83 lines
5.2 KiB
Markdown
83 lines
5.2 KiB
Markdown
# Local model runtime & provisioning
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The pipeline runs entirely on local models. This documents what works and the version traps, so the runtime
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can be reproduced (reference environment: **x86_64 macOS, no GPU**; a Linux/GPU host is easier). Python venv:
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`./.venv-local-asset/bin/python` (py3.12). Nothing here downloads at service runtime — models load offline.
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## Models (each needs a provenance sidecar under `models/`)
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| Model | Role | Runtime | Notes | License |
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|---|---|---|---|---|
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| Piper `en_US-lessac-medium` | voiceover | `piper-tts` 1.5.0 + onnxruntime | real speech; CLI takes `--model`/`--output_file` | MIT / Blizzard dataset |
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| MusicGen small | music | `transformers` 4.44.2 | `facebook/musicgen-small`, CPU ~9× realtime, 32 kHz | **CC-BY-NC** |
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| AudioLDM2 | SFX | `diffusers==0.30.3` | `cvssp/audioldm2`, CPU ~7× realtime, **16 kHz → resample** | **CC-BY-NC-SA** |
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| moondream2 | Tier-2 vision director | `transformers` + `torchvision==0.17.2` | `vikhyatk/moondream2` rev `2024-08-26`, `trust_remote_code`, offline; ~25 s/frame CPU | Apache-2.0 |
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| YOLOv8n (optional CV) | visual analysis | `.venv-local-cv` (ultralytics) | `./yolov8n.pt` loads offline; runs behind loopback HTTP `:8091` | **AGPL-3.0** |
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## Version traps (x86_64 macOS — all real)
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- **PyTorch caps at `torch==2.2.2` / `torchaudio==2.2.2`** (last x86_64 macOS wheels). numpy must be **< 2**
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(pinned `numpy==1.26.4`, `numba==0.60.0`, `llvmlite==0.43.0`, `scipy==1.13.1`).
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- **transformers must be 4.x** (pinned `4.44.2`). transformers 5.x silently disables PyTorch (needs torch ≥ 2.4)
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→ models unavailable.
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- **audiocraft does NOT work here** (hard top-level `from xformers import ops`; xformers has no cp312 x86_64
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wheel/sdist). AudioGen is audiocraft-only → **SFX uses AudioLDM2 (diffusers)** instead. MusicGen runs via
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`transformers`, not audiocraft.
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- **HF downloads:** set `HF_HUB_DISABLE_XET=1` (the xet CDN times out on some networks). HF may **429** after
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many pulls — retry resumes from cache.
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## Wiring notes
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- `tools/local_asset_worker.py` synthesizes voice/music/SFX; `tools/vision_caption.py` runs moondream (batch,
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load-once) for the Tier-2 director.
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- `LocalAssetSynthesizer` requires each model path to be a licensed **regular file** (adjacent `.license.txt`,
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non-blank, not `UNTRACKED`) — `AssetLicensePolicy`.
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- Do **not** run `tools/run_local_cv_worker.sh` / `tools/run_local_asset_worker.sh` in a certified environment:
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their `auto` modes `pip install` and can fetch a named YOLO model.
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- **Licensing blocker for commercial use:** MusicGen (CC-BY-NC), AudioLDM2 (CC-BY-NC-SA) and YOLOv8 (AGPL) are
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non-commercial/copyleft. Swap in commercially-licensed models/assets before any commercial release. moondream2
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(Apache-2.0) and Piper are fine.
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## Optional stronger Tier-2 VLM: Qwen2.5-VL via llama.cpp (recommended upgrade)
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moondream2 is small and, on hard footage (distant, portrait), cannot reliably tell a highlight from an
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aftermath — which caps the director's vision **judge** (see `docs/cinematic-quality-rules.md` R15). A stronger
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local VLM is a **drop-in**: the director calls a captioner script with a fixed manifest→JSON contract, so only
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the configured script path changes. **`tools/vision_caption_llamacpp.py`** implements that contract against
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**llama.cpp** (GGUF) — which runs on this **x86 CPU via AVX SIMD** and **bypasses the torch==2.2.2 /
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transformers 4.x / no-xformers trap entirely** (no PyTorch involved).
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**Recommended model:** `Qwen2.5-VL-3B-Instruct` (Apache-2.0 — commercial-friendly; verify the model card),
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`Q4_K_M` GGUF + its `mmproj` vision projector. `Qwen3-VL-2B/4B` (official GGUF) or `Gemma 3 4B` are alternatives.
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Provision **offline** (on a networked machine, then copy the files over — nothing downloads at service runtime):
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```bash
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# 1) Build llama.cpp with the multimodal CLI (one-time, needs a compiler; NOT in a certified/offline env)
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git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp && cmake -B build && cmake --build build -j
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# -> produces build/bin/llama-mtmd-cli
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# 2) Fetch the GGUF weights + mmproj (e.g. from a bartowski/Mungert/official Qwen GGUF repo)
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# Qwen2.5-VL-3B-Instruct-Q4_K_M.gguf and mmproj-Qwen2.5-VL-3B-Instruct-f16.gguf
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# Record license/SHA-256 next to each file, per the asset-provenance policy.
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```
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Enable it (no code change — config + env only):
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```yaml
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# application-localpoc.yml (video-clipping.editing)
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vision-caption-script: ./tools/vision_caption_llamacpp.py
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```
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```bash
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export LLAMACPP_MTMD_BIN=/abs/llama.cpp/build/bin/llama-mtmd-cli
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export LLAMACPP_VLM_MODEL=/abs/models/qwen2.5-vl-3b/Qwen2.5-VL-3B-Instruct-Q4_K_M.gguf
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export LLAMACPP_VLM_MMPROJ=/abs/models/qwen2.5-vl-3b/mmproj-Qwen2.5-VL-3B-Instruct-f16.gguf
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# LLAMACPP_VLM_NTOKENS=64 (optional)
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```
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Notes: the worker invokes `llama-mtmd-cli` per frame (serverless, fully offline; the mmap'd model stays warm in
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the OS cache across frames). A persistent `llama-server` backend would be faster for large batches — a future
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optimisation, not required. It is **unverified whether Qwen2.5-VL breaks the specific bowling case** — it is
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substantially more capable than moondream, so it likely improves discrimination, but that is a hypothesis to
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test, not a guarantee.
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