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Commit 69843933 69843933efecfbfff4cdcfcfb1b3eb0329384489 by cnb.bofCdSsphPA
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Add a FAISS-first local ACR workflow for music20 samples 

Constraint: local validation should stay lightweight and use /workspace sample files while production retrieval remains pgvector-backed
Rejected: making ChromaDB the default local backend | chromadb is not installed in the current environment and FAISS is already available
Confidence: high
Scope-risk: narrow
Directive: keep local dev workflows explicitly separated from production pgvector flows in docs and scripts
Tested: /usr/local/miniconda3/bin/python -m unittest discover -s acr-engine/tests -v; /usr/local/miniconda3/bin/python acr-engine/scripts/local_music20_acr.py --downloads-dir /workspace/downloads --song-limit 20 --backend faiss --output acr-engine/data/local_eval/music20_summary.json
Not-tested: chromadb backend execution without installation; live pgvector database execution path
1 parent 4806664b
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Showing 5 changed files with 1866 additions and 0 deletions
......@@ -94,6 +94,29 @@ cd acr-engine
python evaluate.py --data data/synthetic --model data/models/best_model.pt --index-prefix data/index/reference --split test --device cpu
```
## 本地 20 首歌流程与生产向量库约定
- 本地小样本（如 `/workspace/downloads` 的 20 首歌流程）优先使用 **FAISS**。
- 若后续本机安装了 `chromadb`，可作为可选对照后端，但不是默认依赖。
- 生产环境统一保留 **pgvector** 作为正式向量存储与检索底座。
本地 20 首歌流程：
```bash
cd acr-engine
/usr/local/miniconda3/bin/python scripts/local_music20_acr.py \
--downloads-dir /workspace/downloads \
--song-limit 20 \
--backend faiss \
--output data/local_eval/music20_summary.json
```
说明：
- `query_type=1/7/8/16` 会在一个汇总 JSON 里统一输出。
- 默认本地后端为 `faiss`。
- 若使用 `--backend chromadb` 且环境缺少 `chromadb`，脚本会明确报错提示。
- 生产向量化链路仍走 `scripts/export_manifest_to_pgvector_json.py` 与后续 pgvector 入库。
## 当前提升方向
- 更强合成混淆样本（confused / humming_like）
......
{
"backend": "faiss",
"purpose": "Local 20-song ACR sanity flow for development; production remains pgvector.",
"downloads_dir": "/workspace/downloads",
"song_limit": 20,
"duration_sec": 8.0,
"sr": 22050,
"evaluations": [
{
"query_type": 1,
"reference_type": 11,
"song_count": 20,
"file_count": 40,
"topk": 3,
"metrics": {
"top1": 1.0,
"top3": 1.0
},
"results": [
{
"song_id": "100",
"query_path": "/workspace/downloads/100/type_1/BQ0000095-1.mp3",
"reference_path": "/workspace/downloads/100/type_11/93dfdeb0-7da5-42a8-9c71-cf12af57dd191650256918.wav",
"rank": 1,
"candidates": [
{
"rank": 1,
"song_id": "100",
"score": 0.9999855160713196
},
{
"rank": 2,
"song_id": "116",
"score": 0.9527432918548584
},
{
"rank": 3,
"song_id": "103",
"score": 0.9517078399658203
}
]
},
{
"song_id": "101",
"query_path": "/workspace/downloads/101/type_1/7679087a-c16c-4d0f-851f-b10f61b2032d1650364264.mp3",
"reference_path": "/workspace/downloads/101/type_11/83c0c07f-4f96-4ff4-998c-58db910f3cfa1650256915.wav",
"rank": 1,
"candidates": [
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"score": 0.9999996423721313
},
{
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"song_id": "118",
"score": 0.9811710715293884
},
{
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"song_id": "116",
"score": 0.9800198078155518
