Files
lidify/services/audio-analyzer/Dockerfile
Your Name cc8d0f6969 Release v1.3.0: Multi-source downloads, audio analyzer resilience, mobile improvements
Major Features:
- Multi-source download system (Soulseek/Lidarr with fallback)
- Configurable enrichment speed control (1-5x)
- Mobile touch drag support for seek sliders
- iOS PWA media controls (Control Center, Lock Screen)
- Artist name alias resolution via Last.fm
- Circuit breaker pattern for audio analysis

Critical Fixes:
- Audio analyzer stability (non-ASCII, BrokenProcessPool, OOM)
- Discovery system race conditions and import failures
- Radio decade categorization using originalYear
- LastFM API response normalization
- Mood bucket infinite loop prevention

Security:
- Bull Board admin authentication
- Lidarr webhook signature verification
- JWT token expiration and refresh
- Encryption key validation on startup

Closes #2, #6, #9, #13, #21, #26, #31, #34, #35, #37, #40, #43
2026-01-06 20:07:33 -06:00

90 lines
3.5 KiB
Docker

# Audio Analyzer Service - Essentia with TensorFlow (MusiCNN)
# Using Ubuntu 20.04 with Python 3.8 for essentia-tensorflow compatibility
FROM ubuntu:20.04
# Avoid interactive prompts
ENV DEBIAN_FRONTEND=noninteractive
ENV TZ=UTC
# CPU thread limiting for TensorFlow and numerical libraries
# Prevents each worker from using all CPU cores
# Override with docker-compose environment variables
ENV TF_NUM_INTRAOP_THREADS=1 \
TF_NUM_INTEROP_THREADS=1 \
OMP_NUM_THREADS=1 \
OPENBLAS_NUM_THREADS=1 \
MKL_NUM_THREADS=1 \
NUMEXPR_MAX_THREADS=1 \
THREADS_PER_WORKER=1
# Install system dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
python3 \
python3-dev \
python3-pip \
ffmpeg \
curl \
&& rm -rf /var/lib/apt/lists/*
# Upgrade pip
RUN pip3 install --upgrade pip setuptools wheel
WORKDIR /app
# Install essentia-tensorflow (includes TensorFlow + MusiCNN support)
RUN pip3 install --no-cache-dir essentia-tensorflow
# Verify TensorflowPredictMusiCNN is available
RUN python3 -c "from essentia.standard import TensorflowPredictMusiCNN, TensorflowPredict2D; print('MusiCNN and TensorflowPredict2D: OK')"
# Create models directory
RUN mkdir -p /app/models
# Download MusiCNN models from essentia.upf.edu/models/
# Base embedding model
RUN curl -L -o /app/models/msd-musicnn-1.pb \
"https://essentia.upf.edu/models/autotagging/msd/msd-musicnn-1.pb"
# Mood classification heads (using MusiCNN embeddings)
RUN curl -L -o /app/models/mood_happy-msd-musicnn-1.pb \
"https://essentia.upf.edu/models/classification-heads/mood_happy/mood_happy-msd-musicnn-1.pb" && \
curl -L -o /app/models/mood_sad-msd-musicnn-1.pb \
"https://essentia.upf.edu/models/classification-heads/mood_sad/mood_sad-msd-musicnn-1.pb" && \
curl -L -o /app/models/mood_relaxed-msd-musicnn-1.pb \
"https://essentia.upf.edu/models/classification-heads/mood_relaxed/mood_relaxed-msd-musicnn-1.pb" && \
curl -L -o /app/models/mood_aggressive-msd-musicnn-1.pb \
"https://essentia.upf.edu/models/classification-heads/mood_aggressive/mood_aggressive-msd-musicnn-1.pb" && \
curl -L -o /app/models/mood_party-msd-musicnn-1.pb \
"https://essentia.upf.edu/models/classification-heads/mood_party/mood_party-msd-musicnn-1.pb" && \
curl -L -o /app/models/mood_acoustic-msd-musicnn-1.pb \
"https://essentia.upf.edu/models/classification-heads/mood_acoustic/mood_acoustic-msd-musicnn-1.pb" && \
curl -L -o /app/models/mood_electronic-msd-musicnn-1.pb \
"https://essentia.upf.edu/models/classification-heads/mood_electronic/mood_electronic-msd-musicnn-1.pb"
# Other classification heads
RUN curl -L -o /app/models/danceability-msd-musicnn-1.pb \
"https://essentia.upf.edu/models/classification-heads/danceability/danceability-msd-musicnn-1.pb" && \
curl -L -o /app/models/voice_instrumental-msd-musicnn-1.pb \
"https://essentia.upf.edu/models/classification-heads/voice_instrumental/voice_instrumental-msd-musicnn-1.pb"
# Verify models downloaded
RUN echo "Models downloaded:" && ls -lh /app/models/
# Install other dependencies
RUN pip3 install --no-cache-dir redis psycopg2-binary sqlalchemy python-dotenv 'numpy>=1.19.0,<2.0.0'
# Copy application code
COPY analyzer.py .
# Create non-root user
RUN useradd -m -u 1000 analyzer && \
chown -R analyzer:analyzer /app
USER analyzer
# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=10s --retries=3 \
CMD python3 -c "from essentia.standard import TensorflowPredictMusiCNN; print('OK')" || exit 1
CMD ["python3", "analyzer.py"]