Add batch inventory for multiple open music directories

Constraint: Personal-use dataset preparation needs fast comparison across several local open-music corpora before ingestion
Rejected: Inspect each dataset directory manually one by one | Slows repeated train/eval setup and comparison
Confidence: high
Scope-risk: narrow
Directive: Use inspect-batch on real FMA and MTG-Jamendo folders before selecting training and held-out evaluation corpora
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 inspect-batch fma=tmp/open_music_demo_fma mtg_jamendo=tmp/open_music_demo_jamendo --eval-ratio 0.5 --query-duration 5.0
Not-tested: Real upstream corpus inventory on downloaded full-size open datasets

@@ -19,6 +19,11 @@ Optional pre-check:
 /usr/local/miniconda3/bin/python src/data/external_adapters.py inspect-local fma data/raw/fma_small_audio --eval-ratio 0.2 --query-duration 8.0
 /usr/local/miniconda3/bin/python src/data/external_adapters.py inspect-local fma data/raw/fma_small_audio --eval-ratio 0.2 --query-duration 8.0
 ```
 ```
 
 
+Batch pre-check across multiple candidate corpora:
+```bash
+/usr/local/miniconda3/bin/python src/data/external_adapters.py inspect-batch fma=data/raw/fma_small_audio mtg_jamendo=data/raw/mtg_jamendo_audio --eval-ratio 0.2 --query-duration 8.0
+```
+
 Then generate manifests:
 Then generate manifests:
 ```bash
 ```bash
 /usr/local/miniconda3/bin/python src/data/external_adapters.py prepare-local fma data/raw/fma_small_audio --output-root data/external_ingested --eval-ratio 0.2 --query-duration 8.0
 /usr/local/miniconda3/bin/python src/data/external_adapters.py prepare-local fma data/raw/fma_small_audio --output-root data/external_ingested --eval-ratio 0.2 --query-duration 8.0

@@ -194,6 +194,21 @@ def write_registry(output_path: str):
     return out
     return out
 
 
 
 
+def inspect_batch(pairs: List[str], eval_ratio: float, query_duration: float) -> Dict:
+    results = []
+    for pair in pairs:
+        dataset, input_dir = pair.split("=", 1)
+        if dataset not in ADAPTERS:
+            raise SystemExit(f"Unknown dataset adapter: {dataset}")
+        summary = ADAPTERS[dataset].inspect_local_audio(
+            Path(input_dir),
+            eval_ratio=eval_ratio,
+            query_duration=query_duration,
+        )
+        results.append(summary)
+    return {"datasets": results, "count": len(results)}
+
+
 def main():
 def main():
     parser = argparse.ArgumentParser()
     parser = argparse.ArgumentParser()
     sub = parser.add_subparsers(dest="cmd", required=True)
     sub = parser.add_subparsers(dest="cmd", required=True)
@@ -222,6 +237,11 @@ def main():
     p.add_argument("--eval-ratio", type=float, default=0.2)
     p.add_argument("--eval-ratio", type=float, default=0.2)
     p.add_argument("--query-duration", type=float, default=8.0)
     p.add_argument("--query-duration", type=float, default=8.0)
 
 
+    p = sub.add_parser("inspect-batch")
+    p.add_argument("pairs", nargs="+", help="dataset=input_dir")
+    p.add_argument("--eval-ratio", type=float, default=0.2)
+    p.add_argument("--query-duration", type=float, default=8.0)
+
     args = parser.parse_args()
     args = parser.parse_args()
     if args.cmd == "registry":
     if args.cmd == "registry":
         path = write_registry(args.output)
         path = write_registry(args.output)
@@ -248,6 +268,9 @@ def main():
             query_duration=args.query_duration,
             query_duration=args.query_duration,
         )
         )
         print(json.dumps(summary, indent=2, ensure_ascii=False))
         print(json.dumps(summary, indent=2, ensure_ascii=False))
+    elif args.cmd == "inspect-batch":
+        summary = inspect_batch(args.pairs, args.eval_ratio, args.query_duration)
+        print(json.dumps(summary, indent=2, ensure_ascii=False))
 
 
 
 
 if __name__ == "__main__":
 if __name__ == "__main__":

@@ -147,6 +147,26 @@
 - 现在真实 FMA / MTG-Jamendo 目录在导入前就能先做规模预估
 - 现在真实 FMA / MTG-Jamendo 目录在导入前就能先做规模预估
 - 这对个人使用下的快速数据准备非常有帮助
 - 这对个人使用下的快速数据准备非常有帮助
 
 
+### Stage: 多目录批量 inventory（inspect-batch）
+
+完成项：
+- 扩展 `src/data/external_adapters.py`
+- 新增 `inspect-batch`
+- 支持一次性检查多个开放数据目录，例如：
+  - `fma=<dir>`
+  - `mtg_jamendo=<dir>`
+
+验证结果：
+- `python -m py_compile src/data/external_adapters.py src/data/manifest_tools.py` 成功
+- `python src/data/external_adapters.py inspect-batch fma=tmp/open_music_demo_fma mtg_jamendo=tmp/open_music_demo_jamendo --eval-ratio 0.5 --query-duration 5.0` 成功
+- 返回：
+  - `count=2`
+  - 每个数据源均给出 `num_audio_files / eligible_query_files / recommended_train_queries / recommended_test_queries`
+
+结论：
+- 现在可以批量对比多个候选开放数据目录的可用规模
+- 这让后续接入真实 FMA / MTG-Jamendo / 其他音乐集更高效
+
 ## 2026-06-02
 ## 2026-06-02
 
 
 ### Stage: 文档补全 + ACR 最小可运行链路
 ### Stage: 文档补全 + ACR 最小可运行链路

@@ -146,6 +146,7 @@ CLI 入口：
 - 低层工具：`src/data/manifest_tools.py audio-dir-to-splits`
 - 低层工具：`src/data/manifest_tools.py audio-dir-to-splits`
 - 高层统一入口：`src/data/external_adapters.py prepare-local <dataset> <input_dir>`
 - 高层统一入口：`src/data/external_adapters.py prepare-local <dataset> <input_dir>`
 - 导入前预检查：`src/data/external_adapters.py inspect-local <dataset> <input_dir>`
 - 导入前预检查：`src/data/external_adapters.py inspect-local <dataset> <input_dir>`
+- 多目录批量预检查：`src/data/external_adapters.py inspect-batch fma=<dir> mtg_jamendo=<dir> ...`
 
 
 ## 5. 文字说明
 ## 5. 文字说明