README.bootstrap.md
133 Bytes
ccmusic bootstrap
- Fill raw audio files under
raw/ - Review license before training
- Convert to final catalog/query manifests
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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