}
]
},
{
"song_id": "102",
"query_path": "/workspace/downloads/102/type_1/8fd5d3e8-e214-4d07-a283-1c5f6ac061f21650376142.mp3",
"reference_path": "/workspace/downloads/102/type_11/43440ec5-70b4-4d50-8683-d3e41cad29411650256908.wav",
"rank": 1,
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"score": 0.9999971389770508
},
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"score": 0.9651333093643188
},
{
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"score": 0.9586168527603149
}
]
},
{
"song_id": "103",
"query_path": "/workspace/downloads/103/type_1/BQ0000092-1.mp3",
"reference_path": "/workspace/downloads/103/type_11/19876dbb-fffc-40f8-9530-9322c9ed77681650256912.wav",
"rank": 1,
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]
},
{
"song_id": "570615",
"query_path": "/workspace/downloads/570615/type_16/故事打烊 片段.mp3",
"reference_path": "/workspace/downloads/570615/type_11/8fe62b2057640446295f901d0fc8c668.wav",
"rank": 4,
"candidates": [
{
"rank": 1,
"song_id": "571113",
"score": 0.971631646156311
},
{
"rank": 2,
"song_id": "569738",
"score": 0.959520697593689
},
{
"rank": 3,
"song_id": "569748",
"score": 0.9524401426315308
}
]
},
{
"song_id": "570690",
"query_path": "/workspace/downloads/570690/type_16/ca8e0c234e560d89d98b52eefc50728b.mp3",
"reference_path": "/workspace/downloads/570690/type_11/456050c584f93f406a10bc67979482bb.wav",
"rank": 1,
"candidates": [
{
"rank": 1,
"song_id": "570690",
"score": 0.992263674736023
},
{
"rank": 2,
"song_id": "569681",
"score": 0.9891844987869263
},
{
"rank": 3,
"song_id": "571113",
"score": 0.9690903425216675
}
]
},
{
"song_id": "571113",
"query_path": "/workspace/downloads/571113/type_16/杨小壮-《痴情小丑》伯格DJ版-副歌.wav",
"reference_path": "/workspace/downloads/571113/type_11/4a568a7e0dbd2566b219af738e9fded9.wav",
"rank": 1,
"candidates": [
{
"rank": 1,
"song_id": "571113",
"score": 0.9814494848251343
},
{
"rank": 2,
"song_id": "569681",
"score": 0.9789079427719116
},
{
"rank": 3,
"song_id": "570345",
"score": 0.9653943777084351
}
]
}
]
}
]
}
\ No newline at end of file
#!/usr/bin/env /usr/local/miniconda3/bin/python
"""Run a FAISS-first local ACR eval on up to 20 songs from /workspace/downloads.
Purpose:
- keep small-sample validation inside acr-engine
- default to FAISS for local dev
- optionally allow ChromaDB when installed
- preserve pgvector as the production path (not used here)
"""
from __future__ import annotations
import argparse
import json
from pathlib import Path
from typing import Dict, List
import faiss
import librosa
import numpy as np
DEFAULT_DOWNLOADS = Path('/workspace/downloads')
DEFAULT_OUTPUT = Path('/root/vprecog/acr-engine/data/local_eval/music20_summary.json')
SUPPORTED_QUERY_TYPES = (1, 7, 8, 16)
REFERENCE_TYPE = 11
def parse_args() -> argparse.Namespace:
ap = argparse.ArgumentParser()
ap.add_argument('--downloads-dir', default=str(DEFAULT_DOWNLOADS))
ap.add_argument('--song-limit', type=int, default=20)
ap.add_argument('--duration', type=float, default=8.0)
ap.add_argument('--sr', type=int, default=22050)
ap.add_argument('--topk', type=int, default=3)
ap.add_argument('--backend', choices=['faiss', 'chromadb'], default='faiss')
ap.add_argument('--output', default=str(DEFAULT_OUTPUT))
return ap.parse_args()
def first_file(path: Path) -> Path | None:
