Extend the data ingress path with bootstrap manifests for real datasets and capture an unsuccessful hard-case oversampling experiment so future iterations can avoid repeating the same weak strategy.

Constraint: Continuous optimization requires preserving negative results, not just successful ones
Rejected: Drop the oversampling attempt without record | would lose evidence and encourage redoing the same low-yield change
Confidence: high
Scope-risk: moderate
Directive: Next hard-case work should focus on melody-aware supervision and harder negatives instead of naive sample repetition
Tested: bootstrap manifest generation for FMA and CCMusic; 2-epoch CPU training for models_v4; index_v4 build; fast eval JSON generation for smoke-v4
Not-tested: whitelisted real audio ingestion beyond placeholder manifests; full melody-aware slow-eval on models_v4

cnb.bofCdSsphPA authored 2026-06-02 12:11:02 +0800