The pod images were built to satisfy imports, not to serve models. The audit
found coqui/XTTS-v2, every whisper.cpp model, diarization, speaker ID, SALAD
place recognition and Surya all unservable on a pod, and the OCR image shipping
no OCR engine at all.

Three of the eight 'isolated' stacks are not isolated any more. Surya, speechbrain
and pytorch_lightning all import cleanly beside torch 2.11 — their venvs were
decided against an older torch and never re-checked, exactly like audiocraft's
torch==2.1.0. They go straight into the ocr, stt and embeddings profiles: place
recognition, speaker ID and Surya now work for a few hundred MB instead of three
new images. Surya's subprocess engine is pointed at the pod's own interpreter,
since building a venv to hold what is already installed would only cost rental
time.

Five stacks genuinely cannot share, each verified rather than assumed:
pyannote (torchaudio.AudioMetaData is gone in 2.11), coqui-tts (a transformers
4.x symbol), CrisperWhisper (transformers 4.46 AND torch 2.5), NeMo (large, own
torch) and paddle (its own CUDA runtime). Each gets an image carrying a baked
venv, rather than 6 GB added to an image everyone pulls.

The `speaker` capability no longer points at the STT image: that was the assumption that it was
'the STT deps plus a bit', and it shipped nothing for diarization or voiceprints
at all.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0182DhR1eFNPQyGmDrpHbedY