Train

python train.py --file_dir $file_list_dir --video_root $video_root --audio_root $audio_root \
--bbx_root $bbx_root --word_root $word_root --avhubert_root $avhubert_root --avhubert_path $avhubert_path \
--checkpoint_dir $checkpoint_dir --log_name $log_name --cont_w $cont_w --lip_w $lip_w --perp_w $perp_w \
--gen_checkpoint_path $gen_checkpoint_path --disc_checkpoint_path $disc_checkpoint_path

• $file_list_dir: a directory which contains train.txt, valid.txt, test.txt of LRS2 dataset
• $word_root: root directory of text annotation. Normally, it should be equal to $video_root, as LRS2 dataset puts a video file ".mp4" and its corresponding text file ".txt" in the same directory.
• $avhubert_root: path of root of avhubert (should like xxx/av_hubert/avhubert)
• $avhubert_path: download the above Lip reading expert and enter its path
• $checkpoint_dir: a directory to save checkpoint of talklip
• $log_name: name of log file
• $cont_w: weight of contrastive learning loss (default: 1e-3)
• $lip_w: weight of lip reading loss (default: 1e-5)
• $perp_w: weight of perceptual loss (default: 0.07)
• $gen_checkpoint_path(optional): enter the path of a generator checkpoint if you want to resume training from a checkpoint
• $disc_checkpoint_path(optional): enter the path of a discriminator checkpoint if you want to resume training from a checkpoint

Note: Sometimes, discriminator losses may diverge during training (close to 100). Please stop the training and resume it with a reliable checkpoint.

Test

The below command is to synthesize videos for quantitative evaluation in our paper.

python inf_test.py --filelist $filelist --video_root $video_root --audio_root $audio_root \
--bbx_root $bbx_root --save_root $syn_video_root --ckpt_path $talklip_ckpt --avhubert_root $avhubert_root

• $syn_video_root: root directory for saving synthesized videos
• $talklip_ckpt: a trained checkpoint of TalkLip net

Demo

I update the inf_demo.py on 4/April as I previously suppose that the height and width of output videos are the same when I set cv2.VideoWriter(). Please ensure the sampling rate of the input audio file is 16000 hz.

If you want to reenact the lip movement of a video with a different speech, you can use the following command.

python inf_demo.py --video_path $video_file --wav_path $audio_file --ckpt_path $talklip_ckpt --avhubert_root $avhubert_root

• $video_file: a video file (end with .mp4)
• $audio_file: a audio file (end with .wav)

Evaluation

Please follow README.md in the evaluation directory