mirror of
https://github.com/thorstenMueller/Thorsten-Voice.git
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52 lines
2.1 KiB
Docker
52 lines
2.1 KiB
Docker
# Dockerfile for running Coqui TTS trainings in a docker container on NVIDIA Jetson platofrm.
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# Based on NVIDIA Jetson ML Image, provided without any warranty as is by Thorsten Müller (https://twitter.com/ThorstenVoice) in august 2021
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FROM nvcr.io/nvidia/l4t-ml:r32.5.0-py3
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RUN echo "deb https://repo.download.nvidia.com/jetson/common r32.4 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
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RUN echo "deb https://repo.download.nvidia.com/jetson/t194 r32.4 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
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RUN apt-get update -y
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RUN apt-get install vim python-mecab libmecab-dev cuda-toolkit-10-2 libcudnn8 libcudnn8-dev libsndfile1-dev locales -y
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# Setting some environment vars
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ENV LLVM_CONFIG=/usr/bin/llvm-config-9
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ENV PYTHONPATH=/coqui/TTS/
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ENV LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH
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# Skipping OPENBLAS_CORETYPE might show "Illegal instruction (core dumped) error
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ENV OPENBLAS_CORETYPE=ARMV8
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ENV NVIDIA_VISIBLE_DEVICES all
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ENV NVIDIA_DRIVER_CAPABILITIES compute,utility
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LABEL com.nvidia.volumes.needed="nvidia_driver"
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# Adjust locale setting to your personal needs
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RUN sed -i '/de_DE.UTF-8/s/^# //g' /etc/locale.gen && \
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locale-gen
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ENV LANG de_DE.UTF-8
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ENV LANGUAGE de_DE:de
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ENV LC_ALL de_DE.UTF-8
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RUN mkdir /coqui
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WORKDIR /coqui
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ARG COQUI_BRANCH
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RUN git clone -b ${COQUI_BRANCH} https://github.com/coqui-ai/TTS.git
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WORKDIR /coqui/TTS
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RUN pip3 install pip setuptools wheel --upgrade
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RUN pip uninstall -y tensorboard tensorflow tensorflow-estimator nbconvert matplotlib
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RUN pip install -r requirements.txt
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RUN python3 ./setup.py develop
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# Jupyter Notebook
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RUN python3 -c "from notebook.auth.security import set_password; set_password('nvidia', '/root/.jupyter/jupyter_notebook_config.json')"
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CMD /bin/bash -c "jupyter lab --ip 0.0.0.0 --port 8888 --allow-root"
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# Build example:
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# nvidia-docker build . -f Dockerfile.Jetson-Coqui --build-arg COQUI_BRANCH=v0.1.3 -t jetson-coqui
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# Run example:
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# nvidia-docker run -p 8888:8888 -d --shm-size 32g --gpus all -v /ssd/___prj/tts/dataset-july21:/coqui/TTS/data jetson-coqui
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# Bash example:
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# nvidia-docker exec -it <containerId> /bin/bash
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