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Commit 90e252b8 90e252b89770b0f4126cfff3e972cf3ad4e66d1f by cnb.bofCdSsphPA
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Reduce silent-query noise in training and open-dataset preparation 

Constraint: Real music queries often include long silence heads/tails, but the pipeline still needs random-crop generalization and simple CLI controls
Rejected: Replace all random crops with structure-aware segmentation | would overfit to curated boundaries and diverge from messy real-world query distributions
Confidence: high
Scope-risk: moderate
Directive: Keep random as fallback; layer beat/onset/chorus-aware segmentation on top instead of removing silence-aware and sliding paths
Tested: /usr/local/miniconda3/bin/python -m py_compile acr-engine/src/data/dataset.py acr-engine/src/data/manifest_tools.py acr-engine/train.py acr-engine/src/data/external_adapters.py; external_adapters.py prepare-local fma /tmp/segtest_audio --query-strategy silence_aware; train.py --data data/synthetic_v2 --dry-run --segment-strategy hybrid
Not-tested: Full FMA smoke retraining/eval with the new segmentation strategies
1 parent d61ee980
Inline Side-by-side
Showing 6 changed files with 260 additions and 6 deletions
...@@ -23,6 +23,8 @@ class ACRDataset(Dataset): ...@@ -23,6 +23,8 @@ class ACRDataset(Dataset):
23 n_crops_per_song: int = 4, 23 n_crops_per_song: int = 4,
24 song_to_idx: Optional[Dict[str, int]] = None, 24 song_to_idx: Optional[Dict[str, int]] = None,
25 references_only: bool = False, 25 references_only: bool = False,
26 segment_strategy: str = "random",
27 silence_top_db: int = 30,
26 ): 28 ):
27 self.sr = sr 29 self.sr = sr
28 self.n_mels = n_mels 30 self.n_mels = n_mels
...@@ -31,6 +33,8 @@ class ACRDataset(Dataset): ...@@ -31,6 +33,8 @@ class ACRDataset(Dataset):
31 self.segment_len = int(segment_dur * sr) 33 self.segment_len = int(segment_dur * sr)
32 self.augment = augment 34 self.augment = augment
33 self.n_crops = n_crops_per_song 35 self.n_crops = n_crops_per_song
36 self.segment_strategy = segment_strategy
37 self.silence_top_db = silence_top_db
34 self.data_dir = Path(data_dir) 38 self.data_dir = Path(data_dir)
35 self.asset_root = self.data_dir.parent if self.data_dir.name == "manifests" else self.data_dir 39 self.asset_root = self.data_dir.parent if self.data_dir.name == "manifests" else self.data_dir
36 40
...@@ -70,13 +74,52 @@ class ACRDataset(Dataset): ...@@ -70,13 +74,52 @@ class ACRDataset(Dataset):
70 ) 74 )
71 return librosa.power_to_db(mel, ref=np.max) 75 return librosa.power_to_db(mel, ref=np.max)
72 76
77 def _find_non_silent_intervals(self, y: np.ndarray) -> List[tuple[int, int]]:
78 intervals = librosa.effects.split(y, top_db=self.silence_top_db)
79 if intervals is None or len(intervals) == 0:
80 return [(0, len(y))]
81 return [(int(s), int(e)) for s, e in intervals]
82
83 def _choose_offset(self, sample: Dict, audio_path: Path) -> float:
84 duration = float(sample["duration"])
85 max_offset = max(0.0, duration - 5.0)
86 if max_offset <= 0:
87 return 0.0
88
89 if self.segment_strategy == "random":
90 return random.uniform(0, max_offset)
91
92 y, _ = librosa.load(str(audio_path), sr=self.sr, mono=True)
93 target_len = self.segment_len
94 intervals = self._find_non_silent_intervals(y)
95 valid_intervals = []
96 for start, end in intervals:
97 if end - start >= target_len:
98 valid_intervals.append((start, end))
99
100 if self.segment_strategy == "silence_aware":
101 if valid_intervals:
