Building an AI Video Generator with Proper Audio Sync: What I Learned
Source: Dev.to
Why I Built This
Existing AI video tools frustrated me:
- Audio sync was awful – lips moved like a badly dubbed movie.
- Quality was inconsistent – characters would morph halfway through a clip.
- Limited control – you got whatever the model produced, with no way to fine‑tune.
I wanted something that actually worked well, something I’d want to use myself.
What Wan 2.6 Does
Text‑to‑Video
Type a description and get a video.
Example: “A chef flipping a pancake in a sunny kitchen” → a 15‑second 1080p video of exactly that.
Image‑to‑Video
Upload a static image and describe what should happen (e.g., “Make her wave at the camera” or “zoom into the product”).
Text‑to‑Image
Generate custom visuals to use in videos or as standalone images.
All outputs are 1080p, 24 fps, with native audio synchronization.
The Audio Sync Nightmare
When you generate video with AI, each frame is created independently, but speech requires precise mouth shapes at exact milliseconds.
The Challenge
- Understand audio timing.
- Generate the correct phoneme‑specific mouth shapes.
- Keep the face consistent.
- Produce natural‑looking motion.
What Didn’t Work
- Generate video first, add audio later – resulted in ventriloquist‑dummy lips.
- Generate audio first, then video – timing was always slightly off.
- Generate both simultaneously with shared information – finally produced believable lip sync.
The breakthrough was treating audio and video as a single, inter‑dependent generation process.
Keeping Characters Consistent
Early versions would let the subject slowly change into a different person. The solution now is a “memory” system that:
- Captures the subject’s appearance in the first frame.
- Tracks key characteristics (facial features, clothing, style).
- Maintains those features throughout the clip.
It’s not perfect, but far better than the morphing mess.
The 1080p Challenge
Generating high‑quality 1080p video at 24 fps is computationally heavy. We tackled it with:
- Smart upscaling – generate at lower resolution, then intelligently upscale.
- Frame interpolation – produce key frames and interpolate smooth transitions, halving the computational load.
- Optimization everywhere – batch processing, caching, and numerous tweaks.
Result: a 5‑second video now takes ~45 seconds to generate (down from 10+ minutes in early versions).
Making Static Images Move
Image‑to‑video lets you animate a photo based on a prompt. The difficulty is producing natural motion:
- Identify objects in the image.
- Determine realistic motion for each object.
- Ensure the motion matches the prompt (e.g., a natural wave, physics‑based car movement, shape‑preserving product rotation).
After many iterations, the feature feels magical when it works.
Real‑World Use Cases
- Educators creating teaching materials and explainer videos.
- Small businesses making product demos without costly production.
- Authors producing book trailers on a budget.
- Social media managers generating quick content for posts and stories.
- Marketers testing video concepts before full production.
- Hobbyists making cool stuff for fun.
What Works Well
- ✅ Audio sync – lip movements match speech naturally.
- ✅ Quality – professional‑looking 1080p output.
- ✅ Consistency – characters stay recognizable.
- ✅ Ease of use – no complex settings or technical knowledge required.
- ✅ Multiple workflows – text‑to‑video, image‑to‑video, text‑to‑image in one place.
Current Limitations (Being Real)
- Video length – capped at 15 seconds; longer clips are still a challenge.
- Processing time – 45 seconds per 5‑second video could be faster.
- Fine control – users want more precise element manipulation.
- Edge cases – complex prompts sometimes yield unexpected results.
- Hardware requirements – decent compute power is needed for quality generation.
Lessons Learned
- Solve the hardest problem first – tackling audio sync before UI saved a lot of wasted effort.
- Quality > speed (usually) – users notice video quality immediately; 720p would have felt cheap.
- Users surprise you – the range of creative use cases exceeded my expectations.
- Iteration is everything – each version brought noticeable improvements.
- Listen to feedback – real users spot problems and request features you never imagined.
What’s Next
- Longer videos (30 + seconds).
- More granular control over elements and scenes.
- Faster generation via better optimization.
- Improved motion for image‑to‑video.
- Additional customization options.
The roadmap is driven by user needs, not just technical curiosity.
Try It Out
Wan 2.6 is live at wan26.io.
Enter a prompt or upload an image, hit Generate, and receive your video—no complex setup required.
What would you create with AI video generation? Any specific use cases you’d love to see supported? Drop your thoughts in the comments—I’m genuinely curious what the dev community thinks! 💬