Perf: Optimiser builds backend avec image de base (90-95% plus rapide)

Architecture en 2 images:
- Image base (audio-classifier-base): deps système + Python (~15min, 1x/semaine)
- Image app (audio-classifier-backend): code uniquement (~30s-2min, chaque commit)

Fichiers ajoutés:
- backend/Dockerfile.base: Image de base avec toutes les dépendances
- .gitea/workflows/docker-base.yml: CI pour build de l'image de base
- backend/DOCKER_BUILD.md: Documentation complète

Fichiers modifiés:
- backend/Dockerfile: Utilise l'image de base (FROM audio-classifier-base)
- .gitea/workflows/docker.yml: Passe BASE_IMAGE en build-arg

Gains de performance:
- Build normal: 15-25min → 30s-2min (90-95% plus rapide)
- Trigger auto du build base: quand requirements.txt change
- Trigger manuel: via interface Gitea Actions

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

backend/Dockerfile.base

@@ -0,0 +1,59 @@
+# Base image for Audio Classifier Backend
+# This image contains all system dependencies and Python packages
+# Build this image only when dependencies change (requirements.txt updates)
+
+# Use amd64 platform for better Essentia compatibility
+FROM --platform=linux/amd64 python:3.9-slim
+
+LABEL maintainer="benoit.schw@gmail.com"
+LABEL description="Base image with all dependencies for Audio Classifier Backend"
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    ffmpeg \
+    libsndfile1 \
+    libsndfile1-dev \
+    gcc \
+    g++ \
+    gfortran \
+    libopenblas-dev \
+    liblapack-dev \
+    pkg-config \
+    curl \
+    build-essential \
+    libyaml-dev \
+    libfftw3-dev \
+    libavcodec-dev \
+    libavformat-dev \
+    libavutil-dev \
+    libswresample-dev \
+    libsamplerate0-dev \
+    libtag1-dev \
+    libchromaprint-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set working directory
+WORKDIR /app
+
+# Upgrade pip, setuptools, wheel
+RUN pip install --no-cache-dir --upgrade pip setuptools wheel
+
+# Copy requirements
+COPY requirements.txt .
+
+# Install Python dependencies in stages for better caching
+# Using versions compatible with Python 3.9
+RUN pip install --no-cache-dir numpy==1.24.3
+RUN pip install --no-cache-dir scipy==1.11.4
+
+# Install Essentia-TensorFlow - Python 3.9 AMD64 support
+RUN pip install --no-cache-dir essentia-tensorflow
+
+# Install remaining dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Verify installations
+RUN python -c "import essentia.standard; import numpy; import scipy; import fastapi; print('All dependencies installed successfully')"
+
+# This image is meant to be used as a base
+# The application code will be copied in the derived Dockerfile