Offline Example of vLLM-omni for Qwen2.5-omni
🛠️ Installation
Environment setup
Use Docker to keep consistent basic environment (Optional, Recommended)
docker run --gpus all --ipc=host --network=host -v $source_dir:$container_dir --name $container_name -it nvcr.io/nvidia/pytorch:25.01-py3 bash
Set up basic uv environment
pip install uv
uv venv --python 3.12 --seed
source .venv/bin/activate
Installation of vLLM (for users)
Now we build it based on vLLM v0.11.0. Please install it with command below.
uv pip install vllm==0.11.0 --torch-backend=auto
Installation of vLLM (for developers)
If you want to check or debug with source code of vLLM, install stable release version of vllm with 0.11.0 from source with pre-built wheel file.
git clone https://github.com/vllm-project/vllm.git
cd vllm
git checkout v0.11.0
Set up environment variables to get pre-built wheels. If there are internet problems, just download the whl file manually. And set VLLM_PRECOMPILED_WHEEL_LOCATION as your local absolute path of whl file.
export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.11.0/vllm-0.11.0-cp38-abi3-manylinux1_x86_64.whl
Install vllm with command below (If you have no existing PyTorch).
uv pip install --editable .
Install vllm with command below (If you already have PyTorch).
python use_existing_torch.py
uv pip install -r requirements/build.txt
uv pip install --no-build-isolation --editable .
Verification for successful installation of vLLM
Just run the command below. If no error, it demonstrates that the installation is successfull.
python -c "import vllm._C"
Installation of vLLM-omni
Install additional requirements for vllm-omni
git clone https://github.com/vllm-project/vllm-omni.git
cd vllm_omni
uv pip install -r requirements.txt
Run examples (Qwen2.5-omni)
Download dataset from seed_tts[https://drive.google.com/file/d/1GlSjVfSHkW3-leKKBlfrjuuTGqQ_xaLP/edit]. To get the prompt, you can:
tar -xf <Your Download Path>/seedtts_testset.tar
cp seedtts_testset/en/meta.lst examples/offline_inference/qwen_2_5_omni/meta.lst
python3 examples/offline_inference/qwen_2_5_omni/extract_prompts.py \
--input examples/offline_inference/qwen_2_5_omni/meta.lst \
--output examples/offline_inference/qwen_2_5_omni/top100.txt \
--topk 100 \
--pt-output examples/offline_inference/qwen_2_5_omni/top100.pt
Get into the example folder
cd examples/offline_inference/qwen_2_5_omni
Modify PYTHONPATH in run.sh as your path of vllm_omni. Then run.
bash run.sh
The output audio is saved in ./output_audio