102 start, end = random.choice(valid_intervals)
103 seg_max_start = max(start, end - target_len)
104 chosen = random.randint(start, seg_max_start) if seg_max_start > start else start
105 return min(chosen / self.sr, max_offset)
106 return random.uniform(0, max_offset)
107
108 if self.segment_strategy == "hybrid":
109 if valid_intervals and random.random() < 0.7:
110 start, end = random.choice(valid_intervals)
111 seg_max_start = max(start, end - target_len)
112 chosen = random.randint(start, seg_max_start) if seg_max_start > start else start
113 return min(chosen / self.sr, max_offset)
114 return random.uniform(0, max_offset)
115
116 return random.uniform(0, max_offset)
117
73 def __getitem__(self, idx): 118 def __getitem__(self, idx):
74 sample = self.samples[idx // self.n_crops] 119 sample = self.samples[idx // self.n_crops]
75 duration = sample["duration"]
76 max_offset = max(0, duration - 5.0)
77 offset = random.uniform(0, max_offset) if max_offset > 0 else 0
78 120
79 audio_path = self.asset_root / sample["audio_path"] 121 audio_path = self.asset_root / sample["audio_path"]
122 offset = self._choose_offset(sample, audio_path)
80 y = self._load_segment(str(audio_path), offset, 5.0) 123 y = self._load_segment(str(audio_path), offset, 5.0)
81 124
82 if self.augment and sample.get("type") != "reference": 125 if self.augment and sample.get("type") != "reference":
...@@ -172,6 +215,8 @@ class SongPairDataset(Dataset): ...@@ -172,6 +215,8 @@ class SongPairDataset(Dataset):
172 hop_length: int = 160, 215 hop_length: int = 160,
173 segment_dur: float = 5.0, 216 segment_dur: float = 5.0,
174 augment: bool = True, 217 augment: bool = True,
218 segment_strategy: str = "random",
219 silence_top_db: int = 30,
175 ): 220 ):
176 self.sr = sr 221 self.sr = sr
177 self.n_mels = n_mels 222 self.n_mels = n_mels
...@@ -179,6 +224,8 @@ class SongPairDataset(Dataset): ...@@ -179,6 +224,8 @@ class SongPairDataset(Dataset):
179 self.hop_length = hop_length 224 self.hop_length = hop_length
180 self.segment_len = int(segment_dur * sr) 225 self.segment_len = int(segment_dur * sr)
181 self.augment = augment 226 self.augment = augment
227 self.segment_strategy = segment_strategy
228 self.silence_top_db = silence_top_db
182 self.data_dir = Path(data_dir) 229 self.data_dir = Path(data_dir)
183 self.asset_root = self.data_dir.parent if self.data_dir.name == "manifests" else self.data_dir 230 self.asset_root = self.data_dir.parent if self.data_dir.name == "manifests" else self.data_dir
184 231
...@@ -211,11 +258,32 @@ class SongPairDataset(Dataset): ...@@ -211,11 +258,32 @@ class SongPairDataset(Dataset):
211 258
212 def _load_clip(self, sample: Dict) -> np.ndarray: 259 def _load_clip(self, sample: Dict) -> np.ndarray:
213 path = self.asset_root / sample["audio_path"] 260 path = self.asset_root / sample["audio_path"]
214 y, _ = librosa.load(str(path), sr=self.sr, mono=True, duration=5.0) 261 full_y, _ = librosa.load(str(path), sr=self.sr, mono=True)
262 duration = float(sample.get("duration", len(full_y) / self.sr))
263 max_offset = max(0.0, duration - 5.0)
264 offset = 0.0
265 if max_offset > 0:
266 if self.segment_strategy == "random":
267 offset = random.uniform(0, max_offset)
268 else:
269 intervals = librosa.effects.split(full_y, top_db=self.silence_top_db)
270 valid = [(int(s), int(e)) for s, e in intervals if int(e) - int(s) >= self.segment_len] if len(intervals) else []
271 if self.segment_strategy == "silence_aware" and valid:
272 start, end = random.choice(valid)
