val.json
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Constraint: Personal-use experimentation needs a single entrypoint from local open-audio directories to train/eval manifests Rejected: Separate manual manifest generation per dataset | Too error-prone and slows iterative training/evaluation Confidence: high Scope-risk: narrow Directive: Point real FMA or MTG-Jamendo local download folders at prepare-local before expanding training runs Tested: /usr/local/miniconda3/bin/python -m py_compile src/data/external_adapters.py src/data/manifest_tools.py; /usr/local/miniconda3/bin/python src/data/external_adapters.py prepare-local fma tmp/open_music_demo --output-root data/external_ingested/demo_via_adapter --eval-ratio 0.5 --query-duration 5.0 Not-tested: Full upstream corpus import and large-scale training
cnb.bofCdSsphPA authored