LipSync-Wav2Lip-Project

This repository contains code for lip synchronization using Wav2Lip, a deep learning-based model.

How to Use this Code for Lip Synchronization

git clone https://github.com/Dishantkharkar/LipSync-Wav2Lip-Project.git
cd LipSync-Wav2Lip-Project

pip install -r requirements.txt

Download the pretrained model from s3fd.pth and save it in the face_detection/detection/sfd/ folder.

Navigate to the official Wav2Lip repository and follow the instructions in the README to obtain additional weights.

Place your video and audio files in the folder shown below:

Run the following command to perform lip synchronization:

python inference.py --checkpoint_path <path_to_pretrained_model> --face <path_to_face_video> --audio <path_to_audio_file>

Replace <path_to_pretrained_model>, <path_to_face_video>, and <path_to_audio_file> with the appropriate paths.

Example using newscript.txt file:

The result will be stored in the Result folder with the name result_audio.

you got like this :

For evaluating the model, you can use the provided evaluation script:

python evaluation/evaluate.py --model_path <path_to_model> --data_path <path_to_evaluation_data>

Replace <path_to_model> and <path_to_evaluation_data> with the paths to your trained model and evaluation dataset, respectively.

For more details and updates, refer to the original Wav2Lip README.

Name		Name	Last commit message	Last commit date
Latest commit History 14 Commits
checkpoints		checkpoints
evaluation		evaluation
face_detection		face_detection
filelists		filelists
models		models
results		results
temp		temp
.gitignore		.gitignore
README.md		README.md
audio.py		audio.py
color_syncnet_train.py		color_syncnet_train.py
hparams.py		hparams.py
hq_wav2lip_train.py		hq_wav2lip_train.py
inference.py		inference.py
newscript.txt		newscript.txt
preprocess.py		preprocess.py
requirements.txt		requirements.txt
wav2lip_train.py		wav2lip_train.py