Files
vllm-omni/examples/offline_inference/audiox/README.md
T
c708aecb18 [Diffusion] [Model] Support AudioX (#2077)
Signed-off-by: Zhang Jian <jianmusings@gmail.com>
Signed-off-by: Zhang <jianmusings@gmail.com>
Signed-off-by: Zhang <zhang.jian@u.nus.edu>
Signed-off-by: Zhang Jian <e0322744@u.nus.edu>
Signed-off-by: 汪志鹏 <wangzhipeng628@gmail.com>
Co-authored-by: Zhang Jian <e0322744@u.nus.edu>
Co-authored-by: 汪志鹏 <wangzhipeng628@gmail.com>
2026-05-04 10:24:35 -04:00

1.3 KiB

AudioX offline inference

Generate audio with the AudioX MMDiT diffusion pipeline (AudioXPipeline). Six tasks: t2a, t2m, v2a, v2m, tv2a, tv2m.

Prerequisites

Download a vLLM-Omni weight bundle (component-sharded safetensors):

huggingface-cli download zhangj1an/AudioX --local-dir ./audiox_weights

The Hugging Face id zhangj1an/AudioX also works directly without prefetching.

Usage

# Text-to-audio only (default uses zhangj1an/AudioX from the Hub):
python end2end.py --tasks t2a

# All six tasks against a local bundle and a sample video for v2*/tv2*:
python end2end.py \
  --model ./audiox_weights \
  --video https://zeyuet.github.io/AudioX/static/samples/V2M/1XeBotOFqHA.mp4

# Subset of tasks, custom seed and steps:
python end2end.py --tasks t2a tv2a --num-inference-steps 100 --seed 0

Arguments

  • --model: HF id or local bundle path (default: zhangj1an/AudioX).
  • --tasks: any subset of t2a t2m v2a v2m tv2a tv2m (default: all).
  • --video: video file/URL — required for v2* and tv2*.
  • --reference-audio: optional audio prompt (audio-conditioned generation).
  • --num-inference-steps, --guidance-scale, --seed, --seconds-total, --sample-rate, --output-dir: generation knobs.

Outputs land in <output-dir>/<task>.wav as 16-bit stereo WAV.