273 seg_max_start = max(start, end - self.segment_len)
274 chosen = random.randint(start, seg_max_start) if seg_max_start > start else start
275 offset = min(chosen / self.sr, max_offset)
276 elif self.segment_strategy == "hybrid" and valid and random.random() < 0.7:
277 start, end = random.choice(valid)
278 seg_max_start = max(start, end - self.segment_len)
279 chosen = random.randint(start, seg_max_start) if seg_max_start > start else start
280 offset = min(chosen / self.sr, max_offset)
281 else:
282 offset = random.uniform(0, max_offset)
283 start = int(offset * self.sr)
284 y = full_y[start : start + self.segment_len]
215 if len(y) < self.segment_len: 285 if len(y) < self.segment_len:
216 y = np.pad(y, (0, self.segment_len - len(y))) 286 y = np.pad(y, (0, self.segment_len - len(y)))
217 else:
218 y = y[: self.segment_len]
219 return y 287 return y
220 288
221 def _to_mel(self, y: np.ndarray) -> torch.Tensor: 289 def _to_mel(self, y: np.ndarray) -> torch.Tensor:
......
...@@ -104,6 +104,8 @@ class BaseAdapter: ...@@ -104,6 +104,8 @@ class BaseAdapter:
104 eval_ratio: float = 0.2, 104 eval_ratio: float = 0.2,
105 query_duration: float = 8.0, 105 query_duration: float = 8.0,
106 query_stride: float | None = None, 106 query_stride: float | None = None,
107 query_strategy: str = "random",
108 silence_top_db: int = 30,
107 seed: int = 42, 109 seed: int = 42,
108 ) -> Dict: 110 ) -> Dict:
109 output_root.mkdir(parents=True, exist_ok=True) 111 output_root.mkdir(parents=True, exist_ok=True)
...@@ -126,6 +128,12 @@ class BaseAdapter: ...@@ -126,6 +128,12 @@ class BaseAdapter:
126 str(query_stride), 128 str(query_stride),
127 ]) 129 ])
128 cmd.extend([ 130 cmd.extend([
131 "--query-strategy",
132 str(query_strategy),
133 "--silence-top-db",
134 str(silence_top_db),
135 ])
136 cmd.extend([
129 "--seed", 137 "--seed",
130 str(seed), 138 str(seed),
131 ]) 139 ])
...@@ -361,6 +369,9 @@ def smoke_local_dataset( ...@@ -361,6 +369,9 @@ def smoke_local_dataset(
361 eval_ratio: float, 369 eval_ratio: float,
362 query_duration: float, 370 query_duration: float,
363 query_stride: float | None, 371 query_stride: float | None,
372 query_strategy: str,
373 segment_strategy: str,
374 silence_top_db: int,
364 seed: int, 375 seed: int,
365 train_epochs: int, 376 train_epochs: int,
366 batch_size: int, 377 batch_size: int,
...@@ -388,6 +399,8 @@ def smoke_local_dataset( ...@@ -388,6 +399,8 @@ def smoke_local_dataset(
388 eval_ratio=eval_ratio, 399 eval_ratio=eval_ratio,
389 query_duration=query_duration, 400 query_duration=query_duration,
390 query_stride=query_stride, 401 query_stride=query_stride,
402 query_strategy=query_strategy,
403 silence_top_db=silence_top_db,
391 seed=seed, 404 seed=seed,
392 ) 405 )
393 manifests_dir = Path(prepare_summary["output_dir"]) 406 manifests_dir = Path(prepare_summary["output_dir"])
...@@ -407,6 +420,8 @@ def smoke_local_dataset( ...@@ -407,6 +420,8 @@ def smoke_local_dataset(
407 "--device", resolved_device, 420 "--device", resolved_device,
408 "--epochs", str(train_epochs), 421 "--epochs", str(train_epochs),
409 "--batch-size", str(batch_size), 422 "--batch-size", str(batch_size),
423 "--segment-strategy", str(segment_strategy),
424 "--silence-top-db", str(silence_top_db),
410 ], check=True) 425 ], check=True)
411 426
412 subprocess.run([ 427 subprocess.run([
...@@ -444,6 +459,9 @@ def smoke_local_dataset( ...@@ -444,6 +459,9 @@ def smoke_local_dataset(
444 base_cfg=base_cfg, 459 base_cfg=base_cfg,
445 ) 460 )
