Intro
I’m running Whisper with (Nvidia) GPU support for local speech to text (STT) recognition. It was surprisingly difficult to find all required information in one place, so I thought I’ll share my results:
To be precise, I’m using the Faster Whisper implementation. There is a linuxserver/faster-whisper docker image, which adds the Wyoming protocol for HA along with GPU support.
I’ve kept my German configuration example as I think this is mostly interesting for non-English setups. Should be easy to adjust though.
Setup
I’m running it externally on a basic Ubuntu server.
Setup: Prerequisites
You’ll first need to install docker (incl. docker compose), if not done yet.
A little less common may be the requirement for Nvidia Container Toolkit to be installed on your host. Example instructions for Ubuntu:
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
export NVIDIA_CONTAINER_TOOLKIT_VERSION=1.17.8-1
sudo apt-get install -y \
nvidia-container-toolkit=${NVIDIA_CONTAINER_TOOLKIT_VERSION} \
nvidia-container-toolkit-base=${NVIDIA_CONTAINER_TOOLKIT_VERSION} \
libnvidia-container-tools=${NVIDIA_CONTAINER_TOOLKIT_VERSION} \
libnvidia-container1=${NVIDIA_CONTAINER_TOOLKIT_VERSION}
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
Setup: Docker Compose Service
Example setup for /opt/faster-whisper:
cd /opt
sudo mkdir faster-whisper
sudo chown server:server faster-whisper
cd faster-whisper
vim docker-compose.yml
docker-compose.yml
services:
faster-whisper:
image: lscr.io/linuxserver/faster-whisper:gpu
container_name: faster-whisper
environment:
- PUID=1000
- PGID=1000
- TZ=Europe/Berlin
- WHISPER_MODEL=large-v3
- WHISPER_LANG=de
- WHISPER_BEAM=20
- LOG_LEVEL=DEBUG
volumes:
- ./faster-whisper/data:/config
- ./faster-whisper/run:/etc/s6-overlay/s6-rc.d/svc-whisper/run
ports:
- 10300:10300
restart: unless-stopped
network_mode: host
runtime: nvidia
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: all
capabilities:
- gpu
- utility
- compute
Setup: Override initial-prompt (optional)
Unfortunately, the docker image does not expose the initial-prompt option yet. So I’ll simply duplicated the file with the relevant command to bind-mount it:
sudo vim /opt/faster-whisper/faster-whisper/run
faster-whisper/run
#!/command/with-contenv bash
# shellcheck shell=bash
export LD_LIBRARY_PATH=$(python3 -c 'import os; import nvidia.cublas.lib; import nvidia.cudnn.lib; print(os.path.dirname(nvidia.cublas.lib.__path__[0]) + "/lib:" + os.path.dirname(nvidia.cudnn.lib.__path__[0]) + "/lib")')
exec \
s6-notifyoncheck -d -n 300 -w 1000 -c "nc -z localhost 10300" \
s6-setuidgid abc python3 -m wyoming_faster_whisper \
--uri 'tcp://0.0.0.0:10300' \
--device cuda \
--model "${WHISPER_MODEL}" \
--beam-size "${WHISPER_BEAM:-1}" \
--language "${WHISPER_LANG:-en}" \
--data-dir /config \
--download-dir /config \
--initial-prompt "Du sollst primär Sprachbefehle für unser Smart Home erkennen. Alle Sätze sind Befehle oder Fragen. Entferne jegliche Hinweise auf Untertitel. Ein Satz fängt selten mit Ich an."
Setup: Final Steps
Run:
docker compose up [-d]
For use with Home Assistant Assist, add the Wyoming integration and supply the hostname/IP and port that Whisper is running on.
Tuning
Tuning: Beam Size
Try different values for WHISPER_BEAM. Default of the original Whisper implementation is 1. Default of Faster Whisper is 5. Higher values should result in higher accuracy, but also higher VRAM usage.
I’m currently using a quite high value of 20, because at least for German accuracy still seems to be a more relevant problem than speed.
Tuning: Model
You can adjust WHISPER_MODEL to any of the predefined models:
_MODELS = {
"tiny.en": "Systran/faster-whisper-tiny.en",
"tiny": "Systran/faster-whisper-tiny",
"base.en": "Systran/faster-whisper-base.en",
"base": "Systran/faster-whisper-base",
"small.en": "Systran/faster-whisper-small.en",
"small": "Systran/faster-whisper-small",
"medium.en": "Systran/faster-whisper-medium.en",
"medium": "Systran/faster-whisper-medium",
"large-v1": "Systran/faster-whisper-large-v1",
"large-v2": "Systran/faster-whisper-large-v2",
"large-v3": "Systran/faster-whisper-large-v3",
"large": "Systran/faster-whisper-large-v3",
"distil-large-v2": "Systran/faster-distil-whisper-large-v2",
"distil-medium.en": "Systran/faster-distil-whisper-medium.en",
"distil-small.en": "Systran/faster-distil-whisper-small.en",
"distil-large-v3": "Systran/faster-distil-whisper-large-v3",
"distil-large-v3.5": "distil-whisper/distil-large-v3.5-ct2",
"large-v3-turbo": "mobiuslabsgmbh/faster-whisper-large-v3-turbo",
"turbo": "mobiuslabsgmbh/faster-whisper-large-v3-turbo",
}
Alternatively, you can also easily use any CTranslate2-compatible model. This link gives you a filtered list of supported models on Hugging Face. Just use the model name (e.g. guillaumekln/faster-whisper-large-v2) from the website for the value of WHISPER_MODEL and Faster Whisper will automatically take care of downloading it etc.
Again, I prefer accuracy and so I’m using the “best” default model large-v3. I’ve also tried
quite a few others, but couldn’t find a better one, even though it may have been optimized for German etc.
Tuning: Initial Prompt
You may want to optimize the --initial-prompt setting mentioned earlier. I haven’t put too many thoughts into this yet though.
Performance
On my 3090 RTX, local speech-to-text recognition now takes about half a second. Quality is the best I could get so far. To be honest, it’s still rather acceptable and not too good yet. Looking forward on receiving feedback to optimize.
