- prerequisites
- Podman installed (for Windows WSL2 machine provider is recommended) -> installation instructions
- (optional) for GPU acceleration Nvidia Container Toolkit installed -> GPU container access
- clone this repository:
git clone https://github.com/rakuri255/UltraSinger.git
- build the container image
podman build -t ultrasinger .
- run the container (note that the first time you run the container models will be downloaded which may take a while)
- PowerShell:
# remember to replace <desired-output-folder> below podman run ` --rm -it --name ultrasinger ` --device nvidia.com/gpu=all ` -v $env:USERPROFILE\.cache:/app/UltraSinger/src/.cache ` -v <desired-output-folder>:/app/UltraSinger/src/output ` ultrasinger ` python3 UltraSinger.py -i <refer to top-level README.md> # explanation: podman run ` --rm -it --name ultrasinger ` # remove container after run, interactive mode, name the container --device nvidia.com/gpu=all ` # optional, enables GPU acceleration if available, requires step 1.ii -v $env:USERPROFILE\.cache:/app/UltraSinger/src/.cache ` # cache directory for models -v <desired-output-folder>:/app/UltraSinger/src/output ` # output directory ultrasinger ` # container image name, we built this in step 3 python3 UltraSinger.py -i <refer to top-level README.md> # run UltraSinger, refer to top-level README.md for all options
- Bash:
# remember to replace <desired-output-folder> below podman run \ --rm -it --name ultrasinger \ --device nvidia.com/gpu=all \ -v $HOME/.cache:/app/UltraSinger/src/.cache \ -v <desired-output-folder>:/app/UltraSinger/src/output \ ultrasinger \ python3 UltraSinger.py -i <refer to top-level README.md>
- to use cookies for YouTube downloads, you can mount your cookies.txt file into the container:
podman run ` ... ` # same as above -v <path-to-cookies.txt>:/app/UltraSinger/src/cookies.txt ` ultrasinger ` python3 UltraSinger.py --cookiefile cookies.txt -i <refer to top-level README.md>
- PowerShell: