Add external dataset bootstrap and record hard-case oversampling regression

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

+# ccmusic bootstrap
+
+- Fill raw audio files under `raw/`
+- Review license before training
+- Convert to final catalog/query manifests

+[
+  {
+    "song_id": "ccmusic_track_0000",
+    "audio_path": "raw/ccmusic_track_0000.wav",
+    "duration": 0.0,
+    "type": "reference",
+    "source_dataset": "ccmusic",
+    "license_status": "review_required"
+  },
+  {
+    "song_id": "ccmusic_track_0001",
+    "audio_path": "raw/ccmusic_track_0001.wav",
+    "duration": 0.0,
+    "type": "reference",
+    "source_dataset": "ccmusic",
+    "license_status": "review_required"
+  },
+  {
+    "song_id": "ccmusic_track_0002",
+    "audio_path": "raw/ccmusic_track_0002.wav",
+    "duration": 0.0,
+    "type": "reference",
+    "source_dataset": "ccmusic",
+    "license_status": "review_required"
+  }
+]
\ No newline at end of file

+# fma bootstrap
+
+- Fill raw audio files under `raw/`
+- Review license before training
+- Convert to final catalog/query manifests

+[
+  {
+    "song_id": "fma_track_0000",
+    "audio_path": "raw/fma_track_0000.wav",
+    "duration": 0.0,
+    "type": "reference",
+    "source_dataset": "fma",
+    "license_status": "review_required"
+  },
+  {
+    "song_id": "fma_track_0001",
+    "audio_path": "raw/fma_track_0001.wav",
+    "duration": 0.0,
+    "type": "reference",
+    "source_dataset": "fma",
+    "license_status": "review_required"
+  },
+  {
+    "song_id": "fma_track_0002",
+    "audio_path": "raw/fma_track_0002.wav",
+    "duration": 0.0,
+    "type": "reference",
+    "source_dataset": "fma",
+    "license_status": "review_required"
+  }
+]
\ No newline at end of file

+{
+  "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
+}
\ No newline at end of file

+{
+  "split": "test",
+  "num_queries": 20,
+  "top1": 0.4,
+  "topk": 0.8,
+  "by_type": {
+    "clean": {
+      "n": 8,
+      "top1": 0.75,
+      "topk": 1.0
+    },
+    "augmented": {
+      "n": 4,
+      "top1": 0.5,
+      "topk": 1.0
+    },
+    "humming_like": {
+      "n": 4,
+      "top1": 0.0,
+      "topk": 0.75
+    },
+    "confused": {
+      "n": 4,
+      "top1": 0.0,
+      "topk": 0.25
+    }
+  },
+  "hard_case_summary": {
+    "humming_like": {
+      "n": 4,
+      "top1": 0.0,
+      "topk": 0.75
+    },
+    "confused": {
+      "n": 4,
+      "top1": 0.0,
+      "topk": 0.25
+    }
+  },
+  "sample_failures": [
+    {
+      "truth": "song_0020",
+      "query": "segments/song_0020_seg_04_confused.wav",
+      "type": "confused",
+      "preds": [
+        "song_0010",
+        "song_0014",
+        "song_0012",
+        "song_0009",
+        "song_0005"
+      ]
+    },
+    {
+      "truth": "song_0021",
+      "query": "segments/song_0021_seg_04_confused.wav",
+      "type": "confused",
+      "preds": [
+        "song_0001",
+        "song_0022",
+        "song_0003",
+        "song_0023",
+        "song_0002"
+      ]
+    },
+    {
+      "truth": "song_0022",
+      "query": "segments/song_0022_seg_03_humming_like.wav",
+      "type": "humming_like",
+      "preds": [
+        "song_0007",
+        "song_0009",
+        "song_0021",
+        "song_0000",
+        "song_0002"
+      ]
+    },
+    {
+      "truth": "song_0023",
+      "query": "segments/song_0023_seg_04_confused.wav",
+      "type": "confused",
+      "preds": [
+        "song_0012",
+        "song_0002",
+        "song_0022",
+        "song_0000",
+        "song_0006"
+      ]
+    }
+  ]
+}
\ No newline at end of file

