SadTalker, Wav2Lip, and HuggingFace: AI Technologies for Face Animation
Introduction
In the world of Artificial Intelligence, innovative technologies such as SadTalker, Wav2Lip, and HuggingFace have revolutionized video processing, text-to-speech, and face animation.
These tools have become essential in content creation, marketing, education, and even entertainment.
What is SadTalker?
SadTalker is an AI-driven technology that converts static images into animated videos synchronized with audio.
It provides highly accurate facial expressions and lip movements, making it ideal for creating realistic virtual avatars.
🔑 Keywords: SadTalker, Face Animation, Talking Avatar, AI Video Generator
What is Wav2Lip?
Wav2Lip is one of the most advanced lip-sync technologies powered by AI.
It allows combining any audio track with a video of a person speaking, producing natural and synchronized lip movements.
✅ Use cases:
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Educational video production
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Enhancing dubbing quality
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Interactive marketing content
🔑 Keywords: Wav2Lip, Lip Sync AI, AI Dubbing, Talking Face
What is HuggingFace?
HuggingFace is an open-source platform and library that hosts thousands of state-of-the-art AI models, including:
It has become the central hub for developers and researchers to access, share, and deploy AI models worldwide.
🔑 Keywords: HuggingFace, AI Models, NLP, Machine Learning
Practical Examples (Command Lines)
🎥 Running SadTalker
PowerShell • AI Video Node
● LIVE
PS E:\ai-video-node\SadTalker> .\.venv\Scripts\Activate.ps1
(.venv) PS E:\ai-video-node\SadTalker> python .\inference.py `
--driven_audio "E:\ai-video-node\projects\demo2\audio\scene_2_16k.wav" `
--source_image "E:\ai-video-node\projects\demo2\output\002.png" `
--preprocess full `
--still `
--size 512 `
--checkpoint_dir ".\checkpoints" `
--result_dir "E:\ai-video-node\projects\demo2\output\sadtalker\scene_1" `
--enhancer gfpgan
using safetensor as default
3DMM Extraction for source image
landmark Det:: 100% |█████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 5.91it/s]
3DMM Extraction In Video:: 100% |█████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 23.62it/s]
mel:: 100% |███████████████████████████████████████████████████████████████████████| 367/367 [00:00<00:00, 39768.25it/s]
audio2exp:: 100% |█████████████████████████████████████████████████████████████████████| 37/37 [00:00<00:00, 158.94it/s]
Face Renderer:: 56% |███████████████████████████████████▊ | 103/184 [18:59<15:30, 11.48s/it]
PS E:\ai-video-node\SadTalker> python .\inference.py `
--driven_audio "E:\ai-video-node\projects\demo2\audio\scene_2_16k.wav" `
--source_image "E:\ai-video-node\projects\demo2\output\002.png" `
--preprocess full --still --size 512 `
--checkpoint_dir ".\checkpoints" `
--result_dir "E:\ai-video-node\projects\demo2\output\sadtalker\scene_1" `
--enhancer gfpgan
NVIDIA-SMI • Live
PS E:\ai-video-node\Wav2Lip> nvidia-smi -l 1
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 573.06 Driver Version: 573.06 CUDA Version: 12.8 |
| 0 Quadro T1000 WDDM | Mem: 2797MiB / 4096MiB | Util: 75~88% |
+-----------------------------------------------------------------------------------------+
