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Commit 2898ef26 2898ef26ef8c15a9eacad47c63db03ff172ed1b3 by cnb.bofCdSsphPA
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Add the song_id pgvector evaluation scaffolding 

Constraint: we need a song-level evaluation path that matches the future pgvector production shape before moving off the local FAISS proving lane
Rejected: jumping straight to a live pgvector-only implementation | we still need a reproducible repo-local evaluation harness and artifact trail first
Confidence: high
Scope-risk: moderate
Directive: keep future pgvector work song_id-first and measure each query type separately before aggregating product claims
Tested: /usr/local/miniconda3/bin/python -m unittest discover -s acr-engine/tests -v; /usr/local/miniconda3/bin/python acr-engine/scripts/export_workspace_music20_embeddings_jsonl.py --downloads-dir /workspace/downloads --song-limit 20 --out-dir acr-engine/data/pgvector_eval/music20; /usr/local/miniconda3/bin/python acr-engine/scripts/evaluate_songid_pgvector_path.py --reference-embeddings-jsonl acr-engine/data/pgvector_eval/music20/reference_embeddings.jsonl --query-embeddings-jsonl acr-engine/data/pgvector_eval/music20/query_embeddings.jsonl --output acr-engine/data/pgvector_eval/music20/songid_eval_report.json
Not-tested: live PostgreSQL/pgvector online retrieval path
1 parent a0ceb991
Inline Side-by-side
Showing 9 changed files with 475 additions and 0 deletions
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7 {"song_id": "106", "audio_path": "/workspace/downloads/106/type_11/bf61426c-67b7-4cf1-a9e7-f78cf519a0021650256910.wav", "type": "reference", "embedding": [0.14020437002182007, 0.17636266350746155, 0.12965762615203857, 0.25691142678260803, 0.10978095233440399, 0.2533515393733978, 0.21380609273910522, 0.11107411980628967, 0.2536095976829529, 0.10436597466468811, 0.209770068526268, 0.19501900672912598, 0.1669294834136963, 0.2681954503059387, 0.11313167214393616, 0.28466466069221497, 0.10289677232503891, 0.27697089314460754, 0.26080024242401123, 0.08941096067428589, 0.30462178587913513, 0.07827121764421463, 0.2455075979232788, 0.24239546060562134]}
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9 {"song_id": "108", "audio_path": "/workspace/downloads/108/type_11/d7e28fe6-4ad6-4243-b66b-d90ff5ca1e491650256909.wav", "type": "reference", "embedding": [0.2387375384569168, 0.4361359775066376, 0.2251766175031662, 0.13100668787956238, 0.1407063752412796, 0.20391406118869781, 0.28039926290512085, 0.25538650155067444, 0.36026281118392944, 0.17519590258598328, 0.17512458562850952, 0.1432817578315735, 0.12832649052143097, 0.16832631826400757, 0.10479722172021866, 0.12569355964660645, 0.1445886343717575, 0.15490975975990295, 0.18144796788692474, 0.16444800794124603, 0.2054930180311203, 0.13935047388076782, 0.1285625696182251, 0.11571376770734787]}
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12 {"song_id": "111", "audio_path": "/workspace/downloads/111/type_11/7f5256e8-de5f-41c5-bf76-419e05df72d81650256908.wav", "type": "reference", "embedding": [0.12701648473739624, 0.14027750492095947, 0.15660777688026428, 0.12502633035182953, 0.14248353242874146, 0.20542755722999573, 0.5798488855361938, 0.21973171830177307, 0.17014208436012268, 0.1961366981267929, 0.14795930683612823, 0.13763731718063354, 0.17900384962558746, 0.18741770088672638, 0.20532628893852234, 0.18452343344688416, 0.18948116898536682, 0.14547105133533478, 0.15455086529254913, 0.14613166451454163, 0.17468073964118958, 0.19636474549770355, 0.15917304158210754, 0.16934967041015625]}
