acr-engine/data/models_v3/song_to_idx.json · 18ba8663fc9c2d5e8b96e97a658ddd8e55db2d85 · wanghai-tech / hikoon-ACR · GitLab

Connect real evaluation outputs to release artifacts · 1b812bea ...

Make the benchmark pipeline produce reusable release artifacts from actual evaluation results so model iterations can be tracked, reviewed, and shipped with evidence.

Constraint: Continuous training only helps if each stage emits durable reports and release metadata
Rejected: Keep artifact generation as a disconnected smoke utility | would block repeatable release discipline
Confidence: high
Scope-risk: moderate
Directive: Next iterations should improve hard-case metrics on real/whitelisted datasets and keep artifact generation on every training milestone
Tested: synthetic_v2 data regeneration; 2-epoch CPU training; index build; fast evaluation JSON export; artifact generation to reports/smoke-v2/synthetic_v2
Not-tested: full melody-aware slow evaluation as release default; real external dataset benchmark generation

authored 2026-06-02 12:08:20 +0800

song_to_idx.json

296 Bytes

Raw
Blame
History
Permalink

{
  "song_0000": 0,
  "song_0001": 1,
  "song_0002": 2,
  "song_0003": 3,
  "song_0004": 4,
  "song_0005": 5,
  "song_0006": 6,
  "song_0007": 7,
  "song_0008": 8,
  "song_0009": 9,
  "song_0010": 10,
  "song_0011": 11,
  "song_0012": 12,
  "song_0013": 13,
  "song_0014": 14,
  "song_0015": 15
}