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Commit 6a97ca13 6a97ca13f76962ceaccff8137f9496bd0a9821a3 by cnb.bofCdSsphPA
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Make the exact lane fail honestly before real audio is mounted 

Constraint: the Phase-1 exact lane must not pretend success when reference audio is unreadable, and repeated writes must be idempotent at the database boundary.
Rejected: keep partial-success writes in completed state | rejected because it would blur asset-readability failures and weaken auditability.
Confidence: high
Scope-risk: moderate
Directive: preserve the repo-local chromaprint-style wording and the all-or-nothing failure semantics until production audio mounts and real extractor validation are in place.
Tested: py_compile for chromaprint matcher and chromaprint worker; live PostgreSQL unique index creation on acr_test; non-dry-run chromaprint worker attempt with job_status=failed and failure_reason=unreadable_audio_assets; bootstrap reset back to pending; architect review APPROVED.
Not-tested: successful audio_fingerprint writes against mounted production audio, semantic worker real writes, large-scale concurrent exact-lane execution.
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Showing 9 changed files with 440 additions and 12 deletions
1 {
2 "worker": "run_chromaprint_job",
3 "schema": "acr_test",
4 "job": {
5 "extraction_job_id": 1,
6 "feature_set_id": 2,
7 "target_scope": "reference_set:phase1_hot_reference_v1",
8 "job_status": "pending",
9 "shard_key": "phase1/reference/chromaprint/v1",
10 "job_metadata": {
11 "lane": "exact",
12 "phase": "phase1",
13 "priority": "p0"
14 },
15 "feature_name": "fingerprint_asset",
16 "feature_level": "asset",
17 "extraction_granularity": "full_asset",
18 "window_sec": 5.0,
19 "hop_sec": 2.5,
20 "embedding_dim": null,
21 "distance_metric": "hamming",
22 "feature_config": {
23 "lane": "exact",
24 "index_target": "audio_fingerprint"
25 },
26 "model_id": 2,
27 "model_name": "chromaprint",
28 "model_version": "v1",
29 "model_family": "fingerprint",
30 "input_sample_rate": 16000,
31 "output_embedding_dim": null,
32 "model_metadata": {
33 "lane": "exact",
34 "note": "exact fingerprint lane baseline",
35 "phase": "phase1"
36 }
37 },
38 "target_scope_summary": {
39 "scope_type": "reference_set",
40 "scope_value": "phase1_hot_reference_v1",
41 "reference_set_id": 2,
42 "reference_set_name": "phase1_hot_reference_v1",
43 "recording_count": 20,
44 "ready_asset_count": 20,
45 "active_window_count": 20
46 },
47 "scope_asset_count": 20,
48 "processed_assets": [],
49 "missing_assets": [
50 {
51 "asset_id": 1,
52 "storage_uri": "/workspace/downloads/100/type_11/93dfdeb0-7da5-42a8-9c71-cf12af57dd191650256918.wav",
53 "reason": "missing_audio"
54 },
55 {
56 "asset_id": 2,
57 "storage_uri": "/workspace/downloads/101/type_11/83c0c07f-4f96-4ff4-998c-58db910f3cfa1650256915.wav",
58 "reason": "missing_audio"
59 },
60 {
61 "asset_id": 3,
62 "storage_uri": "/workspace/downloads/102/type_11/43440ec5-70b4-4d50-8683-d3e41cad29411650256908.wav",
63 "reason": "missing_audio"
64 },
65 {
66 "asset_id": 4,
67 "storage_uri": "/workspace/downloads/103/type_11/19876dbb-fffc-40f8-9530-9322c9ed77681650256912.wav",
68 "reason": "missing_audio"
69 },
70 {
71 "asset_id": 5,
