refactor(docker): optimize Dockerfiles for multi-stage builds and production deployment

- Implement multi-stage builds for main and speech Dockerfiles
- Reduce image size by using slim base images
- Improve dependency installation with frozen lockfile and production flags
- Add resource constraints and healthcheck to speech service Dockerfile
- Enhance build caching and separation of build/runtime dependencies

docker/speech/Dockerfile

@@ -1,41 +1,58 @@
-FROM python:3.10-slim
+# Use Python slim image as builder
+FROM python:3.10-slim as builder
 
-# Install system dependencies
+# Install build dependencies
 RUN apt-get update && apt-get install -y \
     git \
     build-essential \
+    portaudio19-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create and activate virtual environment
+RUN python -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# Install Python dependencies with specific versions and CPU-only variants
+RUN pip install --no-cache-dir torch==2.1.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cpu
+RUN pip install --no-cache-dir faster-whisper==0.10.0 openwakeword==0.4.0 pyaudio==0.2.14 sounddevice==0.4.6
+
+# Create final image
+FROM python:3.10-slim
+
+# Copy virtual environment from builder
+COPY --from=builder /opt/venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# Install only runtime dependencies
+RUN apt-get update && apt-get install -y \
     portaudio19-dev \
     python3-pyaudio \
     && rm -rf /var/lib/apt/lists/*
 
-# Install fast-whisper and its dependencies
-RUN pip install --no-cache-dir torch torchaudio --index-url https://download.pytorch.org/whl/cpu
-RUN pip install --no-cache-dir faster-whisper
-
-# Install wake word detection
-RUN pip install --no-cache-dir openwakeword pyaudio sounddevice
-
-# Create directories
-RUN mkdir -p /models /audio
-
-# Download the base model by default
-# The model will be downloaded automatically when first used
-ENV ASR_MODEL=base.en
-ENV ASR_MODEL_PATH=/models
-
-# Create wake word model directory
-# Models will be downloaded automatically when first used
-RUN mkdir -p /models/wake_word
+# Create necessary directories
+RUN mkdir -p /models/wake_word /audio
 
+# Set working directory
 WORKDIR /app
 
 # Copy the wake word detection script
 COPY wake_word_detector.py .
 
 # Set environment variables
-ENV WHISPER_MODEL_PATH=/models
-ENV WAKEWORD_MODEL_PATH=/models/wake_word
-ENV PYTHONUNBUFFERED=1
+ENV WHISPER_MODEL_PATH=/models \
+    WAKEWORD_MODEL_PATH=/models/wake_word \
+    PYTHONUNBUFFERED=1 \
+    ASR_MODEL=base.en \
+    ASR_MODEL_PATH=/models
 
-# Run the wake word detection service
-CMD ["python", "wake_word_detector.py"] 
+# Add resource limits to Python
+ENV PYTHONMALLOC=malloc \
+    MALLOC_TRIM_THRESHOLD_=100000 \
+    PYTHONDEVMODE=1
+
+# Add healthcheck
+HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
+    CMD ps aux | grep '[p]ython' || exit 1
+
+# Run the wake word detection service with resource constraints
+CMD ["python", "-X", "faulthandler", "wake_word_detector.py"]