446 config["data"]["manifest_query_stride"] = query_stride 461 config["data"]["manifest_query_stride"] = query_stride
462 config["data"]["manifest_query_strategy"] = query_strategy
463 config["data"]["silence_top_db"] = silence_top_db
464 config["run"]["train_segment_strategy"] = segment_strategy
447 report_dir.mkdir(parents=True, exist_ok=True) 465 report_dir.mkdir(parents=True, exist_ok=True)
448 config_path.write_text(json.dumps(config, indent=2)) 466 config_path.write_text(json.dumps(config, indent=2))
449 467
...@@ -493,6 +511,8 @@ def main(): ...@@ -493,6 +511,8 @@ def main():
493 p.add_argument("--eval-ratio", type=float, default=0.2) 511 p.add_argument("--eval-ratio", type=float, default=0.2)
494 p.add_argument("--query-duration", type=float, default=8.0) 512 p.add_argument("--query-duration", type=float, default=8.0)
495 p.add_argument("--query-stride", type=float, default=None) 513 p.add_argument("--query-stride", type=float, default=None)
514 p.add_argument("--query-strategy", choices=["random", "sliding", "silence_aware", "hybrid"], default="random")
515 p.add_argument("--silence-top-db", type=int, default=30)
496 p.add_argument("--seed", type=int, default=42) 516 p.add_argument("--seed", type=int, default=42)
497 517
498 p = sub.add_parser("inspect-local") 518 p = sub.add_parser("inspect-local")
...@@ -523,6 +543,9 @@ def main(): ...@@ -523,6 +543,9 @@ def main():
523 p.add_argument("--eval-ratio", type=float, default=0.2) 543 p.add_argument("--eval-ratio", type=float, default=0.2)
524 p.add_argument("--query-duration", type=float, default=8.0) 544 p.add_argument("--query-duration", type=float, default=8.0)
525 p.add_argument("--query-stride", type=float, default=None) 545 p.add_argument("--query-stride", type=float, default=None)
546 p.add_argument("--query-strategy", choices=["random", "sliding", "silence_aware", "hybrid"], default="random")
547 p.add_argument("--segment-strategy", choices=["random", "silence_aware", "hybrid"], default="random")
548 p.add_argument("--silence-top-db", type=int, default=30)
526 p.add_argument("--seed", type=int, default=42) 549 p.add_argument("--seed", type=int, default=42)
527 p.add_argument("--train-epochs", type=int, default=1) 550 p.add_argument("--train-epochs", type=int, default=1)
528 p.add_argument("--batch-size", type=int, default=2) 551 p.add_argument("--batch-size", type=int, default=2)
...@@ -545,6 +568,8 @@ def main(): ...@@ -545,6 +568,8 @@ def main():
545 eval_ratio=args.eval_ratio, 568 eval_ratio=args.eval_ratio,
546 query_duration=args.query_duration, 569 query_duration=args.query_duration,
547 query_stride=args.query_stride, 570 query_stride=args.query_stride,
571 query_strategy=args.query_strategy,
572 silence_top_db=args.silence_top_db,
548 seed=args.seed, 573 seed=args.seed,
549 ) 574 )
550 print(json.dumps(summary, indent=2, ensure_ascii=False)) 575 print(json.dumps(summary, indent=2, ensure_ascii=False))
...@@ -577,6 +602,9 @@ def main(): ...@@ -577,6 +602,9 @@ def main():
577 eval_ratio=args.eval_ratio, 602 eval_ratio=args.eval_ratio,
578 query_duration=args.query_duration, 603 query_duration=args.query_duration,
579 query_stride=args.query_stride, 604 query_stride=args.query_stride,
605 query_strategy=args.query_strategy,
606 segment_strategy=args.segment_strategy,
607 silence_top_db=args.silence_top_db,
580 seed=args.seed, 608 seed=args.seed,
581 train_epochs=args.train_epochs, 609 train_epochs=args.train_epochs,
582 batch_size=args.batch_size, 610 batch_size=args.batch_size,
......