72 "storage_uri": "/workspace/downloads/104/type_11/4c1d3e22-045f-445b-ab87-ba1ae3ee09b31650256912.wav",
73 "reason": "missing_audio"
74 },
75 {
76 "asset_id": 6,
77 "storage_uri": "/workspace/downloads/105/type_11/57e61cde-4410-4751-93e9-d7a4ecece5791650256910.wav",
78 "reason": "missing_audio"
79 },
80 {
81 "asset_id": 7,
82 "storage_uri": "/workspace/downloads/106/type_11/bf61426c-67b7-4cf1-a9e7-f78cf519a0021650256910.wav",
83 "reason": "missing_audio"
84 },
85 {
86 "asset_id": 8,
87 "storage_uri": "/workspace/downloads/107/type_11/296bbc25-617c-4368-9a69-357aeec394381650256910.wav",
88 "reason": "missing_audio"
89 },
90 {
91 "asset_id": 9,
92 "storage_uri": "/workspace/downloads/108/type_11/d7e28fe6-4ad6-4243-b66b-d90ff5ca1e491650256909.wav",
93 "reason": "missing_audio"
94 },
95 {
96 "asset_id": 10,
97 "storage_uri": "/workspace/downloads/109/type_11/84acef9b-2a74-44bc-9eff-5ca7969ac9b61650256909.wav",
98 "reason": "missing_audio"
99 },
100 {
101 "asset_id": 11,
102 "storage_uri": "/workspace/downloads/110/type_11/2197b39e-23e2-4a66-b07e-dd672eab214a1650256908.wav",
103 "reason": "missing_audio"
104 },
105 {
106 "asset_id": 12,
107 "storage_uri": "/workspace/downloads/111/type_11/7f5256e8-de5f-41c5-bf76-419e05df72d81650256908.wav",
108 "reason": "missing_audio"
109 },
110 {
111 "asset_id": 13,
112 "storage_uri": "/workspace/downloads/112/type_11/34acd523-3c01-443d-ac3d-4ad7b9e2246f1650256907.wav",
113 "reason": "missing_audio"
114 },
115 {
116 "asset_id": 14,
117 "storage_uri": "/workspace/downloads/113/type_11/6d9438af-5d83-434b-bb20-76e28d0bbc4e1650256907.wav",
118 "reason": "missing_audio"
119 },
120 {
121 "asset_id": 15,
122 "storage_uri": "/workspace/downloads/114/type_11/0238ecbf-b234-470e-82e4-f3b80a267d771650256906.wav",
123 "reason": "missing_audio"
124 },
125 {
126 "asset_id": 16,
127 "storage_uri": "/workspace/downloads/115/type_11/aabad0ff-13de-4786-aa9c-40e1f957ed9f1650256906.wav",
128 "reason": "missing_audio"
129 },
130 {
131 "asset_id": 17,
132 "storage_uri": "/workspace/downloads/116/type_11/da34f6ff-39e7-4dde-8265-e1bb01b6263e1650256901.wav",
133 "reason": "missing_audio"
134 },
135 {
136 "asset_id": 18,
137 "storage_uri": "/workspace/downloads/117/type_11/1e1599e6-ebbd-4ceb-a81d-a320331ef6e31650256901.wav",
138 "reason": "missing_audio"
139 },
140 {
141 "asset_id": 19,
142 "storage_uri": "/workspace/downloads/118/type_11/db64461e-d752-4cf3-ab1d-56ff9232823d1650256901.wav",
143 "reason": "missing_audio"
144 },
145 {
146 "asset_id": 20,
147 "storage_uri": "/workspace/downloads/119/type_11/180dfa7d-836a-449c-990f-a3bf39c11da11650256898.wav",
148 "reason": "missing_audio"
149 }
150 ],
151 "status_after_start": {
152 "extraction_job_id": 1,
153 "job_status": "running",
154 "input_count": 20,
155 "output_count": null,
156 "started_at": "2026-06-04T13:35:22.194865+08:00",
157 "finished_at": null,
158 "log_uri": null,
159 "metadata_json": {
160 "lane": "exact",
161 "phase": "phase1",
162 "worker": "run_chromaprint_job",
163 "dry_run": false,
164 "priority": "p0",
165 "output_target": "audio_fingerprint",
166 "execution_mode": "write_attempt",
167 "target_scope_summary": {
168 "scope_type": "reference_set",
169 "scope_value": "phase1_hot_reference_v1",
170 "recording_count": 20,
171 "reference_set_id": 2,
172 "ready_asset_count": 20,
