diff --git a/helperScripts/Dockerfile.Jetson-Coqui b/helperScripts/Dockerfile.Jetson-Coqui
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+# Dockerfile for running Coqui TTS trainings in a docker container on NVIDIA Jetson platofrm.
+# Based on NVIDIA Jetson ML Image, provided without any warranty as is by Thorsten Müller (https://twitter.com/ThorstenVoice) in august 2021
+
+FROM nvcr.io/nvidia/l4t-ml:r32.5.0-py3
+
+RUN echo "deb https://repo.download.nvidia.com/jetson/common r32.4 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
+RUN echo "deb https://repo.download.nvidia.com/jetson/t194 r32.4 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
+
+RUN apt-get update -y
+RUN apt-get install vim python-mecab libmecab-dev cuda-toolkit-10-2 libcudnn8 libcudnn8-dev libsndfile1-dev -y
+
+# Setting some environment vars
+ENV LLVM_CONFIG=/usr/bin/llvm-config-9
+ENV PYTHONPATH=/coqui/TTS/
+ENV LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH
+# Skipping OPENBLAS_CORETYPE might show "Illegal instruction (core dumped) error
+ENV OPENBLAS_CORETYPE=ARMV8
+
+ENV NVIDIA_VISIBLE_DEVICES all
+ENV NVIDIA_DRIVER_CAPABILITIES compute,utility
+LABEL com.nvidia.volumes.needed="nvidia_driver"
+
+RUN mkdir /coqui
+WORKDIR /coqui
+
+ARG COQUI_BRANCH
+RUN git clone -b ${COQUI_BRANCH} https://github.com/coqui-ai/TTS.git
+WORKDIR /coqui/TTS
+RUN pip3 install pip setuptools wheel --upgrade
+RUN pip uninstall -y tensorboard tensorflow tensorflow-estimator nbconvert matplotlib
+RUN pip install -r requirements.txt
+RUN python3 ./setup.py develop
+
+# Jupyter Notebook
+RUN python3 -c "from notebook.auth.security import set_password; set_password('nvidia', '/root/.jupyter/jupyter_notebook_config.json')"
+CMD /bin/bash -c "jupyter lab --ip 0.0.0.0 --port 8888 --allow-root"
+
+
+# Build example:
+#   nvidia-docker build . -f Dockerfile.Jetson-Coqui --build-arg COQUI_BRANCH=v0.1.3 -t jetson-coqui
+# Run example:
+#   nvidia-docker run -p 8888:8888 -d --shm-size 32g --gpus all -v /ssd/___prj/tts/dataset-july21:/coqui/TTS/data jetson-coqui
+# Bash example:
+#   nvidia-docker exec -it <containerId> /bin/bash
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diff --git a/helperScripts/README.md b/helperScripts/README.md
index 3cb60f7..474f19b 100644
--- a/helperScripts/README.md
+++ b/helperScripts/README.md
@@ -4,4 +4,24 @@
 Python script which takes recordings (filesystem and sqlite db) done with Mycroft Mimic-Recording-Studio (https://github.com/MycroftAI/mimic-recording-studio) and creates an audio optimized dataset in widely supported LJSpeech directory structure.
 
 Peter Schmalfeldt (https://github.com/manifestinteractive) did an amazing job as he optimized my originally (quick'n dirty) version of that script, so thank you Peter :-)
-See more details here: https://gist.github.com/manifestinteractive/6fd9be62d0ede934d4e1171e5e751aba#file-mrs2ljspeech-py
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+See more details here: https://gist.github.com/manifestinteractive/6fd9be62d0ede934d4e1171e5e751aba#file-mrs2ljspeech-py
+
+## Dockerfile.Jetson-Coqui
+> Add your user to `docker` group to not require sudo on all operations.
+
+Thanks to NVIDIA for providing docker images for Jetson platform. I use the "machine learning (ML)" image as baseimage for setting up a Coqui environment.
+
+> You can use any branch or tag as COQUI_BRANCH argument. v0.1.3 is just the current stable version.
+
+Switch to directory where Dockerfile is in and run `nvidia-docker build . -f Dockerfile.Jetson-Coqui --build-arg COQUI_BRANCH=v0.1.3 -t jetson-coqui` to build your container image. When build process is finished you can start a container on that image.
+
+
+### Mapped volumes
+We need to bring your dataset and configuration file into our container so we should map a volume on running container
+`nvidia-docker run -p 8888:8888 -d --shm-size 32g --gpus all -v [host path with dataset and config.json]:/coqui/TTS/data jetson-coqui`. Now we have a running container ready for Coqui TTS magic.
+
+### Jupyter notebook
+Coqui provides lots of useful Jupyter notebooks for dataset analysis. Once your container is up and running you should be able to call 
+
+### Running bash into container
+`nvidia-docker exec -it jetson-coqui /bin/bash` now you're inside the container and an `ls /coqui/TTS/data` should show your dataset files.
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