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9d837ba 543fe9c 9d837ba b65c302 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | # Use a lightweight Python 3.11 base image
FROM python:3.11-slim
# Set environment variables
ENV PYTHONUNBUFFERED=1 \
PYTHONDONTWRITEBYTECODE=1 \
HF_HOME=/tmp/hf_home \
DEBIAN_FRONTEND=noninteractive
# Install system dependencies (ffmpeg is required by FunASR/torchaudio for webm/ogg audio decoding)
RUN apt-get update && \
apt-get install -y --no-install-recommends ffmpeg && \
rm -rf /var/lib/apt/lists/*
# Create a non-root user (Hugging Face requirement)
RUN useradd -m -u 1000 user
USER user
ENV PATH="/home/user/.local/bin:$PATH"
# Set working directory
WORKDIR /app
# Install dependencies first (to leverage Docker caching)
COPY --chown=user:user Requirements.txt /app/
RUN pip install --no-cache-dir --upgrade pip && \
pip install --no-cache-dir -r Requirements.txt
# Copy the rest of the application code
COPY --chown=user:user . /app/
# Expose port 7860 (Hugging Face requirement)
EXPOSE 7860
# Start the FastAPI application with 2 workers.
# Each worker loads ~1.5GB of ML models (SenseVoice + BERT + VAD).
# With 16GB RAM this is safe (~3GB total), and 2 workers provides real
# concurrency benefit for CPU-bound STT when multiple users transcribe simultaneously.
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860", "--workers", "2", "--timeout-keep-alive", "75"]
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