Building Multimodal AI Pipelines: Combining Image, Text, and Audio with Python and Transformers
This isn’t the only way to build multimodal systems. Early fusion (combining raw data) or cross-attention architectures can work better for some tasks, but they’re harder to scale when inputs arrive at different times—as they often do in real African deployments.
In production, I batch images (e.g., 16 at a time) to amortize memory overhead. This reduces per-image latency to ~12ms on a 4090.
Whisper’s Swahili support improves with larger models (e.g., whisper-medium), but at 3x the memory cost. For now, whisper-base is a pragmatic trade-off.
In Kenya, I validated this with 50 farmers in Kitale. The multimodal system detected 22% more pest/disease cases than image-only models.
For CPU-only deployment, replace SigLIP with `facebook/dinov2-small` (1.1GB) and whisper-base with `distil-whisper` (1.0GB). Expect ~20% drop in accuracy.