13 {"song_id": "112", "audio_path": "/workspace/downloads/112/type_11/34acd523-3c01-443d-ac3d-4ad7b9e2246f1650256907.wav", "type": "reference", "embedding": [0.3072916567325592, 0.19724711775779724, 0.1906289905309677, 0.12292812764644623, 0.12594938278198242, 0.08180926740169525, 0.14846383035182953, 0.4079470634460449, 0.20003780722618103, 0.12660852074623108, 0.13523519039154053, 0.2503071427345276, 0.2655021846294403, 0.1797436624765396, 0.23180760443210602, 0.18045704066753387, 0.19119608402252197, 0.15219107270240784, 0.13312289118766785, 0.24752527475357056, 0.14813284575939178, 0.18677233159542084, 0.17415298521518707, 0.22857306897640228]}
14 {"song_id": "113", "audio_path": "/workspace/downloads/113/type_11/6d9438af-5d83-434b-bb20-76e28d0bbc4e1650256907.wav", "type": "reference", "embedding": [0.35431715846061707, 0.23911245167255402, 0.24465620517730713, 0.20387834310531616, 0.23059263825416565, 0.4177981913089752, 0.1759462058544159, 0.11241994053125381, 0.11005843430757523, 0.12489016354084015, 0.1394224464893341, 0.18881598114967346, 0.17597010731697083, 0.17929288744926453, 0.23165741562843323, 0.18789054453372955, 0.16568264365196228, 0.2022501826286316, 0.13118518888950348, 0.14595478773117065, 0.15694060921669006, 0.15562653541564941, 0.16738726198673248, 0.16100086271762848]}
15 {"song_id": "114", "audio_path": "/workspace/downloads/114/type_11/0238ecbf-b234-470e-82e4-f3b80a267d771650256906.wav", "type": "reference", "embedding": [0.12651732563972473, 0.10027110576629639, 0.12802469730377197, 0.18769653141498566, 0.20862409472465515, 0.32468947768211365, 0.2312006801366806, 0.14912617206573486, 0.1176583394408226, 0.22750897705554962, 0.2709246277809143, 0.13282042741775513, 0.20911023020744324, 0.17754167318344116, 0.17644557356834412, 0.21608977019786835, 0.20053324103355408, 0.27627646923065186, 0.22318944334983826, 0.2016165405511856, 0.16197042167186737, 0.23252806067466736, 0.2684091031551361, 0.16913236677646637]}
16 {"song_id": "115", "audio_path": "/workspace/downloads/115/type_11/aabad0ff-13de-4786-aa9c-40e1f957ed9f1650256906.wav", "type": "reference", "embedding": [0.179878368973732, 0.17446258664131165, 0.14604492485523224, 0.15842846035957336, 0.22699204087257385, 0.28520315885543823, 0.2951984405517578, 0.26090702414512634, 0.2814539968967438, 0.35812801122665405, 0.2827986478805542, 0.22442717850208282, 0.12899573147296906, 0.14319512248039246, 0.12575498223304749, 0.12688106298446655, 0.12607944011688232, 0.1565801352262497, 0.1662718802690506, 0.1592852622270584, 0.16216188669204712, 0.1832767277956009, 0.12944604456424713, 0.16046792268753052]}
17 {"song_id": "116", "audio_path": "/workspace/downloads/116/type_11/da34f6ff-39e7-4dde-8265-e1bb01b6263e1650256901.wav", "type": "reference", "embedding": [0.1917741894721985, 0.21905340254306793, 0.308736652135849, 0.21765963733196259, 0.2711302638053894, 0.20413215458393097, 0.24082490801811218, 0.1844777911901474, 0.1771651804447174, 0.1965189427137375, 0.21749338507652283, 0.21713195741176605, 0.1901472508907318, 0.17283162474632263, 0.19377116858959198, 0.1660938262939453, 0.1957506239414215, 0.16662529110908508, 0.1860773265361786, 0.17839395999908447, 0.1797271966934204, 0.18633370101451874, 0.18537984788417816, 0.1892005205154419]}