files = sorted(p for p in path.iterdir() if p.is_file()) if path.exists() else []
return files[0] if files else None
def collect_pairs(downloads_dir: Path, song_limit: int, query_type: int) -> List[Dict[str, str]]:
pairs = []
for song_dir in sorted(p for p in downloads_dir.iterdir() if p.is_dir()):
ref = first_file(song_dir / f'type_{REFERENCE_TYPE}')
qry = first_file(song_dir / f'type_{query_type}')
if ref and qry:
pairs.append({
'song_id': song_dir.name,
'reference_path': str(ref),
'query_path': str(qry),
})
if len(pairs) >= song_limit:
break
return pairs
def load_audio(path: str, sr: int, duration: float) -> np.ndarray:
y, _ = librosa.load(path, sr=sr, mono=True, duration=duration)
target_len = int(sr * duration)
if len(y) < target_len:
y = np.pad(y, (0, target_len - len(y)))
else:
y = y[:target_len]
return y.astype(np.float32)
def embed_chroma(path: str, sr: int, duration: float) -> np.ndarray:
y = load_audio(path, sr=sr, duration=duration)
chroma = librosa.feature.chroma_stft(y=y, sr=sr, n_chroma=12)
feat = np.concatenate([chroma.mean(axis=1), chroma.std(axis=1)], axis=0).astype(np.float32)
norm = np.linalg.norm(feat)
if norm > 0:
feat = feat / norm
return feat
def run_faiss(ref_matrix: np.ndarray, qry_matrix: np.ndarray, topk: int):
index = faiss.IndexFlatIP(ref_matrix.shape[1])
index.add(ref_matrix)
return index.search(qry_matrix, topk)
def run_chromadb(ref_matrix: np.ndarray, qry_matrix: np.ndarray, topk: int):
try:
import chromadb # type: ignore
except Exception as exc: # pragma: no cover - env-dependent
raise SystemExit(f'ChromaDB backend requested but unavailable: {exc}')
client = chromadb.EphemeralClient()
collection = client.create_collection('music20_local_eval')
ref_ids = [str(i) for i in range(len(ref_matrix))]
collection.add(ids=ref_ids, embeddings=ref_matrix.tolist())
result = collection.query(query_embeddings=qry_matrix.tolist(), n_results=topk)
distances = np.asarray(result['distances'], dtype=np.float32)
idxs = np.asarray([[int(x) for x in row] for row in result['ids']], dtype=np.int32)
sims = 1.0 - distances
return sims, idxs
def evaluate_query_type(downloads_dir: Path, song_limit: int, query_type: int, sr: int, duration: float, topk: int, backend: str):
pairs = collect_pairs(downloads_dir, song_limit, query_type=query_type)
if not pairs:
return {
'query_type': query_type,
'reference_type': REFERENCE_TYPE,
'song_count': 0,
'file_count': 0,
'topk': topk,
'metrics': {'top1': 0.0, 'top3': 0.0},
'results': [],
'note': 'No matching query/reference pairs found.',
}
ref_vecs = [embed_chroma(item['reference_path'], sr, duration) for item in pairs]
qry_vecs = [embed_chroma(item['query_path'], sr, duration) for item in pairs]
ref_ids = [item['song_id'] for item in pairs]
ref_matrix = np.vstack(ref_vecs).astype(np.float32)
qry_matrix = np.vstack(qry_vecs).astype(np.float32)
if backend == 'faiss':
sims, idxs = run_faiss(ref_matrix, qry_matrix, topk)
else:
sims, idxs = run_chromadb(ref_matrix, qry_matrix, topk)
ranks = []
results = []
for i, item in enumerate(pairs):
candidates = []
rank = None
for j in range(topk):
ref_idx = int(idxs[i, j])
cand_song_id = ref_ids[ref_idx]
score = float(sims[i, j])
candidates.append({'rank': j + 1, 'song_id': cand_song_id, 'score': score})
if cand_song_id == item['song_id'] and rank is None:
rank = j + 1
if rank is None:
