Dockerfile

# Ankui Kazakh forced-alignment service — Hugging Face Docker Space.
FROM python:3.12-slim

RUN apt-get update && apt-get install -y --no-install-recommends ffmpeg && rm -rf /var/lib/apt/lists/*

# HF Spaces run the container as uid 1000.
RUN useradd -m -u 1000 user
ENV HOME=/home/user \
    HF_HOME=/home/user/hf \
    TORCH_HOME=/home/user/torch \
    ANKUI_ALIGN_HOST=0.0.0.0 \
    ANKUI_ALIGN_PORT=7860 \
    PYTHONUNBUFFERED=1

WORKDIR /home/user/app

# CPU-only torch — the default CUDA build is ~2 GB and useless on a CPU Space.
RUN pip install --no-cache-dir torch==2.8.0 torchaudio==2.8.0 \
        --index-url https://download.pytorch.org/whl/cpu
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy align.py + transcription helpers + bake models BEFORE serve.py so
# iterating on serve.py doesn't invalidate the (slow) model-download layer.
COPY align.py truecase.py kk_names.txt convert_kkturbo.py ./
RUN chown -R user:user /home/user
USER user

# kk-turbo Whisper lives in the image at a local path (converted here at
# build time — see convert_kkturbo.py — rather than uploaded pre-converted).
ENV ANKUI_KKTURBO_MODEL=/home/user/app/models/kk-turbo-ct2

# Bake all models into the image so a cold start (after the free Space
# sleeps) loads from disk instead of re-downloading:
#   - kk-turbo: convert the KSC2 Whisper fine-tune to CTranslate2/int8
#   - MMS-1b Kazakh aligner + HDEMUCS separator (forced alignment /align)
#   - validate the transcriber loads from the converted dir
RUN python convert_kkturbo.py && \
    python -c "import align; align.load_model('cpu'); align.load_separator('cpu'); align.load_transcriber('cpu')"

# structure.py is pure Python with no model dependencies, so it is copied here
# rather than above: editing it must not invalidate the (slow) model-bake layer.
COPY --chown=user serve.py structure.py ./

EXPOSE 7860
CMD ["python", "serve.py"]