173 "reference_set_name": "phase1_hot_reference_v1",
174 "active_window_count": 20
175 }
176 }
177 },
178 "status_after_complete": null,
179 "status_after_failed": {
180 "extraction_job_id": 1,
181 "job_status": "failed",
182 "input_count": 20,
183 "output_count": 0,
184 "started_at": "2026-06-04T13:35:22.194865+08:00",
185 "finished_at": "2026-06-04T13:35:22.195659+08:00",
186 "log_uri": null,
187 "metadata_json": {
188 "lane": "exact",
189 "phase": "phase1",
190 "worker": "run_chromaprint_job",
191 "dry_run": false,
192 "priority": "p0",
193 "artifact_dir": "/workspace/acr-engine/data/pgvector_eval/music20/phase1_fingerprints",
194 "output_target": "audio_fingerprint",
195 "execution_mode": "write_attempt",
196 "failure_reason": "unreadable_audio_assets",
197 "write_target_table": "audio_fingerprint",
198 "missing_asset_count": 20,
199 "target_scope_summary": {
200 "scope_type": "reference_set",
201 "scope_value": "phase1_hot_reference_v1",
202 "recording_count": 20,
203 "reference_set_id": 2,
204 "ready_asset_count": 20,
205 "reference_set_name": "phase1_hot_reference_v1",
206 "active_window_count": 20
207 },
208 "missing_asset_samples": [
209 {
210 "reason": "missing_audio",
211 "asset_id": 1,
212 "storage_uri": "/workspace/downloads/100/type_11/93dfdeb0-7da5-42a8-9c71-cf12af57dd191650256918.wav"
213 },
214 {
215 "reason": "missing_audio",
216 "asset_id": 2,
217 "storage_uri": "/workspace/downloads/101/type_11/83c0c07f-4f96-4ff4-998c-58db910f3cfa1650256915.wav"
218 },
219 {
220 "reason": "missing_audio",
221 "asset_id": 3,
222 "storage_uri": "/workspace/downloads/102/type_11/43440ec5-70b4-4d50-8683-d3e41cad29411650256908.wav"
223 },
224 {
225 "reason": "missing_audio",
226 "asset_id": 4,
227 "storage_uri": "/workspace/downloads/103/type_11/19876dbb-fffc-40f8-9530-9322c9ed77681650256912.wav"
228 },
229 {
230 "reason": "missing_audio",
231 "asset_id": 5,
232 "storage_uri": "/workspace/downloads/104/type_11/4c1d3e22-045f-445b-ab87-ba1ae3ee09b31650256912.wav"
233 }
234 ]
235 }
236 },
237 "next_write_target": "audio_fingerprint",
238 "notes": [
239 "dry-run preserves the verified planner -> job -> PostgreSQL state flow",
240 "non-dry-run now writes repo-local chromaprint-style hash artifacts plus audio_fingerprint rows when source audio is readable"
241 ]
242 }
...\ No newline at end of file ...\ No newline at end of file
1 {
2 "job_row": {
3 "extraction_job_id": 1,
4 "job_status": "failed",
5 "input_count": 20,
6 "output_count": 0,
7 "failure_reason": "unreadable_audio_assets"
8 },
9 "audio_fingerprint_count": 0
10 }
...\ No newline at end of file ...\ No newline at end of file
...@@ -222,6 +222,9 @@ CREATE TABLE IF NOT EXISTS audio_fingerprint ( ...@@ -222,6 +222,9 @@ CREATE TABLE IF NOT EXISTS audio_fingerprint (
222 created_at TIMESTAMPTZ NOT NULL DEFAULT NOW() 222 created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
223 ); 223 );
224 224
225 CREATE UNIQUE INDEX IF NOT EXISTS uq_audio_fingerprint_feature_asset
226 ON audio_fingerprint(feature_set_id, asset_id);
227
225 CREATE TABLE IF NOT EXISTS reference_set_registry ( 228 CREATE TABLE IF NOT EXISTS reference_set_registry (
226 reference_set_id BIGSERIAL PRIMARY KEY, 229 reference_set_id BIGSERIAL PRIMARY KEY,
227 set_name TEXT NOT NULL UNIQUE, 230 set_name TEXT NOT NULL UNIQUE,
......
