AMD GPU ROCm

how to use amd grafic cards for speeding up calculations un tumbleweed similar tu nvidia with cuda

Install rocm meta package

sudo zypper ar https://download.opensuse.org/repositories/science:/GPU:/ROCm:/Work/openSUSE_Tumbleweed/ science_GPU_ROCm
sudo zypper ref
sudo zypper in rocm
sudo usermod -aG render,video  # restart session

Test Installation

clinfo -l

Should output something like this:

Platform #0: AMD Accelerated Parallel Processing
 +-- Device #0: gfx1201
 `-- Device #1: gfx1036

rocminfo | grep gfx

Should output something like

 Name:                    gfx1201                            
     Name:                    amdgcn-amd-amdhsa--gfx1201         
     Name:                    amdgcn-amd-amdhsa--gfx12-generic   
 Name:                    gfx1036                            
     Name:                    amdgcn-amd-amdhsa--gfx1036         
     Name:                    amdgcn-amd-amdhsa--gfx10-3-generic

PyTorch

for python projects like wyoming using pytorch add rocm support to a virtual environment. See https://pytorch.org/get-started/locally/ for the current final pip command

cd your/venv/basedirectory
python3 -m venv wyoming
. wyoming/bin/activate
pip install --upgrade pip
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.4

Test with (should print True)

python -c 'import torch; print(torch.cuda.is_available())'

Wyoming Faster Whisper

running a server for Home Assistant voice commands

mkdir wyoming-faster-whisper-data
git clone https://github.com/rhasspy/wyoming-faster-whisper.git
cd wyoming-faster-whisper
script/setup
HF_HUB_CACHE=../wyoming-faster-whisper-data script/run --device cpu --model large-v3 --language de --uri 'tcp://0.0.0.0:10300' --data-dir ../wyoming-faster-whisper-data --download-dir ../wyoming-faster-whisper-data

Wyoming Piper

see https://github.com/rhasspy/wyoming-piper?tab=readme-ov-file#local-install

mkdir wyoming-piper-data
git clone https://github.com/rhasspy/wyoming-piper.git
cd wyoming-piper
script/setup
curl -L -s "https://github.com/rhasspy/piper/releases/download/v1.2.0/piper_amd64.tar.gz" | tar -zxvf - -C ..
HF_HUB_CACHE=../wyoming-piper-data PATH="../piper:$PATH" script/run --voice de_DE-thorsten-high --uri 'tcp://0.0.0.0:10200' --data-dir ../wyoming-piper-data --download-dir ../wyoming-piper-data --use-cuda

Docker Containers for Wyoming with ROCm

AMD supports only a few distros like SLES, freely available is only Ubuntu. So I stop using tumbleweed and try to use an AMD docker image for onnxruntime

• prepare docker environment

sudo zypper in docker
sudo mv /var/lib/docker /data
sudo ln -s /data/docker /var/lib/docker
sudo sytemctl enable docker
sudo systemctl start docker
sudo usermod -aG docker $USER  # restart session

• prepare and run wyoming image with ROCm and persistence in /var/lib/docker/volumes/whisper-data/_data/

docker pull rocm/onnxruntime:rocm7.0_ub24.04_ort1.22_torch2.8.0
docker run -it -p 10300:10300 -v whisper-data:/data rocm/onnxruntime:rocm7.0_ub24.04_ort1.22_torch2.8.0

• prepare container for wyoming

rocminfo  # check if GPU is visible inside the container
cd /data
mkdir cache data download
apt-get update
apt-get install apt-utils
apt-get install git
apt-get install vim
git clone https://github.com/rhasspy/wyoming-faster-whisper.git
cd wyoming-faster-whisper
script/setup
HF_HUB_CACHE=/data/cache script/run --model large-v3 --language de --uri 'tcp://0.0.0.0:10300' --data-dir /data/data --download-dir /data/download --device cuda

RuntimeError: CUDA failed with error CUDA driver version is insufficient for CUDA runtime version

Todo

For now, I tried to use --device cuda with whisper and failed. It creates an own .venv, but installing torch there with above pip does not help

with docker approach: whisper at start: RuntimeError: CUDA failed with error no CUDA-capable device is detected

zypper info rocm: Version  : 6.4.3-2.2

probably need to adapt image to downgrade from rocm7 to rocm6.4.3

Configure Home Assistant

Settings/Integrations/Wyoming Protocol: Add Service (host and port as used above, type is autodetected) Settings/Voice Assistants: Add Assistant: some name, STT->faster-whisper, TTS->piper