...@@ -11,6 +11,7 @@ from pathlib import Path ...@@ -11,6 +11,7 @@ from pathlib import Path
11 from typing import List, Dict 11 from typing import List, Dict
12 import numpy as np 12 import numpy as np
13 import soundfile as sf 13 import soundfile as sf
14 import librosa
14 15
15 16
16 def write_catalog(records: List[Dict], output_path: Path): 17 def write_catalog(records: List[Dict], output_path: Path):
...@@ -45,6 +46,8 @@ def build_train_eval_from_audio_dir( ...@@ -45,6 +46,8 @@ def build_train_eval_from_audio_dir(
45 eval_ratio: float = 0.2, 46 eval_ratio: float = 0.2,
46 query_duration: float = 8.0, 47 query_duration: float = 8.0,
47 query_stride: float | None = None, 48 query_stride: float | None = None,
49 query_strategy: str = "random",
50 silence_top_db: int = 30,
48 seed: int = 42, 51 seed: int = 42,
49 ): 52 ):
50 rng = random.Random(seed) 53 rng = random.Random(seed)
...@@ -59,6 +62,38 @@ def build_train_eval_from_audio_dir( ...@@ -59,6 +62,38 @@ def build_train_eval_from_audio_dir(
59 train = [] 62 train = []
60 test = [] 63 test = []
61 64
65 def compute_silence_aware_offsets(path: Path, duration: float) -> List[float]:
66 if duration < query_duration:
67 return []
68 try:
69 y, sr = librosa.load(str(path), sr=None, mono=True)
70 intervals = librosa.effects.split(y, top_db=silence_top_db)
71 if intervals is None or len(intervals) == 0:
72 raise ValueError("no_non_silent_intervals")
73 offsets = []
74 target_len = int(query_duration * sr)
75 for start, end in intervals:
76 start = int(start)
77 end = int(end)
78 if end - start < target_len:
79 continue
80 if query_stride and query_stride > 0:
81 stride = int(query_stride * sr)
82 local_positions = list(range(start, max(start + 1, end - target_len + 1), stride))
83 if not local_positions:
84 local_positions = [start]
85 last_pos = end - target_len
86 if last_pos >= start and local_positions[-1] != last_pos:
87 local_positions.append(last_pos)
88 offsets.extend([round(pos / sr, 3) for pos in local_positions])
89 else:
90 seg_max_start = max(start, end - target_len)
91 chosen = rng.randint(start, seg_max_start) if seg_max_start > start else start
92 offsets.append(round(chosen / sr, 3))
93 return sorted(set(x for x in offsets if x <= max(0.0, duration - query_duration)))
94 except Exception:
95 return []
96
62 for idx, path in enumerate(files): 97 for idx, path in enumerate(files):
63 target_name = f"{source_dataset}_{idx:05d}{path.suffix.lower()}" 98 target_name = f"{source_dataset}_{idx:05d}{path.suffix.lower()}"
64 target_path = audio_out_dir / target_name 99 target_path = audio_out_dir / target_name
...@@ -82,7 +117,21 @@ def build_train_eval_from_audio_dir( ...@@ -82,7 +117,21 @@ def build_train_eval_from_audio_dir(
82 refs.append(ref) 117 refs.append(ref)
83 118
84 if duration >= query_duration: 119 if duration >= query_duration:
