About this model
MMAudio V2 is a multimodal audio generation model that synthesizes sound from text prompts and, optionally, from video, producing tracks that align with both semantic descriptions and visual timing. Within Venice's catalog it is exposed as a text-to-audio tool: you describe an environment, material, or effect, and the model returns matching audio. The same underlying system can also add synchronized soundscapes to silent footage when given a video input.
The model traces to the CVPR 2025 research paper "MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis," whose key innovation is joint training across large-scale audio-text and audio-visual datasets to improve semantic alignment and audio-visual synchrony. It draws on corpora including AudioSet, Freesound, VGGSound, AudioCaps and WavCaps.
The V2 generation centers on the large_44k_v2 weights, the default in the official repository, producing 44 kHz audio. Because the catalog records no earlier same-family entry here, direct V1-versus-V2 comparisons are limited to these published characteristics rather than head-to-head benchmark deltas.
Practical controls include an optional negative prompt to exclude unwanted audio characteristics and duration control for varying clip lengths, with video workflows also offering a mask-away-clip option. These features make it suited to video post-production, sound design, and game-audio prototyping.
This About section is AI-generated from public sources (Claude Opus 4.8), with no human editing. It may contain inaccuracies β verify critical details against the sources listed above.
Data sources: Venice API Β· HuggingFace Β· Wikipedia β enrichment updated 1d ago