Which AI Video Generators Make Their Own Audio? (And What to Do When They Don't)

Which AI Video Generators Make Their Own Audio? (And What to Do When They Don't)

Several current AI video generators create sound together with the picture: Google's Veo 3.1, Kling 3.0 and ByteDance's Seedance 2.0 all document native, synchronized audio. When a model or mode returns a silent clip, you add sound afterwards with a video-to-audio model, a sound library, or code-generated sound effects.

Native audio is a good first draft. For ads and edits with fixed lengths, you'll often still replace the music and add timed effects yourself.

What does "native audio" mean for a video model?

Native audio means the video model generates the soundtrack (dialogue, ambience, sound effects, sometimes music) in the same generation as the picture, so the sound is synchronized to on-screen action without a separate audio step.

The opposite is a silent clip that you score in post. Both are normal, and many tools let you choose.

Which AI video generators make their own audio?

Here's what the makers' own documentation says as of October 2026.

Veo 3.1 (Google DeepMind). The Veo page and the Gemini API Veo docs describe video with synchronized audio, including dialogue, ambient sound and effects. Google's Veo 3.1 prompting guide shows how to describe speech and SFX in the prompt.

Kling 3.0 (Kuaishou). Kling's 3.0 model guide lists Native Audio for both Kling 2.6 and 3.0, and describes two modes: "Native Audio" and "No Native Audio." The Kling Video 3.0 page covers multi-shot output up to 15 seconds.

Seedance 2.0 (ByteDance Seed). The launch post describes a joint audio-video architecture that accepts text, image, audio and video inputs, with 15-second multi-shot output and two-channel (stereo) audio.

Runway as a hub. Runway hosts third-party models, and its Veo 3.1 page notes you can generate with native audio or without it when you plan to score the edit yourself.

Model lineups change quickly, so check the current docs before you plan a project around a feature.

Why does my AI video come back silent?

There are three common reasons:

1. The model doesn't generate audio. Many open models on Hugging Face's text-to-video list output picture only.
2. Audio is a mode you didn't turn on. Kling, for example, separates Native Audio from No Native Audio and prices them differently.
3. You used a planning model, not a rendering model. Chat models write prompts and scripts. OpenAI's GPT-6 Astra model page lists audio and video as unsupported, so it can plan a video but not render its sound.

How do I add sound to a silent AI video?

Pick the route that matches what's missing.

For on-screen action (footsteps, splashes, doors): use a video-to-audio model, which watches the frames and generates matching sound. Two open options are MMAudio and HunyuanVideo-Foley, which generates 48 kHz audio from video plus an optional text description. Check each model's license before commercial use.

For music beds: use a licensed library such as Pixabay music or the YouTube Audio Library, or a text-to-music model.

For timed effects (whooshes, risers, stings): pull from Freesound or Pixabay sound effects, or have a code-capable LLM write synthesis code that lands each sound on an exact frame.

For background ambience: a seamless loop can run under the whole clip so a generated scene never sits in dead silence, for example:

Foley is the craft of creating everyday sound effects (footsteps, cloth, props) to match picture.

Should I keep the generated audio or replace it?

Keep generated dialogue and ambience when they match the scene. They are hard to recreate and already in sync.

Replace or layer the rest when you need control:

• Music that must be exactly 15 or 30 seconds with a real ending.
• Brand sounds such as a logo sting that must be identical in every video.
• Hits on specific frames, like a whoosh on a cut or an impact on a reveal.
• Level control. Generated mixes vary, so measure the final mix with ffmpeg's ebur128 filter.

A useful habit is to export the AI clip both with and without audio when the tool allows it, so you can use the generated sound as a guide track.

What prompts work for adding sound in post?

If you're generating effects with code, give the model frame-accurate numbers. This whoosh lands on a cut at frame 30. It uses the same template as this entry from Opus Sounds Directory:

Make a 2 s whoosh into a hit for a hard cut in an AI-generated clip at 30 fps.
Output: one WAV, 48 kHz / 16-bit / stereo, exactly 96000 samples.
Python + numpy/scipy only. Synthesise everything (no samples, no downloads). Fixed random seed.
Sound: band-pass noise sweep rising 300 Hz -> 6 kHz, panned left to right, landing in a short
metallic hit at frame 30 (sample 48000), then a 0.5 s decaying tail. No click at start or end.
Master: true peak <= -1 dBTP.
Verify length and peak with ffmpeg ebur128; confirm the loudest frame is frame 30; render a spectrogram.

For a quiet room tone under a generated scene that came back silent, compare with this ambient entry:

Make a 12 s loopable ambient bed to sit under dialogue at 30 fps.
Output: one WAV, 48 kHz / 16-bit / stereo, exactly 576000 samples.
Python + numpy/scipy only. Synthesise everything. Fixed random seed.
Sound: warm detuned pads low-passed at 2 kHz, faint vinyl-like noise, very slow movement;
keep 1-4 kHz quiet so speech stays clear. Last 50 ms must crossfade cleanly into the first 50 ms.
Master: -14 LUFS integrated, true peak <= -1 dBTP. Verify with ffmpeg ebur128 and report the numbers.

Each entry on the site shows its prompt, the Python code, a spectrogram and measured loudness. Every entry is Claude Opus 5.5 output: the model writes the Python synthesis code that renders the sound.