18 {"song_id": "117", "audio_path": "/workspace/downloads/117/type_11/1e1599e6-ebbd-4ceb-a81d-a320331ef6e31650256901.wav", "type": "reference", "embedding": [0.29365643858909607, 0.1854555308818817, 0.15902569890022278, 0.22892306745052338, 0.20183074474334717, 0.31933408975601196, 0.18667900562286377, 0.3031176030635834, 0.35480087995529175, 0.16361133754253387, 0.1261240839958191, 0.1636485904455185, 0.1925216019153595, 0.1636282354593277, 0.1861574947834015, 0.20228561758995056, 0.18324914574623108, 0.21258926391601562, 0.14629212021827698, 0.1574486792087555, 0.1288272738456726, 0.11544198542833328, 0.14687807857990265, 0.14499057829380035]}
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1 {
2 "backend": "faiss-as-pgvector-standin",
3 "note": "Uses song-level aggregation compatible with a future pgvector online path.",
4 "overall": {
5 "count": 22,
6 "top1": 0.909091,
7 "top3": 0.954545,
8 "top10": 0.954545,
9 "mrr": 0.934343,
10 "mean_rank": 1.8182,
11 "median_rank": 1.0
12 },
13 "by_query_type": {
14 "1": {
15 "count": 20,
16 "top1": 1.0,
17 "top3": 1.0,
18 "top10": 1.0,
19 "mrr": 1.0,
20 "mean_rank": 1.0,
21 "median_rank": 1.0
22 },
23 "7": {
24 "count": 2,
25 "top1": 0.0,
26 "top3": 0.5,
27 "top10": 0.5,
28 "mrr": 0.277778,
29 "mean_rank": 10.0,
30 "median_rank": 10.0
31 }
32 },
33 "examples": {
34 "1": [
35 {
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37 "rank": 1,
38 "top3": [
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41 0.9099869644641876,
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115 },
116 {
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118 "rank": 1,
119 "top3": [
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171 "7": [
172 {
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225 }
226 ]
227 }
228 }
...\ No newline at end of file ...\ No newline at end of file
1 #!/usr/bin/env /usr/local/miniconda3/bin/python
2 from __future__ import annotations
3
4 import argparse
5 import json
6 import sys
7 from collections import defaultdict
8 from pathlib import Path
9 from statistics import median
10
11 ROOT = Path(__file__).resolve().parents[1]
12 if str(ROOT) not in sys.path:
13 sys.path.insert(0, str(ROOT))
14
15 import faiss
16 import numpy as np
17
18
19 def load_jsonl(path: Path):
20 return [json.loads(line) for line in path.read_text(encoding='utf-8').splitlines() if line.strip()]
21
22
23 def aggregate_song_scores(song_ids, sims, idxs):
24 aggregated = defaultdict(list)
25 for score, idx in zip(sims, idxs):
26 aggregated[song_ids[int(idx)]].append(float(score))
27 ranked = []
28 for song_id, vals in aggregated.items():
29 vals.sort(reverse=True)
30 max_sim = vals[0]
31 top3_avg = sum(vals[:3]) / min(3, len(vals))
32 vote = len(vals)
33 combined = 0.6 * max_sim + 0.3 * top3_avg + 0.1 * min(vote / 10.0, 1.0)
34 ranked.append((song_id, combined, max_sim, top3_avg, vote))
35 ranked.sort(key=lambda x: x[1], reverse=True)
36 return ranked
37
38
39 def compute_metrics(ranks, topk):
40 if not ranks:
41 return {'count': 0}
42 return {
43 'count': len(ranks),
44 'top1': round(sum(1 for r in ranks if r == 1) / len(ranks), 6),
45 'top3': round(sum(1 for r in ranks if r <= 3) / len(ranks), 6),
46 f'top{topk}': round(sum(1 for r in ranks if r <= topk) / len(ranks), 6),
47 'mrr': round(sum(1.0 / r for r in ranks) / len(ranks), 6),
48 'mean_rank': round(sum(ranks) / len(ranks), 4),
49 'median_rank': median(ranks),
50 }
51
52
53 def main():
54 ap = argparse.ArgumentParser()
55 ap.add_argument('--reference-embeddings-jsonl', required=True)
56 ap.add_argument('--query-embeddings-jsonl', required=True)
57 ap.add_argument('--topn', type=int, default=20)
58 ap.add_argument('--topk', type=int, default=10)
59 ap.add_argument('--output', required=True)
60 args = ap.parse_args()
61
62 refs = load_jsonl(Path(args.reference_embeddings_jsonl))
63 queries = load_jsonl(Path(args.query_embeddings_jsonl))
