Dockerfile

# syntax=docker/dockerfile:1.7
#
# Multi-stage build:
#   1. ``converter``  – best-effort conversion of an optional Kazakh-fine-tuned
#      Whisper model from HuggingFace into CTranslate2 / faster-whisper
#      format. ``transformers`` and ``torch`` live ONLY in this stage.
#   2. ``runtime``    – installs only the API / ML deps, copies the converted
#      KK weights (if any), generates the seed dataset and trains the
#      ensemble classifier.
#
# The primary model is always vanilla ``openai/whisper-small`` (loaded by
# faster-whisper at startup, no conversion needed). The KK model is an
# optional academic fine-tune (``akuzdeuov/whisper-base.kk``, 15.36% WER on
# the 1000-hour Kazakh Speech Corpus 2). If KK conversion fails for any
# reason the runtime simply uses the primary model for Kazakh too.

# ---------------------------------------------------------------------------
# Stage 1 — convert the optional Kazakh fine-tune from HF to CT2
# ---------------------------------------------------------------------------
FROM python:3.11-slim AS converter

ENV PIP_NO_CACHE_DIR=1 \
    PIP_DISABLE_PIP_VERSION_CHECK=1 \
    HF_HOME=/cache/hf

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

# transformers + torch are needed by ct2-transformers-converter to load
# the HuggingFace weights before conversion. CPU torch keeps it small-ish.
RUN pip install \
        --index-url https://download.pytorch.org/whl/cpu \
        torch==2.4.1
# ctranslate2 4.6.0 is the only version in 4.x that:
#   - has the executable-stack fix (broken in 4.4 and 4.5)
#   - does NOT pass the 'dtype' kwarg incompatible with WhisperForConditionalGeneration
#     (introduced in 4.7+)
RUN pip install \
        transformers==4.45.2 \
        ctranslate2==4.6.0 \
        huggingface-hub==0.25.2 \
        sentencepiece==0.2.0 \
        safetensors==0.4.5

ARG WHISPER_KK_FINETUNE=akuzdeuov/whisper-base.kk

COPY ml_training/convert_whisper.py /tmp/convert_whisper.py

# Convert the KK fine-tune. Failures are tolerated — the runtime falls
# back to the primary vanilla model for Kazakh in that case.
RUN mkdir -p /converted && \
    case "$WHISPER_KK_FINETUNE" in \
        ""|"none"|"NONE") \
            echo ">>> WHISPER_KK_FINETUNE is empty — no KK fine-tune will be built."; \
            ;; \
        tiny|base|small|medium|large|large-v1|large-v2|large-v3) \
            echo ">>> WHISPER_KK_FINETUNE='$WHISPER_KK_FINETUNE' is a vanilla size, skipping conversion."; \
            ;; \
        *) \
            echo ">>> Converting '$WHISPER_KK_FINETUNE' to CTranslate2 ..."; \
            if python /tmp/convert_whisper.py \
                    --model "$WHISPER_KK_FINETUNE" \
                    --output /converted/kk \
                    --quantization int8; then \
                echo ">>> Conversion of '$WHISPER_KK_FINETUNE' SUCCEEDED."; \
            else \
                echo ">>> CONVERSION FAILED — KK will use primary vanilla model at runtime."; \
                rm -rf /converted/kk; \
            fi \
            ;; \
    esac && \
    echo "--- /converted ---" && ls -la /converted/ && \
    if [ -d /converted/kk ]; then echo "--- /converted/kk ---"; ls -la /converted/kk/; fi

# ---------------------------------------------------------------------------
# Stage 2 — runtime
# ---------------------------------------------------------------------------
FROM python:3.11-slim AS runtime

ENV PYTHONUNBUFFERED=1 \
    PYTHONDONTWRITEBYTECODE=1 \
    PYTHONUTF8=1 \
    PYTHONIOENCODING=utf-8 \
    PIP_NO_CACHE_DIR=1 \
    PIP_DISABLE_PIP_VERSION_CHECK=1 \
    OMP_NUM_THREADS=4 \
    HF_HOME=/app/.cache/hf \
    HF_HUB_DOWNLOAD_TIMEOUT=120

RUN apt-get update \
    && apt-get install -y --no-install-recommends libgomp1 ca-certificates curl \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /app

RUN pip install --upgrade pip setuptools wheel \
    && pip install \
        fastapi==0.115.0 \
        uvicorn[standard]==0.30.6 \
        pydantic==2.9.2 \
        pydantic-settings==2.5.2 \
        sqlalchemy==2.0.35 \
        python-multipart==0.0.12 \
        scikit-learn==1.5.2 \
        scipy==1.14.1 \
        joblib==1.4.2 \
        numpy==1.26.4 \
        pandas==2.2.3 \
        requests==2.32.3 \
        huggingface-hub==0.25.2 \
        faster-whisper==1.0.3

COPY app /app/app
COPY ml_training /app/ml_training

# Pull the converted Kazakh CT2 weights (if conversion succeeded). If not,
# /app/models/whisper-kk will be missing /kk subdirectory and the loader's
# fallback uses the primary model for Kazakh.
COPY --from=converter /converted /app/models/whisper-kk

RUN mkdir -p /app/models /app/data \
    && python -m ml_training.generate_dataset \
    && python -m ml_training.train

EXPOSE 8000

HEALTHCHECK --interval=30s --timeout=10s --start-period=120s --retries=3 \
  CMD curl -fs http://localhost:8000/v1/health || exit 1

CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "1"]