...@@ -8,7 +8,6 @@ Implements landmark-based audio fingerprinting: ...@@ -8,7 +8,6 @@ Implements landmark-based audio fingerprinting:
8 """ 8 """
9 9
10 import numpy as np 10 import numpy as np
11 import librosa
12 from numpy.lib.stride_tricks import sliding_window_view 11 from numpy.lib.stride_tricks import sliding_window_view
13 from collections import defaultdict 12 from collections import defaultdict
14 from typing import Dict, List, Tuple, Optional 13 from typing import Dict, List, Tuple, Optional
...@@ -16,6 +15,50 @@ import pickle ...@@ -16,6 +15,50 @@ import pickle
16 import json 15 import json
17 from pathlib import Path 16 from pathlib import Path
18 import time 17 import time
18 import wave
19
20 try:
21 import librosa # type: ignore
22 except ImportError: # pragma: no cover - optional dependency
23 librosa = None
24
25
26 def _resample_linear(y: np.ndarray, src_sr: int, target_sr: int) -> np.ndarray:
27 if src_sr == target_sr or y.size == 0:
28 return y.astype(np.float32, copy=False)
29 duration = y.shape[0] / float(src_sr)
30 target_len = max(int(round(duration * target_sr)), 1)
31 src_x = np.linspace(0.0, duration, num=y.shape[0], endpoint=False)
32 dst_x = np.linspace(0.0, duration, num=target_len, endpoint=False)
33 return np.interp(dst_x, src_x, y).astype(np.float32, copy=False)
34
35
36 def load_audio_mono(path: str, sr: int) -> tuple[np.ndarray, int]:
37 if librosa is not None:
38 y, _ = librosa.load(path, sr=sr, mono=True)
39 return y.astype(np.float32, copy=False), sr
40
41 with wave.open(path, 'rb') as wav_file:
42 src_sr = wav_file.getframerate()
43 channels = wav_file.getnchannels()
44 sample_width = wav_file.getsampwidth()
45 frame_count = wav_file.getnframes()
46 raw = wav_file.readframes(frame_count)
47
48 if sample_width == 1:
49 y = np.frombuffer(raw, dtype=np.uint8).astype(np.float32)
50 y = (y - 128.0) / 128.0
51 elif sample_width == 2:
52 y = np.frombuffer(raw, dtype=np.int16).astype(np.float32) / 32768.0
53 elif sample_width == 4:
54 y = np.frombuffer(raw, dtype=np.int32).astype(np.float32) / 2147483648.0
55 else:
56 raise ValueError(f'unsupported wav sample width: {sample_width}')
57
58 if channels > 1:
59 y = y.reshape(-1, channels).mean(axis=1)
60 y = _resample_linear(y, src_sr, sr)
61 return y, sr
19 62
20 63
21 class Fingerprint: 64 class Fingerprint:
...@@ -51,8 +94,19 @@ class ChromaprintMatcher: ...@@ -51,8 +94,19 @@ class ChromaprintMatcher:
51 return candidate 94 return candidate
52 95
53 def _spectrogram(self, y: np.ndarray) -> np.ndarray: 96 def _spectrogram(self, y: np.ndarray) -> np.ndarray:
54 S = np.abs(librosa.stft(y, n_fft=self.n_fft, hop_length=self.hop_length)) 97 if librosa is not None:
55 return S 98 return np.abs(librosa.stft(y, n_fft=self.n_fft, hop_length=self.hop_length))
99
100 if y.shape[0] < self.n_fft:
101 y = np.pad(y, (0, self.n_fft - y.shape[0]))
102 frame_count = 1 + max((y.shape[0] - self.n_fft) // self.hop_length, 0)
103 frames = np.stack(
104 [y[i * self.hop_length:i * self.hop_length + self.n_fft] for i in range(frame_count)],
105 axis=1,
106 )
107 window = np.hanning(self.n_fft).astype(np.float32)
108 frames = frames * window[:, None]
109 return np.abs(np.fft.rfft(frames, axis=0))
56 110
57 def _find_peaks(self, S: np.ndarray) -> List[Tuple[int, int, float]]: 111 def _find_peaks(self, S: np.ndarray) -> List[Tuple[int, int, float]]:
58 if S.shape[0] <= self.peak_neighborhood or S.shape[1] <= self.peak_neighborhood: 112 if S.shape[0] <= self.peak_neighborhood or S.shape[1] <= self.peak_neighborhood:
...@@ -82,12 +136,15 @@ class ChromaprintMatcher: ...@@ -82,12 +136,15 @@ class ChromaprintMatcher:
82 return hashes 136 return hashes
83 137
84 def index_song(self, song_id: str, y: np.ndarray): 138 def index_song(self, song_id: str, y: np.ndarray):
85 S = self._spectrogram(y) 139 hashes = self.extract_hashes(y)
86 peaks = self._find_peaks(S)
87 hashes = self._hash_peaks(peaks)
88 for h, offset in hashes: 140 for h, offset in hashes:
89 self.hash_db[h].append(Fingerprint(song_id, offset, h)) 141 self.hash_db[h].append(Fingerprint(song_id, offset, h))
90 142
143 def extract_hashes(self, y: np.ndarray) -> List[Tuple[int, int]]:
144 S = self._spectrogram(y)
145 peaks = self._find_peaks(S)
146 return self._hash_peaks(peaks)
147
91 def index_songs_from_dir( 148 def index_songs_from_dir(
92 self, 149 self,
93 songs_dir: str, 150 songs_dir: str,
...@@ -137,7 +194,7 @@ class ChromaprintMatcher: ...@@ -137,7 +194,7 @@ class ChromaprintMatcher:
137 continue 194 continue
138 song_id = item["song_id"] 195 song_id = item["song_id"]
139 try: 196 try:
140 y, _ = librosa.load(str(audio_path), sr=self.sr, mono=True) 197 y, _ = load_audio_mono(str(audio_path), sr=self.sr)
141 except Exception as exc: 198 except Exception as exc:
142 skipped_refs += 1 199 skipped_refs += 1
143 print( 200 print(
......
1 ## 2026-06-04 1 ## 2026-06-04
2 2
3 - 更新 `run_chromaprint_job.py` 与 `src/engines/chromaprint_matcher.py`，把 exact lane 从“只有 dry-run”推进到“具备真实 `audio_fingerprint` 写入路径”；同时增加无 `librosa` 环境下的 `wave + numpy` 回退实现，避免 worker 被运行时依赖直接卡死。
4 - 给 `audio_fingerprint` 补上 `(feature_set_id, asset_id)` 唯一索引，并把 exact lane 写入改成 `INSERT ... ON CONFLICT DO UPDATE`；同时把失败语义收紧为“全量成功 / 否则失败”，避免部分不可读资产被误标成 completed。
5 - 新增 `phase1_worker_chromaprint_write_attempt.json` 与 `phase1_worker_chromaprint_write_guard_report.json`，在 live PostgreSQL `acr_test` 上验证 exact lane 的非 dry-run 行为：当前因 `/workspace/downloads/...` 缺失导致 `scope_asset_count=20` 但 `processed_assets=0`，job 被明确标记为 `failed` 且 `failure_reason=unreadable_audio_assets`，证明写入路径已接上但受环境挂载阻塞。
3 - 新增 `bootstrap_phase1_reference_members_live.py` 与 `phase1_reference_member_bootstrap_report.json`，把 `acr_test` 中 `recording.is_reference=true` 的 20 条录音真实挂到 `phase1_hot_reference_v1`，使 worker dry-run 的 scope 从 `0` 提升为 `20 recordings / 20 assets / 20 windows`。 6 - 新增 `bootstrap_phase1_reference_members_live.py` 与 `phase1_reference_member_bootstrap_report.json`，把 `acr_test` 中 `recording.is_reference=true` 的 20 条录音真实挂到 `phase1_hot_reference_v1`，使 worker dry-run 的 scope 从 `0` 提升为 `20 recordings / 20 assets / 20 windows`。
4 - 根据 architect 复核修正 worker contract：`mark_job_status.py` 现支持真正的“CLI 覆盖 env”并限制状态白名单；`_job_common.update_job_status()` 新增前置状态约束并防止 `finished_at` 被重复覆盖；`bootstrap_phase1_extraction_jobs_live.py` 在恢复 pending 时会清空旧时间戳与计数；`run_embedding_job.py` 对 embedding job 契约做了更严格校验。 7 - 根据 architect 复核修正 worker contract：`mark_job_status.py` 现支持真正的“CLI 覆盖 env”并限制状态白名单；`_job_common.update_job_status()` 新增前置状态约束并防止 `finished_at` 被重复覆盖；`bootstrap_phase1_extraction_jobs_live.py` 在恢复 pending 时会清空旧时间戳与计数；`run_embedding_job.py` 对 embedding job 契约做了更严格校验。
5 - 修正 `plan_phase1_extraction_jobs_live.py`：新增 schema 校验，命令模板显式锚定 `cd /workspace/acr-engine &&`，并把 `--complete-dry-run` 与 `--expected-status pending` 带入生成的命令，避免 planner 产物“看起来能跑但实际上缺关键上下文/步骤”。 8 - 修正 `plan_phase1_extraction_jobs_live.py`：新增 schema 校验，命令模板显式锚定 `cd /workspace/acr-engine &&`，并把 `--complete-dry-run` 与 `--expected-status pending` 带入生成的命令，避免 planner 产物“看起来能跑但实际上缺关键上下文/步骤”。
......