64 ref_matrix = np.asarray([r['embedding'] for r in refs], dtype=np.float32)
65 song_ids = [r['song_id'] for r in refs]
66 index = faiss.IndexFlatIP(ref_matrix.shape[1])
67 index.add(ref_matrix)
68
69 by_type = defaultdict(list)
70 examples = defaultdict(list)
71 for q in queries:
72 qvec = np.asarray(q['embedding'], dtype=np.float32).reshape(1, -1)
73 sims, idxs = index.search(qvec, args.topn)
74 ranked = aggregate_song_scores(song_ids, sims[0], idxs[0])
75 gold = q['song_id']
76 rank = next((i + 1 for i, item in enumerate(ranked) if item[0] == gold), len(ranked) + 1)
77 qtype = str(q['query_type'])
78 by_type[qtype].append(rank)
79 if len(examples[qtype]) < 5:
80 examples[qtype].append({'song_id': gold, 'rank': rank, 'top3': ranked[:3]})
81
82 report = {
83 'backend': 'faiss-as-pgvector-standin',
84 'note': 'Uses song-level aggregation compatible with a future pgvector online path.',
85 'overall': compute_metrics([r for ranks in by_type.values() for r in ranks], args.topk),
86 'by_query_type': {qtype: compute_metrics(ranks, args.topk) for qtype, ranks in by_type.items()},
87 'examples': examples,
88 }
89 out = Path(args.output)
90 out.parent.mkdir(parents=True, exist_ok=True)
91 out.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding='utf-8')
92 print(json.dumps(report, ensure_ascii=False, indent=2))
93
94
95 if __name__ == '__main__':
96 main()
1 #!/usr/bin/env /usr/local/miniconda3/bin/python
2 from __future__ import annotations
3
4 import argparse
5 import json
6 import sys
7 from pathlib import Path
8
9 ROOT = Path(__file__).resolve().parents[1]
10 if str(ROOT) not in sys.path:
11 sys.path.insert(0, str(ROOT))
12
13 from scripts.local_music20_acr import REFERENCE_TYPE, SUPPORTED_QUERY_TYPES, embed_chroma, first_file
14
15
16 def parse_args():
17 ap = argparse.ArgumentParser()
18 ap.add_argument('--downloads-dir', default='/workspace/downloads')
19 ap.add_argument('--song-limit', type=int, default=20)
20 ap.add_argument('--duration', type=float, default=8.0)
21 ap.add_argument('--sr', type=int, default=22050)
22 ap.add_argument('--out-dir', default='data/pgvector_eval/music20')
23 return ap.parse_args()
24
25
26 def main():
27 args = parse_args()
28 downloads_dir = Path(args.downloads_dir)
29 out_dir = Path(args.out_dir)
30 out_dir.mkdir(parents=True, exist_ok=True)
31 ref_path = out_dir / 'reference_embeddings.jsonl'
32 qry_path = out_dir / 'query_embeddings.jsonl'
33 ref_f = ref_path.open('w', encoding='utf-8')
34 qry_f = qry_path.open('w', encoding='utf-8')
35 ref_count = qry_count = 0
36 refs_seen = set()
37 for song_dir in sorted(p for p in downloads_dir.iterdir() if p.is_dir()):
38 ref = first_file(song_dir / f'type_{REFERENCE_TYPE}')
39 if ref and song_dir.name not in refs_seen and len(refs_seen) < args.song_limit:
40 row = {
41 'song_id': song_dir.name,
42 'audio_path': str(ref),
43 'type': 'reference',
44 'embedding': embed_chroma(str(ref), args.sr, args.duration).tolist(),
45 }
46 ref_f.write(json.dumps(row, ensure_ascii=False) + '\n')
47 ref_count += 1
48 refs_seen.add(song_dir.name)
49 for query_type in SUPPORTED_QUERY_TYPES:
50 kept = 0
51 for song_id in sorted(refs_seen):
52 song_dir = downloads_dir / song_id
53 qry = first_file(song_dir / f'type_{query_type}')
54 if not qry:
55 continue
56 row = {
57 'song_id': song_id,
58 'audio_path': str(qry),
59 'query_type': query_type,
60 'embedding': embed_chroma(str(qry), args.sr, args.duration).tolist(),
61 }
62 qry_f.write(json.dumps(row, ensure_ascii=False) + '\n')
63 qry_count += 1
64 kept += 1
65 print(f'query_type={query_type} rows={kept}')
66 ref_f.close()
