MMAudio synthesizes a cinematic soundtrack from video and optional text — it does not pull the original audio out of the file. That distinction matters: most “video to audio” pages are extractors. MMAudio is a research video-to-audio model (CVPR 2025) that needs a GPU, so this page does not pretend to run it in your browser. Paste a link in the live BibiGPT tool below to read the picture and the spoken content first — the understanding half of a soundtrack workflow.
Paste a video or podcast URL — BibiGPT returns key points, a transcript, and a visual read. The live app loads inline. This is not an in-browser MMAudio renderer.
MMAudio writes a new soundtrack that follows the picture; extractors copy the audio already inside the file. This page explains the generation intent, then hands you a live BibiGPT embed so you can summarize and visually read the same clip before you go looking for a GPU demo.
MMAudio is a video-to-audio generation model: given a clip and optional text, it writes a new, frame-synced soundtrack. Extraction tools copy the track that is already in the container. Mixing those two intents is how search pages cannibalize each other.
An extractor answers “give me the MP3 that is already in this MP4.” MMAudio answers “invent foley, ambience, or a score that matches what is on screen.” Silent AI video, temp-track cuts, and research demos live in the second bucket.
The 2024–2025 MMAudio work jointly trains on video-audio and text-audio data, then aligns audio latents to video frames. You can steer the sound with a short prompt, but the picture still drives timing.
Public checkpoints are in the 157M-parameter class and the authors report about 1.23 seconds to generate an 8-second clip on GPU. There is no WebAssembly port. A landing page that “converts” in-tab is extracting, not running MMAudio.
Soundtrack work starts with knowing what the picture is doing. BibiGPT turns the same link into a timestamped transcript, a structured summary, and a visual read — the brief you would hand a sound designer, without assembling a research stack.
A generator matches motion and prompt text. It does not tell you the argument of a lecture, the joke timing of a short, or which objects actually appear. Summaries and visual analysis fill that gap.
Paste a YouTube, Bilibili, or local-file workflow into the embed above. You get notes you can search and export. The generation half still belongs on a research demo or a studio box.
If you truly need the original track, use an MP4-to-MP3 extractor. This page will not rebrand that job as MMAudio. One intent, one URL.
You will not render MMAudio here. You will leave with a brief of the clip and a clear split between generation and extraction.
Use the embed under the hero. Paste a video or podcast URL. The BibiGPT app loads inline — no extra tab required to start.
Get a structured summary, a timestamped transcript, and a visual take on what appears on screen. That is the brief a soundtrack pass needs.
Silent or badly scored picture → a GPU MMAudio demo or a sound editor. Original track you must keep → an extractor. Do not send both jobs to the same URL.
Three tools, three jobs. Search engines and people both need this split kept honest.
| Capability | MMAudio (generation) | Extract-audio tools | BibiGPT |
|---|---|---|---|
| Output | New soundtrack synced to picture | Original audio stream, copied out | Transcript, summary, visual analysis |
| Runs in the browser | No — GPU / research demo | Often yes (local convert) | Yes — paste a link |
| Same as “video to audio”? | Only the generation sense | Yes — extraction sense | No — understanding the clip |
| Best when | Clip is silent or needs a new score | You must keep the source track | You need to know what happens first |
Where generation, extraction, and understanding should not share a URL.
A text-to-video clip arrives without foley. First, BibiGPT tells you what is actually on screen. Then a generation model can propose ambience that follows those events — not a ripped MP3 from an empty audio track.
The picture is locked; the placeholder music is licensed or wrong. Read the scene beats from the summary, then generate or design a score. Extraction would only give you the temp track you already hate.
This is the extraction and transcription job. Pull the original voice, then summarize it. MMAudio would invent a new bed and throw away the words that matter. Send this intent to an extractor plus BibiGPT, not to a generator.
Model facts below follow the paper and the authors’ project page, not reseller copy.
MMAudio synthesizes high-quality, synchronized audio from video and optional text via multimodal joint training and a frame-level synchronization module. Authors report 157M parameters and 1.23s to generate an 8-second clip, with code and demo at the project page.
Cheng et al., arXiv:2412.15322 (CVPR 2025) ↗Project page, code, Hugging Face demo, and Colab for MMAudio are published by the authors at hkchengrex.github.io/MMAudio.
MMAudio project page ↗MMAudio is a multimodal generative model that synthesizes synchronized audio from video and optional text. Joint training on video-audio and text-audio data teaches semantic alignment; a synchronization module lines audio latents up with video frames. The result is a new soundtrack, not an extraction of the source file.
Video-to-audio generation is the task of producing a new audio waveform that matches a video’s events and timing, sometimes steered by a text prompt. It is used when a clip is silent, poorly scored, or generated without sound. It is not speech recognition and it is not ripping an MP3 out of an MP4.
Audio extraction copies the audio stream already stored in a video container into a file such as MP3 or WAV. No new sound is invented. Extraction is the right tool when the original voice or music must be preserved; generation is the right tool when that stream is missing or wrong.
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