This project provides a pure Python ML framework for upstream GStreamer, supporting a broad range of ML vision and language features.
Supported functionality includes:
- object detection
- tracking
- video captioning
- translation
- transcription
- speech to text
- text to speech
- text to image
- LLMs
- serializing model metadata to Kafka server
Different ML toolkits are supported via the MLEngine abstraction - we have nominal support for
TensorFlow, LiteRT and OpenVINO, but all testing thus far has been done with PyTorch.
These elements will work with your distribution's GStreamer packages as long as the GStreamer version is >= 1.24.
There are two installation options described below: on host machine or on Docker container:
sudo apt update && sudo apt -y upgrade
sudo apt install -y python3-pip python3-venv \
gstreamer1.0-plugins-base gstreamer1.0-plugins-base-apps \
gstreamer1.0-plugins-good gstreamer1.0-plugins-bad \
gir1.2-gst-plugins-bad-1.0 python3-gst-1.0 gstreamer1.0-python3-plugin-loader \
libcairo2 libcairo2-dev git
(adjust Fedora version from 42 to match your version number)
sudo dnf install https://download1.rpmfusion.org/free/fedora/rpmfusion-free-release-42.noarch.rpm https://download1.rpmfusion.org/nonfree/fedora/rpmfusion-nonfree-release-42.noarch.rpm
sudo dnf update -y
sudo dnf install akmod-nvidia xorg-x11-drv-nvidia-cuda -y
sudo dnf upgrade -y
sudo dnf install -y python3-pip \
python3-devel cairo cairo-devel cairo-gobject-devel pkgconfig git \
gstreamer1-plugins-base gstreamer1-plugins-base-tools \
gstreamer1-plugins-good gstreamer1-plugins-bad-free \
gstreamer1-plugins-bad-free-devel python3-gstreamer1
curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv --system-site-packages
source .venv/bin/activate
uv pip install --upgrade pip
uv sync
Now manually install flash-attn wheel (must match your version of python, torch and cuda) For example:
uv pip install ./flash_attn-2.8.3+cu128torch2.9-cp313-cp313-linux_x86_64.whl
Pe-built wheels can be found here: https://github.com/mjun0812/flash-attention-prebuild-wheels/releases
cd $HOME/src
git clone https://github.com/collabora/gst-python-ml.git
echo 'export GST_PLUGIN_PATH=$HOME/src/gst-python-ml/demos:$HOME/src/gst-python-ml/plugins:$GST_PLUGIN_PATH' >> ~/.bashrc
source ~/.bashrc
Important Note:
This Dockerfile maps a local gst-python-ml repository to the container,
and expects this repository to be located in $HOME/src i.e. $HOME/src/gst-python-ml.
To use the host GPU in a docker container, you will need to install the nvidia container toolkit. If running on CPU, these steps can be skipped.
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 update
sudo apt install -y nvidia-container-toolkit
sudo systemctl restart docker
sudo dnf install docker
sudo usermod -aG docker $USER
# Then either log out/in completely, or:
newgrp docker
# 1. Add NVIDIA Container Toolkit repository
curl -s -L https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo | \
sudo tee /etc/yum.repos.d/nvidia-container-toolkit.repo
# 2. Remove Fedora's conflicting partial package (if present)
sudo dnf remove -y golang-github-nvidia-container-toolkit 2>/dev/null || true
# 3. Install the full NVIDIA Container Toolkit
sudo dnf install -y nvidia-container-toolkit
# 4. Configure Docker to use the NVIDIA runtime as default
sudo mkdir -p /etc/docker
sudo tee /etc/docker/daemon.json > /dev/null <<EOF
{
"runtimes": {
"nvidia": {
"path": "/usr/bin/nvidia-container-runtime",
"runtimeArgs": []
}
},
"default-runtime": "nvidia"
}
EOF
# 5. Fix Fedora's broken dockerd ExecStart (required!)
sudo mkdir -p /etc/systemd/system/docker.service.d
sudo tee /etc/systemd/system/docker.service.d/override.conf >/dev/null <<EOF
[Service]
ExecStart=
ExecStart=/usr/bin/dockerd -H fd:// --containerd=/run/containerd/containerd.sock
EOF
# 6. Reload and restart Docker
sudo systemctl daemon-reload
sudo systemctl restart docker
# 7. Verify it works
docker info --format '{{.DefaultRuntime}}' # → should print: nvidia
docker run --rm --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi
docker build -f ./Dockerfile_ubuntu24 -t ubuntu24:latest .
docker build -f ./Dockerfile_fedora42 -t fedora42:latest .
Note: If running on CPU, just remove --gpus all from commands below:
docker run -v ~/src/gst-python-ml/:/root/gst-python-ml -it --rm --gpus all --name ubuntu24 ubuntu24:latest /bin/bash
or
docker run -v ~/src/gst-python-ml/:/root/gst-python-ml -it --rm --gpus all --name fedora42 fedora42:latest /bin/bash
Now, in the container shell, set up uv venv as detailed above.