+#!/usr/bin/env python3
+"""Bootstrap manifest skeletons for whitelisted external datasets."""
+
+import argparse
+import json
+from pathlib import Path
+
+TEMPLATES = {
+    'fma': {'source_dataset': 'fma', 'license_status': 'review_required'},
+    'mtg_jamendo': {'source_dataset': 'mtg_jamendo', 'license_status': 'review_required'},
+    'ccmusic': {'source_dataset': 'ccmusic', 'license_status': 'review_required'},
+    'modelscope_music': {'source_dataset': 'modelscope_music', 'license_status': 'deny_until_whitelisted'},
+}
+
+
+def bootstrap(dataset: str, output_dir: str, num_placeholders: int = 3):
+    out = Path(output_dir)
+    out.mkdir(parents=True, exist_ok=True)
+    base = TEMPLATES[dataset]
+    rows = []
+    for i in range(num_placeholders):
+        rows.append({
+            'song_id': f'{dataset}_track_{i:04d}',
+            'audio_path': f'raw/{dataset}_track_{i:04d}.wav',
+            'duration': 0.0,
+            'type': 'reference',
+            **base,
+        })
+    (out / 'raw').mkdir(exist_ok=True)
+    (out / 'manifests').mkdir(exist_ok=True)
+    with open(out / 'manifests' / 'catalog.bootstrap.json', 'w') as f:
+        json.dump(rows, f, indent=2, ensure_ascii=False)
+    with open(out / 'README.bootstrap.md', 'w') as f:
+        f.write(f'# {dataset} bootstrap\n\n- Fill raw audio files under `raw/`\n- Review license before training\n- Convert to final catalog/query manifests\n')
+    return out / 'manifests' / 'catalog.bootstrap.json'
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('dataset', choices=sorted(TEMPLATES))
+    parser.add_argument('--output-dir', required=True)
+    parser.add_argument('--num-placeholders', type=int, default=3)
+    args = parser.parse_args()
+    path = bootstrap(args.dataset, args.output_dir, args.num_placeholders)
+    print(path)
+
+
+if __name__ == '__main__':
+    main()

@@ -191,10 +191,14 @@ class SongPairDataset(Dataset):
                 self.by_song.setdefault(item["song_id"], []).append(item)
                 self.by_song.setdefault(item["song_id"], []).append(item)
 
 
         self.song_ids = sorted(self.by_song)
         self.song_ids = sorted(self.by_song)
+        self.sample_song_ids = []
+        for sid, items in self.by_song.items():
+            weight = 3 if any(x.get("type") in {"confused", "humming_like"} for x in items) else 1
+            self.sample_song_ids.extend([sid] * weight)
         self.song_to_idx = {sid: i for i, sid in enumerate(self.song_ids)}
         self.song_to_idx = {sid: i for i, sid in enumerate(self.song_ids)}
 
 
     def __len__(self):
     def __len__(self):
-        return len(self.song_ids)
+        return len(self.sample_song_ids)
 
 
     def _load_clip(self, sample: Dict) -> np.ndarray:
     def _load_clip(self, sample: Dict) -> np.ndarray:
         path = self.data_dir / sample["audio_path"]
         path = self.data_dir / sample["audio_path"]
@@ -217,7 +221,7 @@ class SongPairDataset(Dataset):
         return torch.FloatTensor(mel)
         return torch.FloatTensor(mel)
 
 
     def __getitem__(self, idx):
     def __getitem__(self, idx):
-        song_id = self.song_ids[idx]
+        song_id = self.sample_song_ids[idx]
         choices = self.by_song[song_id]
         choices = self.by_song[song_id]
         if len(choices) == 1:
         if len(choices) == 1:
             a = b = choices[0]
             a = b = choices[0]

@@ -113,3 +113,26 @@
 - artifact generator 成功输出 4 类发布产物
 - artifact generator 成功输出 4 类发布产物
 - `reports/smoke-v2/synthetic_v2/` 目录产物存在性检查通过
 - `reports/smoke-v2/synthetic_v2/` 目录产物存在性检查通过
 - 当前 fast-eval 指标：top1=0.60, top5=0.75，hard-case 仍需继续优化
 - 当前 fast-eval 指标：top1=0.60, top5=0.75，hard-case 仍需继续优化
+
+## 2026-06-02
+
+### Stage: 外部数据集 bootstrap + hard-case 过采样试验
+
+完成项：
+- 新增 `src/data/bootstrap_external.py`
+- 可自动为 `fma` / `ccmusic` 生成 bootstrap catalog manifest
+- 在 `SongPairDataset` 中加入困难样本过采样试验（`confused` / `humming_like`）
+- 重新训练 `models_v4`、重建 `index_v4`、重跑 `smoke-v4` 评测
+
+验证结果：
+- `data/external_bootstrap/fma/manifests/catalog.bootstrap.json` 成功生成
+- `data/external_bootstrap/ccmusic/manifests/catalog.bootstrap.json` 成功生成
+- `reports/smoke-v4/synthetic_v2/eval.json` 成功生成
+- 当前试验结果：top1=0.40, top5=0.80
+- hard-case 结果未改善：
+  - humming_like top1=0.00
+  - confused top1=0.00
+
+结论：
+- 该轮简单过采样策略无效，且整体精度下降
+- 下一轮应改用更细粒度 hard-negative / melody-aware 正则，而不是继续放大样本重复权重