120 if query_strategy in {"silence_aware", "hybrid"}:
121 silence_offsets = compute_silence_aware_offsets(path, duration)
122 else:
123 silence_offsets = []
124
125 if query_strategy == "silence_aware" and silence_offsets:
126 offsets = silence_offsets
127 elif query_strategy == "hybrid" and silence_offsets:
85 if query_stride and query_stride > 0: 128 if query_stride and query_stride > 0:
129 offsets = silence_offsets
130 else:
131 max_offset = max(0.0, duration - query_duration)
132 random_offset = round(rng.uniform(0.0, max_offset) if max_offset > 0 else 0.0, 3)
133 offsets = sorted(set(silence_offsets + [random_offset]))
134 elif query_stride and query_stride > 0:
86 max_offset = max(0.0, duration - query_duration) 135 max_offset = max(0.0, duration - query_duration)
87 offsets = [round(x, 3) for x in np.arange(0.0, max_offset + 1e-9, query_stride).tolist()] 136 offsets = [round(x, 3) for x in np.arange(0.0, max_offset + 1e-9, query_stride).tolist()]
88 if not offsets: 137 if not offsets:
...@@ -124,6 +173,7 @@ def build_train_eval_from_audio_dir( ...@@ -124,6 +173,7 @@ def build_train_eval_from_audio_dir(
124 "test_queries": len(test), 173 "test_queries": len(test),
125 "query_duration": query_duration, 174 "query_duration": query_duration,
126 "query_stride": query_stride, 175 "query_stride": query_stride,
176 "query_strategy": query_strategy,
127 "output_dir": str(manifests_dir), 177 "output_dir": str(manifests_dir),
128 } 178 }
129 179
...@@ -225,6 +275,8 @@ def main(): ...@@ -225,6 +275,8 @@ def main():
225 p.add_argument("--eval-ratio", type=float, default=0.2) 275 p.add_argument("--eval-ratio", type=float, default=0.2)
226 p.add_argument("--query-duration", type=float, default=8.0) 276 p.add_argument("--query-duration", type=float, default=8.0)
227 p.add_argument("--query-stride", type=float, default=None) 277 p.add_argument("--query-stride", type=float, default=None)
278 p.add_argument("--query-strategy", choices=["random", "sliding", "silence_aware", "hybrid"], default="random")
279 p.add_argument("--silence-top-db", type=int, default=30)
228 p.add_argument("--seed", type=int, default=42) 280 p.add_argument("--seed", type=int, default=42)
229 281
230 p = sub.add_parser("inspect-audio-dir") 282 p = sub.add_parser("inspect-audio-dir")
...@@ -247,6 +299,8 @@ def main(): ...@@ -247,6 +299,8 @@ def main():
247 eval_ratio=args.eval_ratio, 299 eval_ratio=args.eval_ratio,
248 query_duration=args.query_duration, 300 query_duration=args.query_duration,
249 query_stride=args.query_stride, 301 query_stride=args.query_stride,
302 query_strategy=args.query_strategy,
303 silence_top_db=args.silence_top_db,
250 seed=args.seed, 304 seed=args.seed,
251 ) 305 )
252 print(json.dumps({"status": "ok", **summary}, ensure_ascii=False)) 306 print(json.dumps({"status": "ok", **summary}, ensure_ascii=False))
......