To use pyml_birdseye, additional pip requirements must be installed from the plugins/python/birdseye folder.
Run gst-inspect-1.0 python to list pyml elements.
- Generate token on PyPI and copy to
.pypirc
[pypi]
username = __token__
password = $TOKEN
- Install build dependencies
pip install setuptools wheel twine
pip install --upgrade build
python -m build
twine upload dist/*
Below are some sample pipelines for the various elements in this project.
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/people.mp4 ! decodebin ! videoconvert ! videoscale ! video/x-raw,width=640,height=480 ! pyml_classifier model-name=resnet18 device=cuda ! videoconvert ! autovideosink
pyml_objectdetector supports all TorchVision object detection models.
Simply choose a suitable model name and set it on the model-name property.
A few possible model names:
fasterrcnn_resnet50_fpn
ssdlite320_mobilenet_v3_large
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/people.mp4 ! decodebin ! videoconvert ! videoscale ! video/x-raw,width=640,height=480 ! pyml_objectdetector model-name=fasterrcnn_resnet50_fpn device=cuda batch-size=4 ! videoconvert ! pyml_overlay ! videoconvert ! autovideosink
a) run pipeline from host
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/people.mp4 ! decodebin ! videoconvert ! videoscale ! video/x-raw,width=640,height=480 ! pyml_objectdetector model-name=fasterrcnn_resnet50_fpn device=cuda batch-size=4 ! pyml_kafkasink schema-file=data/pyml_object_detector.json broker=localhost:29092 topic=test-kafkasink-topic
b) run pipeline from docker
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/people.mp4 ! decodebin ! videoconvert ! videoscale ! video/x-raw,width=640,height=480 ! pyml_objectdetector model-name=fasterrcnn_resnet50_fpn device=cuda batch-size=4 ! pyml_kafkasink schema-file=data/pyml_object_detector.json broker=kafka:9092 topic=test-kafkasink-topic
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/people.mp4 ! decodebin ! videoconvert ! videoscale ! pyml_maskrcnn device=cuda batch-size=4 model-name=maskrcnn_resnet50_fpn ! videoconvert ! pyml_overlay ! videoconvert ! autovideosink
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/soccer_tracking.mp4 ! decodebin ! videoconvertscale ! video/x-raw,width=640,height=480 ! pyml_yolo model-name=yolo11m device=cuda:0 track=True ! pyml_overlay ! videoconvert ! autovideosink
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/soccer_tracking.mp4 ! decodebin ! videoconvertscale ! video/x-raw,width=640,height=480,format=RGB ! pyml_streammux name=mux filesrc location=data/soccer_tracking.mp4 ! decodebin ! videoconvertscale ! video/x-raw,width=640,height=480,format=RGB ! mux. mux. ! pyml_yolo model-name=yolo11m device=cuda:0 track=True ! pyml_streamdemux name=demux demux. ! queue ! videoconvert ! pyml_overlay ! videoconvert ! autovideosink sync=false demux. ! queue ! videoconvert ! pyml_overlay ! videoconvert ! autovideosink sync=false
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/soccer_tracking.mp4 ! decodebin ! videoconvertscale ! video/x-raw,width=640,height=480 ! demo_soccer model-name=yolo11m device=cuda:0 ! pyml_overlay ! videoconvert ! autovideosink
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/air_traffic_korean_with_english.wav ! decodebin ! audioconvert ! pyml_whispertranscribe device=cuda language=ko initial_prompt = "Air Traffic Control은, radar systems를, weather conditions에, flight paths를, communication은, unexpected weather conditions가, continuous training을, dedication과, professionalism" ! fakesink
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/air_traffic_korean_with_english.wav ! decodebin ! audioconvert ! pyml_whispertranscribe device=cuda language=ko translate=yes ! fakesink
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/air_traffic_korean_with_english.wav ! decodebin ! audioconvert ! audioresample ! pyml_demucs device=cuda ! wavenc ! filesink location=separated_vocals.wav
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/air_traffic_korean_with_english.wav ! decodebin ! audioconvert ! pyml_whispertranscribe device=cuda language=ko translate=yes ! pyml_coquitts device=cuda ! audioconvert ! wavenc ! filesink location=output_audio.wav
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/air_traffic_korean_with_english.wav ! decodebin ! audioconvert ! pyml_whispertranscribe device=cuda language=ko translate=yes ! pyml_whisperspeechtts device=cuda ! audioconvert ! wavenc ! filesink location=output_audio.wav
