Dockerfile.train

# This is a Dockerfile useful for training models with Coqui STT.
# You can train "acoustic models" with audio + Tensorflow, and
# you can create "scorers" with text + KenLM.

FROM nvcr.io/nvidia/tensorflow:20.06-tf1-py3 AS kenlm-build
ENV DEBIAN_FRONTEND=noninteractive

RUN apt-get update && \
    apt-get install -y --no-install-recommends \
    build-essential cmake libboost-system-dev \
    libboost-thread-dev libboost-program-options-dev \
    libboost-test-dev libeigen3-dev zlib1g-dev \
    libbz2-dev liblzma-dev && \
    rm -rf /var/lib/apt/lists/*

# Build KenLM to generate new scorers
WORKDIR /code
COPY kenlm /code/kenlm
RUN cd /code/kenlm && \
    mkdir -p build && \
    cd build && \
    cmake .. && \
    make -j $(nproc) || \
    ( echo "ERROR: Failed to build KenLM."; \
    echo "ERROR: Make sure you update the kenlm submodule on host before building this Dockerfile."; \
    echo "ERROR: $ cd STT; git submodule update --init kenlm"; \
    exit 1; )


FROM nvcr.io/nvidia/tensorflow:20.06-tf1-py3
ENV DEBIAN_FRONTEND=noninteractive

RUN apt-get update && \
    apt-get install -y --no-install-recommends \
        git \
        wget \
        libopus0 \
        libopusfile0 \
        libsndfile1 \
        sox \
        libsox-fmt-mp3 && \
    rm -rf /var/lib/apt/lists/*

# Make sure pip and its dependencies are up-to-date
RUN pip3 install --upgrade pip wheel setuptools

WORKDIR /code

COPY native_client /code/native_client
COPY .git /code/.git
COPY training/coqui_stt_training/VERSION /code/training/coqui_stt_training/VERSION
COPY training/coqui_stt_training/GRAPH_VERSION /code/training/coqui_stt_training/GRAPH_VERSION

# Build CTC decoder first, to avoid clashes on incompatible versions upgrades
RUN cd native_client/ctcdecode && make NUM_PROCESSES=$(nproc) bindings
RUN pip3 install --upgrade native_client/ctcdecode/dist/*.whl

COPY setup.py /code/setup.py
COPY VERSION /code/VERSION
COPY training /code/training
# Copy files from previous build stages
RUN mkdir -p /code/kenlm/build/
COPY --from=kenlm-build /code/kenlm/build/bin /code/kenlm/build/bin

# Tool to convert output graph for inference
RUN curl -L https://github.com/coqui-ai/STT/releases/download/v0.9.3/convert_graphdef_memmapped_format.linux.amd64.zip | funzip > convert_graphdef_memmapped_format && \
    chmod +x convert_graphdef_memmapped_format

# Pre-built native client tools
RUN LATEST_STABLE_RELEASE=$(curl "https://api.github.com/repos/coqui-ai/STT/releases/latest" | python -c 'import sys; import json; print(json.load(sys.stdin)["tag_name"])') \
 bash -c 'curl -L https://github.com/coqui-ai/STT/releases/download/${LATEST_STABLE_RELEASE}/native_client.tflite.Linux.tar.xz | tar -xJvf -'

# Install STT
# No need for the decoder since we did it earlier
# TensorFlow GPU should already be installed on the base image,
# and we don't want to break that
RUN DS_NODECODER=y DS_NOTENSORFLOW=y pip3 install --upgrade -e .

# Copy rest of the code and test training
COPY . /code
RUN ./bin/run-ldc93s1.sh && rm -rf ~/.local/share/stt