Most open video generation models produce silent clips. MMAudio solves the obvious next problem: given a video, generate the sound it should make, synchronized to what is happening on screen.
It is also small. At 157 million parameters it generates an eight-second clip in a little over a second, which makes it a practical final stage in a video pipeline rather than a bottleneck.
What is MMAudio?
MMAudio is an open-source video-to-audio model presented at CVPR 2025. It generates Foley: the environmental and event-based sound effects that make video feel real. Footsteps landing, a door closing, rain on a surface, an impact at the moment of impact.
- Parameters: 157 million
- Speed: roughly 1.23 seconds to generate eight seconds of audio
- Input: video, optionally guided by a text prompt
- Approach: multimodal joint training across video, audio, and text in a single transformer
That joint training is what distinguishes it. Rather than learning audio and video separately and hoping alignment emerges, MMAudio trains across all three modalities together, which is why the output lands on the right frames. It reports state-of-the-art results among public models on audio quality, semantic alignment, and audio-visual synchronization.
Why run MMAudio on Runpod?
It completes a video pipeline. If you are generating clips with LTX-2, Wan, or another open video model, MMAudio is the stage that gives them sound. Running it on the same infrastructure is the obvious arrangement.
Video work is bursty by nature. You generate a batch of clips, add audio, review, iterate. Per-second billing suits that rhythm better than a machine sitting idle between sessions.
The model is small enough to be an afterthought. On a GPU you already have running for video generation, MMAudio adds little overhead. Its cost profile makes it easy to include.
GPU requirements for MMAudio
Undemanding, because the model is small.
- Standard generation: RTX A5000 (24 GB) from $0.16/hr, or RTX 4090 from $0.34/hr
- Alongside a video generation model: whatever the video model needs. MMAudio adds a small increment on top.
- Batch processing: L40S (48 GB) from $0.79/hr for parallel jobs
There is no reason to provision a large GPU for MMAudio alone. If you are running it standalone, the smallest available card will do.
Step 1: Create a Runpod account
Go to runpod.io and sign up with email, GitHub, or Google. Add credits or a payment method.
Step 2: Deploy a pod
- Open the Pods section and select an RTX A5000 or RTX 4090.
- Choose a PyTorch template with CUDA support.
- Set container disk to at least 30 GB for model weights and video files.
- Attach a network volume if you are processing a batch, so inputs and outputs persist.
- Deploy.
Step 3: Generate audio for a clip
Connect through JupyterLab or the web terminal and install MMAudio from its repository. Point it at a video file and it produces a synchronized audio track.
Text prompting is optional but useful. Left alone, the model infers what the scene should sound like. Given a prompt, you can steer it: "heavy rain on a metal roof" produces something different from "light drizzle on leaves" for the same input footage.
Pro tips
Prompt for texture, not events. The model reads the events from the video. What it benefits from you specifying is the character of the sound: the surface, the weather, the space it is happening in.
Generate several takes. At roughly a second per clip, producing multiple candidates and picking the best costs almost nothing. Foley is subjective enough that this is usually worth doing.
Chain it after video generation. The natural pipeline is video model, then MMAudio, then review. Running both on one pod avoids shuffling large files between machines.
Check synchronization on impacts. Sharp transient sounds are where sync errors are most audible. If footsteps and impacts land correctly, ambient layers almost always will too.
Stop your pod when you are done. Runpod bills by the second while a pod runs.
Wrapping up
MMAudio fills a specific gap neatly. Open video generation has advanced quickly, but most of it produces silence, and adding convincing Foley by hand is slow specialist work. A 157M-parameter model that does it in about a second per clip is a genuinely useful addition to a generation pipeline.
Ready to try it? Deploy a pod and run it against a clip you have already generated.
FAQ
What is Foley?
The environmental and event-based sound effects added to video in post-production: footsteps, doors, impacts, rustling fabric, weather. Traditionally recorded by hand by specialists, which is why generating it automatically is useful.
What GPU do I need for MMAudio?
Very little. At 157M parameters it runs comfortably on an RTX A5000 from $0.16/hr. If you are already running a video generation model on a larger GPU, MMAudio adds minimal overhead on top.
How fast is MMAudio?
Roughly 1.23 seconds to generate eight seconds of audio, which makes it practical to produce several takes and choose between them rather than accepting the first result.
Can I control the sound it generates?
Yes, through text prompting alongside the video input. The model reads events from the footage, so prompts are most useful for specifying texture and character: surface materials, weather, the acoustic space.
Does MMAudio work with generated video?
Yes, and that is a common use. Most open video models produce silent output, so pairing them with MMAudio gives you clips with synchronized sound from a single pipeline.