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/air_traffic_korean_with_english.wav ! decodebin ! audioconvert ! pyml_whispertranscribe device=cuda language=ko translate=yes ! pyml_mariantranslate device=cuda src=en target=fr ! fakesink
Supported src/target languages:
https://huggingface.co/models?sort=trending&search=Helsinki
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/air_traffic_korean_with_english.wav ! decodebin ! audioconvert ! pyml_whisperlive device=cuda language=ko translate=yes llm-model-name="microsoft/phi-2" ! audioconvert ! wavenc ! filesink location=output_audio.wav
-
generate HuggingFace token
-
huggingface-cli loginand pass in token -
LLM pipeline (in this case, we use phi-2)
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/prompt_for_llm.txt ! pyml_llm device=cuda model-name="microsoft/phi-2" ! fakesink
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/prompt_for_stable_diffusion.txt ! pyml_stablediffusion device=cuda ! pngenc ! filesink location=output_image.png
(should also work with "microsoft/Phi-3.5-vision-instruct" model)
GST_DEBUG=3 gst-launch-1.0 filesrc location=data/soccer_single_camera.mp4 ! decodebin ! videoconvertscale ! video/x-raw,width=640,height=480 ! tee name=t t. ! queue ! textoverlay name=overlay wait-text=false ! videoconvert ! autovideosink t. ! queue leaky=2 max-size-buffers=1 ! videoconvertscale ! video/x-raw,width=240,height=180 ! pyml_caption_qwen device=cuda:0 prompt="In one sentence, describe what you see?" model-name="Qwen/Qwen2.5-VL-3B-Instruct-AWQ" name=cap cap.src ! fakesink async=0 sync=0 cap.text_src ! queue ! coalescehistory history-length=10 ! pyml_llm model-name="Qwen/Qwen3-0.6B" device=cuda system-prompt="You receive the history of what happened in recent times, summarize it nicely with excitement but NEVER mention the specific times. Focus on the most recent events." ! queue ! overlay.text_sink
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/soccer_single_camera.mp4 ! decodebin ! videoconvert ! pyml_birdseye ! videoconvert ! autovideosink
GST_DEBUG=4 gst-launch-1.0 filesrc location=data/soccer_single_camera.mp4 ! decodebin ! videorate ! video/x-raw,framerate=30/1 ! videoconvert ! pyml_birdseye ! videoconvert ! openh264enc ! h264parse ! matroskamux ! filesink location=output.mkv
docker network create kafka-network
and list networks
docker network ls
To launch a docker instance with the kafka network, add --network kafka-network
to the docker launch command above.
Note: setup below assumes you are running your pipeline in a docker container.
If running pipeline from host, then the port changes from 9092 to 29092,
and the broker changes from kafka to localhost.
docker stop kafka zookeeper
docker rm kafka zookeeper
docker run -d --name zookeeper --network kafka-network -e ZOOKEEPER_CLIENT_PORT=2181 confluentinc/cp-zookeeper:latest
docker run -d --name kafka --network kafka-network \
-e KAFKA_ZOOKEEPER_CONNECT=zookeeper:2181 \
-e KAFKA_ADVERTISED_LISTENERS=INSIDE://kafka:9092,OUTSIDE://localhost:29092 \
-e KAFKA_LISTENER_SECURITY_PROTOCOL_MAP=INSIDE:PLAINTEXT,OUTSIDE:PLAINTEXT \
-e KAFKA_LISTENERS=INSIDE://0.0.0.0:9092,OUTSIDE://0.0.0.0:29092 \
-e KAFKA_INTER_BROKER_LISTENER_NAME=INSIDE \
-e KAFKA_BROKER_ID=1 \
-e KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR=1 \
-p 9092:9092 \
-p 29092:29092 \
confluentinc/cp-kafka:latest
docker exec kafka kafka-topics --create --topic test-kafkasink-topic --bootstrap-server kafka:9092 --partitions 1 --replication-factor 1
docker exec -it kafka kafka-topics --list --bootstrap-server kafka:9092
docker exec -it kafka kafka-topics --delete --topic test-topic --bootstrap-server kafka:9092
docker exec -it kafka kafka-console-consumer --bootstrap-server kafka:9092 --topic test-kafkasink-topic --from-beginning
GST_DEBUG=4 gst-launch-1.0 videotestsrc ! video/x-raw,width=1280,height=720 ! pyml_overlay meta-path=data/sample_metadata.json tracking=true ! videoconvert ! autovideosink
GST_DEBUG=4 gst-launch-1.0 videotestsrc pattern=ball ! video/x-raw, width=320, height=240 ! queue ! pyml_streammux name=mux videotestsrc pattern=smpte ! video/x-raw, width=320, height=240 ! queue ! mux.sink_1 videotestsrc pattern=smpte ! video/x-raw, width=320, height=240 ! queue ! mux.sink_2 mux.src ! queue ! pyml_streamdemux name=demux demux.src_0 ! queue ! glimagesink demux.src_1 ! queue ! glimagesink demux.src_2 ! queue ! glimagesink