SadTalker Live Demo Studio
Experience how SadTalker takes a single static photo and an audio voice track to generate a realistic, lip-synchronized talking video with natural head motion and GFPGAN face restoration.
Professional Female Presenter & Course Instructor
Clean studio lighting portrait paired with clear, professional instructional narration. Demonstrates steady eye contact, natural blinking timing, and anchored shoulders via --still mode.
Portrait Preprocessing:
• Mode: --preprocess crop
• Face Crop: Auto 3DMM alignment
✓ Unmodified single frame input
Female Studio Narration (Clean English)
python inference.py --source_image ./sadtalker-female-portrait-source.png --driven_audio ./sadtalker-female-narration-audio.wav --result_dir ./results --size 512 --still --preprocess crop --enhancer gfpganExplore All 5 Audio-to-Video Demonstrations
Compare how SadTalker adapts to different voices, head poses, speech tempos, and character demographics.
Emma — Professional Female Presenter & Course Instructor
Clean studio lighting portrait paired with clear, professional instructional narration. Demonstrates steady eye contact, natural blinking timing, and anchored shoulders via --still mode.
Sarah — American Accent Explainer Video Presenter
Conversational American English speech with natural dynamic inflections. Demonstrates fluid jaw motion, subtle cheek muscle movement, and zero visual stuttering.
Alex — Male Digital Avatar & Tech Reviewer
Synthetic 3D character portrait animated with clear male audio. Shows how SadTalker seamlessly animates Midjourney, Stable Diffusion, or 3D digital human avatars.
Marcus — Fast Paced Urban Social Media Creator
High-tempo speech bursts with rapid rhythmic syllables. Tests SadTalker’s latency and demonstrates that speech timing stays locked without drifting.
David — Executive Briefing & Corporate Spokesperson
Deep, resonant male speech paired with a mature corporate portrait. Shows GFPGAN preserving facial creases, natural skin texture, and dignified composure.
SadTalker Best Settings Finder
Choose your photo type, audio recording qualities, and production goal. The engine will determine optimal parameters to avoid distortion, jitter, or GPU out-of-memory errors.
Target Parameters for Your Use Case
python inference.py --source_image ./photo.png --driven_audio ./audio.wav --result_dir ./results --size 512 --still --preprocess crop --enhancer gfpganWant to Generate With Your Own Photo Right Now?
You don't need an expensive local GPU to generate custom SadTalker videos. Use these official free cloud options:
Hugging Face Spaces Demo
The official interactive Gradio web application. Upload your image, record your voice directly with your microphone, and render videos online without installing any software.
- ✓ 100% Free browser GUI — no programming required
- ✓ Supports 256px and 512px models + GFPGAN
- ✓ Instant drag-and-drop upload
Google Colab 1-Click Notebook
Run full SadTalker inference on a free cloud NVIDIA T4 GPU. Perfect for long videos, batch avatar rendering, and testing custom checkpoint weights.
- ✓ Free cloud GPU without local hardware wear
- ✓ Full GFPGAN high-resolution post-processing
- ✓ Step-by-step pre-configured cells
Planning Local Setup on Your Own PC?
SadTalker requires an NVIDIA GPU with CUDA support for best performance. Here are real hardware expectations:
Frequently Asked Questions
Can I test SadTalker for free without coding?
Yes! The official Hugging Face Space offers a zero-code web interface where you can drag and drop your photo and voice track. You can also run the 1-click Google Colab notebook completely in the cloud.
What does the GFPGAN enhancer do?
SadTalker’s 3DMM renderer generates facial movement at 256×256 or 512×512. When the --enhancer gfpgan flag is included, each output frame is passed through GFPGAN to restore natural skin pores, teeth sharpness, and clear eyes, transforming basic animation into a crisp, photorealistic avatar.
Why should I use the --still flag?
By default, SadTalker simulates natural head tilting and breathing motions. For educational courses, corporate explainers, or news broadcasts, excessive swaying can distract viewers. The --still flag locks the torso and neck in place so all motion is dedicated to expressive lip-sync and eye movement.
How long does it take to generate a video?
As seen in our verified benchmarks above, a 10–14 second video (approx. 200–350 frames) requires roughly 25–40 seconds for the face motion generation, followed by 1–2 minutes for GFPGAN enhancement on an NVIDIA GPU (e.g., RTX 3060/4060 or Google Colab T4).