...@@ -125,6 +125,8 @@ def main(): ...@@ -125,6 +125,8 @@ def main():
125 parser.add_argument("--epochs", type=int, default=None) 125 parser.add_argument("--epochs", type=int, default=None)
126 parser.add_argument("--batch-size", type=int, default=None) 126 parser.add_argument("--batch-size", type=int, default=None)
127 parser.add_argument("--lr", type=float, default=None) 127 parser.add_argument("--lr", type=float, default=None)
128 parser.add_argument("--segment-strategy", choices=["random", "silence_aware", "hybrid"], default="random")
129 parser.add_argument("--silence-top-db", type=int, default=30)
128 parser.add_argument("--dry-run", action="store_true") 130 parser.add_argument("--dry-run", action="store_true")
129 args = parser.parse_args() 131 args = parser.parse_args()
130 132
...@@ -153,6 +155,8 @@ def main(): ...@@ -153,6 +155,8 @@ def main():
153 hop_length=cfg["data"]["hop_length"], 155 hop_length=cfg["data"]["hop_length"],
154 segment_dur=cfg["data"]["segment_dur"], 156 segment_dur=cfg["data"]["segment_dur"],
155 augment=True, 157 augment=True,
158 segment_strategy=args.segment_strategy,
159 silence_top_db=args.silence_top_db,
156 ) 160 )
157 161
158 catalog_dataset = ACRDataset( 162 catalog_dataset = ACRDataset(
...@@ -166,6 +170,8 @@ def main(): ...@@ -166,6 +170,8 @@ def main():
166 augment=False, 170 augment=False,
167 n_crops_per_song=1, 171 n_crops_per_song=1,
168 song_to_idx=train_dataset.song_to_idx, 172 song_to_idx=train_dataset.song_to_idx,
173 segment_strategy=args.segment_strategy,
174 silence_top_db=args.silence_top_db,
169 ) 175 )
170 176
171 train_loader = DataLoader( 177 train_loader = DataLoader(
......
...@@ -5398,3 +5398,46 @@ ...@@ -5398,3 +5398,46 @@
5398 - **人工标 offset 的短视频片段**：保持单条 query 5398 - **人工标 offset 的短视频片段**：保持单条 query
5399 - **只有整首音频、没有 query 起点的素材**：自动生成多窗口 query 5399 - **只有整首音频、没有 query 起点的素材**：自动生成多窗口 query
5400 - 这让 `7/8/16/18` 这类 query 型素材可以更直接进入训练与评测流水线，同时保留对 `pgvector` 入库的可追踪性 5400 - 这让 `7/8/16/18` 这类 query 型素材可以更直接进入训练与评测流水线，同时保留对 `pgvector` 入库的可追踪性
5401
5402 ### Stage: silence-aware segmentation for training and open-dataset query generation
5403
5404 完成项：
5405 - 在 `acr-engine/src/data/dataset.py` 为训练切片新增：
5406 - `segment_strategy=random|silence_aware|hybrid`
5407 - `silence_top_db`
5408 - 接入 `librosa.effects.split`，用于优先选择非静音区作为训练片段来源
5409 - 在 `acr-engine/src/data/manifest_tools.py` 为外部数据 query 生成新增：
5410 - `--query-strategy random|sliding|silence_aware|hybrid`
5411 - `--silence-top-db`
5412 - 在 `acr-engine/train.py` 暴露训练 CLI 参数：
5413 - `--segment-strategy`
5414 - `--silence-top-db`
5415 - 在 `acr-engine/src/data/external_adapters.py` 接通 `prepare-local` / `smoke-local` 的策略透传与配置落盘
5416 - 在 [docs/training-data-and-pgvector-guide.md](./training-data-and-pgvector-guide.md) 补充“切片策略”章节
5417
5418 验证结果：
5419 - 代码编译验证：
