Sora Audio AI: How OpenAI Synchronised Sound and Video
The defining feature of Sora 2 isn't the pixels—it's the audio. For decades, computer vision and computer audio were separate disciplines. Sora 2 collapses them into a single multimodal generation process. But how does it actually work, and why does the sound perfectly match the visual cue of a door slamming or a glass breaking?
The "Silent Movie" Problem
Before Sora 2, AI video generation was like the silent film era. You could generate a stunning explosion, but the silence broke the immersion immediately. Creators had to find a stock sound effect of "explosion," layer it in a timeline, and manually sync the transient spike with the visual flash.
Sora 2 solves this by training on video-audio pairs. It doesn't just learn that a dog looks like a dog; it learns that a barking dog has a specific waveform associated with the opening of its mouth. When it generates the visual frames of a bark, the transformer model simultaneously predicts the corresponding audio tokens.
Types of Audio Generation
Sora 2 generates three distinct layers of audio simultaneously:
1. Foley (Sound Effects)
The immediate physical sounds. Footsteps on gravel vs. pavement. The click of a lighter. The woosh of a passing car. This requires precise frame-level synchronisation.
2. Ambient (Soundscapes)
The environment. Wind, room tone, distant traffic. This provides the "glue" that makes a scene feel real and continuous.
3. Speech (Voice)
The most difficult layer. Synchronising phonemes (sounds) with visemes (mouth shapes). Sora 2 excels here, though emotion sometimes drifts.
Spatial Audio Simulation
One subtle but powerful feature is simulated spatial audio. If a car drives from left to right in the generated video, the audio pans from the left channel to the right channel. Sora 2 understands depth and direction.
Early tests show that closer objects sound "drier" (less reverb), whilst distant objects have more environmental reflection. This suggests the model has learned a rudimentary physics simulation of sound propagation.
Where it Fails
The illusion breaks in complex auditory environments. For example, if you generate a video of a crowded party, the "chat" often sounds like gibberish or a loop of generic murmuring rather than distinct conversations. Specificity is still a challenge—asking for "a 1967 Mustang engine sound" might yield a generic car engine noise rather than the specific growl of that model.
Why This Matters for Creators
Integrated audio drastically reduces the "Time to Publish." A 30-second social media clip that used to take 2 hours (15 mins generation, 1 hour 45 mins sound design) now takes 5 minutes. The barrier to entry for high-quality storytelling has lowered significantly.
