Wav2Lip-HQ Inference is a Python implementation of the Wav2Lip-HQ model, a lip-syncing system that generates high-quality talking-face videos. Given an input video of a person speaking and a target audio, this inference script uses the Wav2Lip-HQ model to generate a new video where the person's lips are synced to the provided audio. The model is trained to achieve high-quality lip-syncing results, making it suitable for various applications, including video dubbing, deep fakes, and more.
High-Quality Lip-Syncing: The Wav2Lip-HQ model produces lip-synced videos with realistic lip movements, delivering impressive results.
Real-Time Inference: The inference script efficiently generates lip-synced videos in real-time, making it convenient for rapid experimentation.
Custom Audio Input: Users can provide custom audio files to be synced with the input video, allowing for flexible use cases.
To run the Wav2Lip-HQ inference script, ensure you have the following dependencies installed:
Python 3.7 or higher PyTorch OpenCV dlib ffmpeg
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Clone this repository:
git clone https://github.com/your-username/wav2lip-hq-inference.git -
Install the required dependencies:
pip install -r requirements.txt -
Download the pre-trained Wav2Lip-HQ model weights:
wget -O wav2lip_hq.pth https://example.com/wav2lip_hq.pth
To generate a lip-synced video, follow these steps:
Prepare your input video (with a person speaking) and the target audio file.
Place the input video in the input_vids directory and the audio file in the audio_input directory.
Run the inference script:
python inference.py --checkpoint_path wav2lip_hq.pth --face <input_video_filename> --audio <audio_filename> --outfile <output_filename>
This implementation is based on the original Wav2Lip-HQ repository. We acknowledge the authors for their valuable contributions to the lip-syncing research.
Happy lip-syncing! 🎤📽️