rank = topk + 1
ranks.append(rank)
results.append({
'song_id': item['song_id'],
'query_path': item['query_path'],
'reference_path': item['reference_path'],
'rank': rank,
'candidates': candidates,
})
top1 = sum(1 for r in ranks if r == 1) / len(ranks)
top3 = sum(1 for r in ranks if r <= min(3, topk)) / len(ranks)
return {
'query_type': query_type,
'reference_type': REFERENCE_TYPE,
'song_count': len(pairs),
'file_count': len(pairs) * 2,
'topk': topk,
'metrics': {'top1': top1, 'top3': top3},
'results': results,
}
def main() -> None:
args = parse_args()
downloads_dir = Path(args.downloads_dir)
out = Path(args.output)
out.parent.mkdir(parents=True, exist_ok=True)
summary = {
'backend': args.backend,
'purpose': 'Local 20-song ACR sanity flow for development; production remains pgvector.',
'downloads_dir': str(downloads_dir),
'song_limit': args.song_limit,
'duration_sec': args.duration,
'sr': args.sr,
'evaluations': [],
}
for query_type in SUPPORTED_QUERY_TYPES:
summary['evaluations'].append(
evaluate_query_type(downloads_dir, args.song_limit, query_type, args.sr, args.duration, args.topk, args.backend)
)
out.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding='utf-8')
print(json.dumps(summary, ensure_ascii=False, indent=2))
if __name__ == '__main__':
main()
import tempfile
import unittest
from pathlib import Path
from scripts.local_music20_acr import collect_pairs, first_file
class LocalMusic20AcrTests(unittest.TestCase):
def test_first_file_returns_none_for_missing_dir(self):
with tempfile.TemporaryDirectory() as tmp:
self.assertIsNone(first_file(Path(tmp) / 'missing'))
def test_collect_pairs_uses_reference_and_query_types(self):
with tempfile.TemporaryDirectory() as tmp:
root = Path(tmp)
song = root / '1'
(song / 'type_11').mkdir(parents=True)
(song / 'type_7').mkdir(parents=True)
((song / 'type_11') / 'ref.wav').write_bytes(b'r')
((song / 'type_7') / 'query.mp3').write_bytes(b'q')
pairs = collect_pairs(root, song_limit=20, query_type=7)
self.assertEqual(len(pairs), 1)
self.assertEqual(pairs[0]['song_id'], '1')
self.assertTrue(pairs[0]['reference_path'].endswith('ref.wav'))
self.assertTrue(pairs[0]['query_path'].endswith('query.mp3'))
if __name__ == '__main__':
unittest.main()
## 2026-06-03 20-song local ACR workflow in acr-engine
- 新增 `acr-engine/scripts/local_music20_acr.py`，在 `acr-engine` 内提供基于 `/workspace/downloads` 的本地 20 首歌 ACR 小样本流程。
- 本地流程默认优先使用 **FAISS**，`chromadb` 作为可选对照后端；生产环境继续保留 **pgvector**。
- 新增 `acr-engine/tests/test_local_music20_acr.py`，覆盖本地配对逻辑。
- 新增 `acr-engine/data/local_eval/music20_summary.json`，记录 `type_1/7/8/16 -> type_11` 的本地 20-song 汇总结果。
- 更新 `acr-engine/README.md`，明确“本地 FAISS / 可选 ChromaDB / 生产 pgvector”的分层约定。
Fresh evidence:
- `/usr/local/miniconda3/bin/python -m unittest discover -s acr-engine/tests -v` => `Ran 2 tests, OK`
- `/usr/local/miniconda3/bin/python acr-engine/scripts/local_music20_acr.py --downloads-dir /workspace/downloads --song-limit 20 --backend faiss --output acr-engine/data/local_eval/music20_summary.json` 跑通
- 汇总结果：
- `type_1 -> type_11`: `top1=1.0`, `top3=1.0`
- `type_7 -> type_11`: `top1=0.45`, `top3=0.65`
- `type_8 -> type_11`: `top1=0.4667`, `top3=0.7333`
- `type_16 -> type_11`: `top1=0.4167`, `top3=0.4167`
### Stage: production encoder freeze FAQ and rollout guidance
完成项：
......