The project started as a part of an interview process with some company, I received an email with the following task:
Assignment Object:
Your task is to develop a lip-syncing model using machine learning
techniques. It takes an input image and audio and then generates a video
where the image appears to lip sync with the provided audio. You have to
develop this task using python3.
Requirements:
● Avatar / Image : Get one AI-generated avatar, the avatar may be for a
man, woman, old man, old lady or a child. Ensure that the avatar is
created by artificial intelligence and does not represent real
individuals.
● Audio : Provide two distinct and clear audio recordings—one in Arabic
and the other in English. The duration of each audio clip should be
no less than 30 seconds and no more than 1 minute.
● Lip-sync model: Develop a lip-syncing model to synchronise the lip
movements of the chosen avatar with the provided audio. Ensure the
model demonstrates proficiency in accurately aligning lip motions
with the spoken words in both Arabic and English.
Hint : You can refer to state of the art models in lip-syncing.
I was given about 96 hours to accomplish this task, I spent the first 12 hours sick with a very bad flu and no proper internet connection so I had 84 hours!
After submitting the task on time, I took more time to deploy the project on Streamlight, as I thought it was a fun project and would be a nice addition to my CV:)
Given the provided hint from the company, "You can refer to state-of-the-art models in lip-syncing.", I started looking into the available open-source pre-trained model that can accomplish this task and most available resources pointed towards Wav2Lip. I found a couple of interesting tutorials for that model that I will share below.
1- clone the repo to your local machine.
2- open your terminal inside the project folder and run the following command: pip install -r requirements.txt and then run this command sudo xargs -a packages.txt apt-get install to install the needed modules and packages.
3- open your terminal inside the project folder and run the following command: streamlit run app.py to run the streamlit application.
In order to work with and deploy the wav2lip model I had to make the following changes:
1- Changed the _build_mel_basis() function in audio.py, I had to do that to be able to work with librosa>=0.10.0 package, check this issue for more details.
2- Changed the main() function at the inferance.py to directly take an output from the app.py instead of using the command line arguments.
3- I took the load_model(path) function and added it to app.py and added @st.cache_data in order to only load the model once, instead of using it multiple times, I also modified it
4- Deleted the unnecessary files like the checkpoints to make the Streamlit website deployment easier.
5- Since I'm using Streamlit for deployment and Streamlit Cloud doesn't support GPU, I had to change the device to work with cpu instead of cuda.
6- I made other minor changes like changing the path to a file or modifying import statements.
This part is a documentation for me, just in case, I need to face an issue in the future and also could be helpful for any poor soul who would have to work with Streamlit:
1-
Error downloading object: wav2lip/checkpoints/wav2lip_gan.pth (ca9ab7b): Smudge error: Error downloading wav2lip/checkpoints/wav2lip_gan.pth (ca9ab7b7b812c0e80a6e70a5977c545a1e8a365a6c49d5e533023c034d7ac3d8): batch request: [email protected]: Permission denied (publickey).: exit status 255
Errors logged to /mount/src/ai-lip-sync/.git/lfs/logs/20240121T212252.496674
This essentially Streamlit telling you that it can't handle that big file, upload it to Google Drive, and then load it using Python code later, and no git lfs won't solve the problem :)
A ground rule that I learned here is: that the lighter you make your app, the better and faster it is to deploy it.
I opened a topic with that issue on the Streamlit forum, right here
2- Other issues that I faced a lot were dependency issues -lots of them- and that was mostly due to the fact that I depended on pipreqs to write down my requirements.txt, that pipreqs missed up my modules, it added unneeded ones and missed others, unfortunately, it took me some time to discover that and really slowed me down.
3-
ImportError: libGL.so.1: cannot open shared object file: No such file or directory
I faced that problem during importing cv2 -openCv- and the solution was to install libgl1-mesa-dev and some other packages using apt, you can't just add such packages to the requirements.txt, you need to create a file named packages.txt to do so.
4- Streamlit can't handle heavy processing, I discovered that when I tried to deploy the slow animation button to process video input alongside recording to get more accurate lip-syncing, the application failed directly when I used that button -and I tried to use it twice :)-, and that kinda make sense as Streamlit doesn't have a GPU or even a high ram space -I don't have a good GPU but I have about 64GB ram which was enough to run that function locally- and to solve that issue, I initiated another branch to contain the deployment version that doesn't have the slow animation button and used that branch for deployment while kept the main branch containing that button.
Pushing the checkpoints files:
Given the size of those kind of files, There are 2 ways to handle that.
At the start, I had to use git lfs, here's how to do it:
1- Follow the installation instructions that are suitable for your system from here
2- Use the command git lfs track "*.pth" to let git lfs know that those are your big files.
3- When pushing from the command line -I usually use VS code but it usually doesn't work with big files like .pth files- you need to generate a personal access token, to do so, follow the instructions from here, and then copy the token
4- When pushing the file from the terminal you will be asked to pass a password, don't pass your GitHub profile password, instead pass your personal access token that you got from step 3.
But then Streamlit wasn't capable of even pulling the repo! so I uploaded the model checkpoints and some other files to Google Drive, put them in a public folder, and then used a module called gdown to download those folders when needed! here’s a link to that gdown, it’s straightforward to use and install.
Video preview of the application:
fast animation version
Notice how only the lips are moving.
English version:
simplescreenrecorder-.-.-._.online-video-cutter.com.mp4
Arabic version:
result_voice.mp4
slower animation version
Notice how the eye and the whole face are moving instead of only the lips.
Unfortunately, Streamlit can't handle the computational power that the slower animation version requires and that's why I made it only available on the offline version, which means that you need to run the application locally to try that version.
English version:
simplescreenrecorder-.-.-._.online-video-cutter.com.mp4
Arabic version:
result_voice111.mp4
The only difference between the fast and slow versions of animation here is the fact that the fast version passes only a photo while the slow version passes a video instead.