67 qry_f.close()
68 print(json.dumps({'reference_rows': ref_count, 'query_rows': qry_count, 'out_dir': str(out_dir.resolve())}, ensure_ascii=False, indent=2))
69
70
71 if __name__ == '__main__':
72 main()
1 import unittest
2
3 from scripts.evaluate_songid_pgvector_path import aggregate_song_scores, compute_metrics
4
5
6 class SongIdPgvectorPathTests(unittest.TestCase):
7 def test_aggregate_song_scores_ranks_by_combined_score(self):
8 song_ids = ['a', 'a', 'b', 'c']
9 sims = [0.9, 0.85, 0.95, 0.2]
10 idxs = [0, 1, 2, 3]
11 ranked = aggregate_song_scores(song_ids, sims, idxs)
12 self.assertEqual(ranked[0][0], 'b')
13 self.assertEqual(ranked[1][0], 'a')
14
15 def test_compute_metrics(self):
16 metrics = compute_metrics([1, 2, 4], 5)
17 self.assertEqual(metrics['count'], 3)
18 self.assertEqual(metrics['top1'], 0.333333)
19 self.assertEqual(metrics['top3'], 0.666667)
20 self.assertEqual(metrics['top5'], 1.0)
21
22
23 if __name__ == '__main__':
24 unittest.main()
1 - 新增 `acr-engine/scripts/export_workspace_music20_embeddings_jsonl.py` 与 `acr-engine/scripts/evaluate_songid_pgvector_path.py`，补齐 song_id 级 pgvector 评测脚手架。
2 - 新增 `acr-engine/data/pgvector_eval/music20/` 评测产物，当前 `faiss-as-pgvector-standin` 结果：整体 `top1=0.9091`、`top3=0.9545`；其中 `query_type=1` 很强（`top1=1.0`），`query_type=7` 仍明显偏弱（`top1=0.0`，`top3=0.5`）。
1 - 新增 `acr-engine/data/local_eval/voice_workspace20_type7_eval.json`，对当前 `workspace_music20` 语义做了 20 条 `type_7` 批量验证：`top1=0.0`、`top3=0.05`，说明业务 song_id 正确性仍明显不足。 3 - 新增 `acr-engine/data/local_eval/voice_workspace20_type7_eval.json`，对当前 `workspace_music20` 语义做了 20 条 `type_7` 批量验证：`top1=0.0`、`top3=0.05`，说明业务 song_id 正确性仍明显不足。
2 - 新增 `acr-engine/data/local_eval/voice_workspace20_type8_eval.json` 与 `voice_workspace20_type16_eval.json`，补充 business-corpus voice correctness 基线：`type_8 top1=0.0/top3=0.0`，`type_16 top1=0.0/top3=0.0`。 4 - 新增 `acr-engine/data/local_eval/voice_workspace20_type8_eval.json` 与 `voice_workspace20_type16_eval.json`，补充 business-corpus voice correctness 基线：`type_8 top1=0.0/top3=0.0`，`type_16 top1=0.0/top3=0.0`。
3 - architect review 当前结论：`APPROVED (WATCH)`，允许继续沿当前架构推进，但不能把当前 business-corpus 结果视作完成。 5 - architect review 当前结论：`APPROVED (WATCH)`，允许继续沿当前架构推进，但不能把当前 business-corpus 结果视作完成。
......
...@@ -51,3 +51,5 @@ flowchart TD ...@@ -51,3 +51,5 @@ flowchart TD
51 - handoff / changelog / docs README 已同步 51 - handoff / changelog / docs README 已同步
52 52
53 - handoff 已刷新：yes（已指向 voice service runtime 当前状态与下一步排查路径） 53 - handoff 已刷新：yes（已指向 voice service runtime 当前状态与下一步排查路径）
54
55 - business-corpus song_id baseline 已生成：yes（`data/pgvector_eval/music20/songid_eval_report.json`）
......
...@@ -46,6 +46,15 @@ ...@@ -46,6 +46,15 @@
46 3. 把哼唱评测集接入 `evaluate.py` 或独立评测脚本 46 3. 把哼唱评测集接入 `evaluate.py` 或独立评测脚本
47 4. 继续做 docs 第二轮收敛，只保留当前有效主文档 47 4. 继续做 docs 第二轮收敛，只保留当前有效主文档
48 48
49 - 已新增 song_id 级 pgvector 评测脚手架：
50 - `acr-engine/scripts/export_workspace_music20_embeddings_jsonl.py`
51 - `acr-engine/scripts/evaluate_songid_pgvector_path.py`
52 - 当前 `acr-engine/data/pgvector_eval/music20/songid_eval_report.json` 结果：
53 - overall: `top1=0.9091`, `top3=0.9545`
54 - `query_type=1`: `top1=1.0`, `top3=1.0`
55 - `query_type=7`: `top1=0.0`, `top3=0.5`
56 - 注意：当前 20-song 导出里 `query_type=8/16` 行数仍不足（0 行），说明下一步需要专门扩 business reference / query 选择，而不是只沿用当前前 20 首 reference。
57
49 - 当前 `workspace_music20` 业务正确性初测（`acr-engine/data/local_eval/voice_workspace20_type7_eval.json`）： 58 - 当前 `workspace_music20` 业务正确性初测（`acr-engine/data/local_eval/voice_workspace20_type7_eval.json`）：
50 - `num_queries=20` 59 - `num_queries=20`
51 - `top1=0.0` 60 - `top1=0.0`
......