...@@ -227,10 +227,62 @@ flowchart TD ...@@ -227,10 +227,62 @@ flowchart TD
227 后续把下面逻辑塞进 `run_chromaprint_job.py`： 227 后续把下面逻辑塞进 `run_chromaprint_job.py`：
228 228
229 1. 读取 `recording_asset` 229 1. 读取 `recording_asset`
230 2. 调 chromaprint CLI / library 230 2. 读取可用音频并提取 exact-lane hash
231 3. 写 `audio_fingerprint` 231 3. 写 artifact JSON
232 4. 更新 `output_count` 232 4. 写 `audio_fingerprint`
233 5. 标记 `completed` 233 5. 更新 `output_count`
234 6. 标记 `completed`
235
236 ### 当前 exact lane 的真实状态
237
238 这轮已经把 `run_chromaprint_job.py` 从“只有 dry-run”推进到：
239
240 - 如果 source audio 可读：
241 - 生成 repo-local chromaprint-style hash artifact
242 - 写入 `audio_fingerprint`
243 - 如果 source audio 不可读：
244 - 明确把 job 标记为 `failed`
245 - 把 `failure_reason`、`missing_asset_count`、`missing_asset_samples` 写回 PostgreSQL
246
247 ### 当前失败语义
248
249 当前 exact lane 采用的是 **全量成功 / 否则失败**：
250
251 - 只要 scope 内任意 asset：
252 - 缺文件
253 - 解码失败
254 - hash 提取失败
255
256 就整体标记：
257
258 - `job_status = failed`
259 - `failure_reason = unreadable_audio_assets`
260
261 这样不会把“部分成功”伪装成 `completed`。
262
263 ### 当前依赖策略
264
265 当前 exact lane 不再强依赖 `librosa`：
266
267 - 优先使用 `librosa`（如果环境里存在）
268 - 否则回退到：
269 - Python `wave`
270 - `numpy` 线性重采样
271 - `numpy` FFT spectrogram
272
273 这使得 worker contract 能在更瘦的运行环境里继续工作。
274
275 ### 当前幂等保护
276
277 `audio_fingerprint` 现在补了：
278
279 - `UNIQUE(feature_set_id, asset_id)`
280
281 对应 worker 写入改成：
282
283 - `INSERT ... ON CONFLICT DO UPDATE`
284
285 因此 exact lane 对同一 `(feature_set_id, asset_id)` 的重复写入不再依赖应用层先查再写。
234 286
235 ### 7.2 Embedding worker 287 ### 7.2 Embedding worker
236 288
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...@@ -378,6 +378,66 @@ flowchart TD ...@@ -378,6 +378,66 @@ flowchart TD
378 378
379 - 基础 claim guard 379 - 基础 claim guard
380 - 基础重复执行保护 380 - 基础重复执行保护
381
382 ---
383
384 ## exact lane 非 dry-run 写入尝试（新增）
385
386 这轮又继续向前推进了一步：
387
388 > `run_chromaprint_job.py` 已经不再只是 dry-run。
389
390 当前行为：
391
392 1. 如果 reference asset 对应音频文件可读：
393 - 提取 repo-local chromaprint-style hash
394 - 写 artifact JSON
395 - 写 `audio_fingerprint`
396 - job 标记为 `completed`
397
398 2. 如果 reference asset 对应音频文件不可读：
399 - job 标记为 `failed`
400 - 在 `metadata_json` 里写入：
401 - `failure_reason`
402 - `missing_asset_count`
403 - `missing_asset_samples`
404
405 ### 本轮 live 结果
406
407 报告：
408
409 - `acr-engine/data/pgvector_eval/music20/phase1_worker_chromaprint_write_attempt.json`
410 - `acr-engine/data/pgvector_eval/music20/phase1_worker_chromaprint_write_guard_report.json`
411
412 关键结果：
413
414 - `scope_asset_count = 20`
