Our application works to support Visual Speech Recognition (VSR) is the ability to recognize words from the mouth movements of a speaking person Lip reading in arabic language,the system using a locally collected dataset that was prepared and photographed through us, and the number of videos (9000), more details in PAPER ..In ResearchGate until modifying it publish in journal
Just a bronze 🥉 .. over 150 participating Teams around Egypt
| Video Input | Processed Input | output in our application |
|---|---|---|
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1- download and extract the code.
2- run the file call demo.ipynb.
3- use the api address that appear for you from running demo.ipynb in test_api.py file .
4- change the video path you want to test in test_api.py file.
In the beginning, we will talk about the first approach to building our owndatabase, which is by collecting videos from YouTube each video isbetween 3 and 7 seconds long and 25 fps. We take from multiple channel ex sada Elbalad and different platforms about 50 people (20males an 30 females), And we have collected 6,000 videos, and it isentered in several stages to be prepared for processing.
The multiple stages are: 1- We upload the video in 360 or 240 qualities.
2- Then we extract text from video
3- Then we make a time stamp in which every word in video said in certain amount of seconds (00:00 00:02 "اخبار")With using tool named “time thing”
Now, we will talk about the second approach to building our database,where in this approach we photographed people with our mobilecamera. 9000 videos were captured for different participants from both genders (i.e. 24 females and 62 males). Age range is 13 to 75 years from the same country. . The videos of the dataset are captured with different background lighting settings, different distances, and using cameras of multismartphones. Different cameras, distances, and background lighting settings guarantee dataset generalization. All used cameras record videos at a rate of 30 frames per second (fps). All 86 speakers uttered each one of the 100 words once (i.e. 9000 videos) This ensures the inclusiveness of the dataset because there are many diversities between people that need to be accounted for, such as: Speediness of speaking, mouth shape and movements, lips geometric features, amount of tongue determined by the redness of the taken mouth frame, the alveolar ridge, teeth, braces, mustache, beard, and makeup.
If you want to use dataset for future work, you can contact a member of our team to take it we provide preprocssing video only now .
One of the most important steps in our application tasks is to preprocess and split the dataset before feeding it to the learning algorithm. In our application, the following preprocessing steps are required:
- The lengths of all videos in the dataset are fixed to exactly one or two second while making sure that each video contains the target word without any errors.
- Each video is converted to 30 frames of images using the Video Capture class from the Python cv2 library
- Each frame is processed using Face Tracker Which tracks the movement of the person's face in the video. You can find more details in https://github.com/mpc001/Visual_Speech_Recognition_for_Multiple_Languages/tree/master/tools
- Each frame is processed to locate the lip area of the face. A cut is made for the frame at the mouth area. As a result, the final frame will only contain the mouth area in order to isolate any interference or noise from the background or other parts of the face.
- Each frame is resized to 112X112 pixels in order to reduce the dataset size without affecting the accuracy.
- Each pixel value in a frame is normalized to a range between 0 and 1 without distorting differences between the frame’s pixels. This is achieved by dividing each pixel value by 255.
During the implementation phase of our application, deep learning models were investigated in order to test the one with the highest recognition accuracy for the Arabic words using the dataset described in subsection dataset .
We pretrain backend with You Tupe videos with fine tuning and train frontend model with camera videos and freeze another layer andmake end to end train model which means 2 model trainingwithout freeze in camera dataset with new face trackerprocessing not cutting with fixed size like paper[1] and made Merge model combine 2 model vf with backend to train vf with transfromer not 2 temp 2 layer CNN as backend that is occurred before in paper[1]. Finally, train this model with different lengths of char ... means backend when You Tube fixed 145 frames with padding and change it 15to 100 when camera dataset and in end-to-end train ... make it variable mean changes to change of frames of video.Output of model is probabilities of SoftMax layer for 40 num classes and loss depend on CTC with greedy search to backward and train model ..made front-end with beam search 100 beam width.Greedy we take the best path in alternative paths and beam walk as tree take the best 100 every alternative paths
If you want to use pre-trained weights for future work, you can contact a member of our team to take it .
| Operation Mode | VO Model | (WER) | (CER) |
|---|---|---|---|
| VO | Greedy | 39.8% | 28.1% |
All rights reserved to our teams who update the model and developed it and built the application
You can communicate through one of team by following e-mails to obtain the pre-trained model and the processed dataset:
1- The pre-trained weights of the Visual Frontend and the Model have been obtained from https://github.com/smeetrs/deep_avsr GitHub repository.
2- We also used this paper in our work https://www.sciencedirect.com/science/article/pii/S1110866522000433
3- We used the LRW, LRS2 and LRS3 lip reading datasets from the BBC https://www.robots.ox.ac.uk/~vgg/data/lip_reading/
4- We used https://github.com/feldberlin/timething.git to Calculate timestamp