5420 - `/usr/local/miniconda3/bin/python -m py_compile src/data/dataset.py src/data/manifest_tools.py train.py src/data/external_adapters.py`
5421 - 人造音频验证：
5422 - 构造 `4s silence + 10s tone + 4s silence`
5423 - `manifest_tools.py --query-strategy silence_aware --query-duration 5 --query-stride 2.5`
5424 - 导出 query offset：`3.968, 8.968, 9.08`
5425 - 说明 query 已明显偏向非静音主体区
5426 - 训练侧偏移验证：
5427 - `random` offset 样本：`0.325, 1.13, 2.902, 3.575, 8.313, 8.797, 9.574, 11.598`
5428 - `silence_aware` offset 样本：`4.173, 4.228, 4.736, 5.111, 5.874, 5.974, 8.436, 8.805`
5429 - 说明 silence-aware 显著减少落入头尾静音区的概率
5430 - dry-run 验证：
5431 - `train.py --data data/synthetic_v2 --dry-run --segment-strategy silence_aware`
5432 - forward/backward 成功，`Embedding shape: torch.Size([64, 192])`
5433 - adapter 验证：
5434 - `external_adapters.py prepare-local ... --query-strategy silence_aware`
5435 - summary 已记录 `query_strategy: silence_aware`
5436
5437 结论：
5438 - 当前项目不再只有“随机切”
5439 - 已形成：
5440 - **训练侧**：`random / silence_aware / hybrid`
5441 - **建库侧**：固定滑窗
5442 - **开源集 query 生成侧**：`random / sliding / silence_aware / hybrid`
5443 - 下一阶段可继续叠加 beat/onset/chorus-aware 切片，而无需推翻现有流程
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...@@ -345,6 +345,61 @@ flowchart TD ...@@ -345,6 +345,61 @@ flowchart TD
345 345
346 --- 346 ---
347 347
348 ## 11.5 切片策略：不要只用随机切
349
350 当前项目现在已经支持 4 类切片思路，但职责不同：
351
352 | 策略 | 适用位置 | 作用 | 是否已接入 |
353 |---|---|---|---|
354 | `random` | 训练 query | 增强泛化，模拟未知用户截取点 | 是 |
355 | `sliding` | 建库 / query 生成 | 保证覆盖率，减少漏召回 | 是 |
356 | `silence_aware` | 训练 query / 外部 query 生成 | 优先避开静音，落到真正有音乐内容的片段 | 是 |
357 | `hybrid` | 训练 query / 外部 query 生成 | 混合 silence-aware + random，兼顾稳定性与泛化 | 是 |
358
359 推荐理解：
360
361 1. **训练不是全部随机切**
362 当前训练集可用 `random / silence_aware / hybrid`
363 2. **reference 建库不是随机切**
364 建库仍然是固定滑窗
365 3. **外部数据 query 生成也不是只能随机切**
366 现在可选 `--query-strategy silence_aware`
367
368 为什么不直接完全依赖音乐结构分段？
369
370 - ACR 真实 query 往往来自短视频、录屏、随手截取，不一定对齐节拍或段落边界
371 - 先做 **静音感知分段**，收益最大、风险最小
372 - 更复杂的 beat / chorus / onset 分段可以作为下一阶段增强，而不应替代现有随机增强
373
374 ### 训练侧推荐
375
376 ```bash
377 /usr/local/miniconda3/bin/python acr-engine/train.py \
378 --data data/your_manifests \
379 --segment-strategy hybrid \
380 --silence-top-db 30
381 ```
382
383 建议：
384 - baseline：`random`
385 - 更稳的音乐任务：`hybrid`
386 - 已知原始音频静音很多：`silence_aware`
387
388 ### 外部数据 query 生成推荐
389
390 ```bash
391 /usr/local/miniconda3/bin/python acr-engine/src/data/external_adapters.py prepare-local fma data/raw/fma_small_audio \
392 --output-root data/external_ingested \
393 --query-duration 8 \
394 --query-stride 4 \
395 --query-strategy silence_aware \
396 --silence-top-db 30
397 ```
398
399 这会优先从非静音区生成 query，而不是从长静音头尾随机采样。
400
401 ---
402
348 ## 12. 你这批内部素材 type，哪些推荐参与训练 403 ## 12. 你这批内部素材 type，哪些推荐参与训练
349 404
350 ## 12.1 一页结论 405 ## 12.1 一页结论
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