Advanced Lip Reading: LipNet employs a powerful combination of Conv3D and Bidirectional LSTM layers to capture intricate lip movements and convert them into textual representations. Real-time Lip Reading: With LipNet, you can perform lip reading in real-time, making it suitable for applications like live transcription and augmented communication devices. Training and Customization: The project includes tools and documentation for training LipNet on your own dataset, allowing for customization and adaptation to specific use cases.
git clone https://github.com/devjedan/lip-reading-AI.git
pip install -r requirements.txt
Download raw video data from youtube or any other source and custom train the model to match the expectations. test the model on new data and note the results.
Collect and Organize Data:
Gather a dataset of videos with corresponding transcriptions. Each video should contain a person speaking, and the transcriptions should be aligned with the spoken words. Organize the data in a directory structure where each video is associated with its transcript. For example:
data/ ├── video1.mp4 ├── video1.txt ├── video2.mp4 ├── video2.txt └── ...
Extract video frames from the videos and convert them to grayscale. Resize the frames to a consistent size (e.g., 75x46 pixels). Normalize the pixel values. Tokenize the transcriptions into characters or phonemes and convert them to numerical labels.
Split the dataset into training, validation, and test sets to evaluate model performance. Common splits might be 70% for training, 15% for validation, and 15% for testing. Model Architecture: LipNet typically uses a deep learning architecture that combines Convolutional 3D (Conv3D) layers and Bidirectional Long Short-Term Memory (Bi-LSTM) layers for lip reading. Here's an overview of the model architecture:
Input: The model takes as input a sequence of video frames, each represented as a 3D tensor (height, width, time). Conv3D Layers: Conv3D layers are used to capture spatiotemporal features from the video frames. Bidirectional LSTM Layers: Bidirectional LSTM layers process the output of Conv3D layers. They have a dual structure, which allows them to capture both past and future context, making them effective for sequence-to-sequence tasks like lip reading. TimeDistributed Flatten: This layer is applied to the output of the LSTM layers to prepare the data for the final classification layer. Dense Layer: The model ends with a dense layer with a softmax activation function. This layer outputs character probabilities for each time step. Loss Function: LipNet typically uses the Connectionist Temporal Classification (CTC) loss function, suitable for sequence-to-sequence tasks. The CTC loss measures the dissimilarity between the predicted sequence of characters and the ground truth. Optimizer: Choose an optimizer, often Adam or RMSprop, and configure the learning rate. Training: Train the LipNet model using the training dataset. Monitor training progress, including loss and validation metrics, to avoid overfitting. Model Checkpoints: Save model checkpoints during training to ensure you can resume training or perform inference later. Pre-trained Weights (Optional): You can optionally start training with pre-trained weights if they are available. Pre-trained weights may come from a model previously trained on a large dataset for lip reading. To use pre-trained weights, initialize your LipNet model with these weights and fine-tune it on your specific dataset. Fine-tuning typically involves freezing some layers (e.g., Conv3D and lower Bi-LSTM layers) and allowing later layers to adapt to your data. Evaluation and Testing: Validation: Periodically evaluate the model on the validation dataset to monitor its performance. Testing: After training, test the model on the test dataset to assess its lip reading accuracy. Inference: Deploy the trained model for lip reading tasks in real-time or on new video data.
python lipnet.py --input video.mp4
python train.py --dataset data/
- Create a new branch:
git checkout -b feature-name - Make your changes and commit them:
git commit -m 'Add new feature' - Push to the branch:
git push origin feature-name - Submit a pull request