415 - `processed_assets = 0`
416 - `missing_assets = 20`
417 - `job_status = failed`
418 - `failure_reason = unreadable_audio_assets`
419 - `audio_fingerprint_count = 0`
420
421 ### 这说明什么
422
423 说明当前 exact lane 的 PostgreSQL worker contract 已经具备：
424
425 - 非 dry-run 的真实写入路径
426 - 明确的失败落盘
427 - 环境缺失时的可审计错误证据
428 - “全量成功 / 否则失败”的批次语义
429 - `audio_fingerprint(feature_set_id, asset_id)` 的原子 upsert 约束基础
430
431 但当前容器仍然缺：
432
433 - `/workspace/downloads/...` 实际音频文件
434
435 因此这轮证明的是：
436
437 - **worker 写入路径已经接上**
438 - **当前被环境数据挂载阻塞**
439
440 而不是 exact lane 逻辑本身还没落地。
381 - `type_7` 441 - `type_7`
382 442
383 因此： 443 因此：
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...@@ -191,10 +191,11 @@ sed -n '1,320p' acr-engine/sql/acr_pg_schema_v2.sql ...@@ -191,10 +191,11 @@ sed -n '1,320p' acr-engine/sql/acr_pg_schema_v2.sql
191 - 下一阶段已经不是“补 planner”，而是把 dry-run worker 替换为真实 extractor，并把 `audio_fingerprint / audio_embedding` 写入做成幂等执行 191 - 下一阶段已经不是“补 planner”，而是把 dry-run worker 替换为真实 extractor，并把 `audio_fingerprint / audio_embedding` 写入做成幂等执行
192 - `phase1_hot_reference_v1` 在 `acr_test` 里已经真实补齐 `20` 个 reference members，因此 worker dry-run 当前看到的 scope 已是 `20 recordings / 20 assets / 20 windows` 192 - `phase1_hot_reference_v1` 在 `acr_test` 里已经真实补齐 `20` 个 reference members，因此 worker dry-run 当前看到的 scope 已是 `20 recordings / 20 assets / 20 windows`
193 - worker contract 现在已有基础前置状态保护；重复执行同一 chromaprint dry-run job 会被 `expected_status=pending` 明确拒绝，证据见 `phase1_worker_double_claim_guard_report.json` 193 - worker contract 现在已有基础前置状态保护；重复执行同一 chromaprint dry-run job 会被 `expected_status=pending` 明确拒绝，证据见 `phase1_worker_double_claim_guard_report.json`
194 - exact lane 的 `run_chromaprint_job.py` 已具备非 dry-run 写入路径；当前在 `acr_test` 的 live 结果是因为 `/workspace/downloads/...` 缺失而明确 `failed`，不是继续假装 `completed`
194 195
195 ### 未验证 / 仍是缺口 196 ### 未验证 / 仍是缺口
196 - **未实际跑 MERT / MuQ encoder-only 特征抽取** 197 - **未实际跑 MERT / MuQ encoder-only 特征抽取**
197 - **worker 目前仍以 dry-run 为主，尚未写真实 `audio_fingerprint / audio_embedding`** 198 - **semantic / cover 等后续 lane 仍主要停留在 dry-run；exact lane 已接上真实 `audio_fingerprint` 写入路径，但当前容器缺 reference 音频挂载，live 结果仍停在可审计失败**
198 - **还未落更大规模的生产 reference set 真实业务数据（当前仅验证了 `acr_test` 下的 20-song live members）** 199 - **还未落更大规模的生产 reference set 真实业务数据（当前仅验证了 `acr_test` 下的 20-song live members）**
199 - **未定义最终线上分数融合细则** 200 - **未定义最终线上分数融合细则**
200 - **type_8 / type_16 还没有进入当前 live JSONL 的 PostgreSQL 实测链** 201 - **type_8 / type_16 还没有进入当前 live JSONL 的 PostgreSQL 实测链**
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