ab_smoke_segmentation.py
6.06 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
#!/usr/bin/env python3
from __future__ import annotations
import argparse
import json
import shutil
import subprocess
from pathlib import Path
PYTHON = "/usr/local/miniconda3/bin/python"
DEFAULT_STRATEGIES = [
"random",
"silence_aware",
"high_energy",
"beat_aware",
"repeated_section_aware",
"hybrid",
]
def run(cmd: list[str], cwd: Path) -> str:
return subprocess.check_output(cmd, cwd=str(cwd), text=True)
def parse_last_json(text: str) -> dict:
for start in range(len(text) - 1, -1, -1):
if text[start] != "{":
continue
try:
return json.loads(text[start:])
except json.JSONDecodeError:
continue
raise ValueError("No JSON object found in command output")
def prepare_subset(src_dir: Path, subset_dir: Path, limit: int) -> dict:
files = sorted(src_dir.rglob("*.mp3"))[:limit]
subset_dir.mkdir(parents=True, exist_ok=True)
copied = []
for src in files:
rel = src.relative_to(src_dir)
dst = subset_dir / rel
dst.parent.mkdir(parents=True, exist_ok=True)
if not dst.exists():
shutil.copy2(src, dst)
copied.append(str(dst))
return {
"source_dir": str(src_dir),
"subset_dir": str(subset_dir),
"num_files": len(copied),
"sample_files": copied[:5],
}
def train_strategy_for_query(strategy: str) -> str:
if strategy == "sliding":
return "random"
return strategy
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", default="fma")
parser.add_argument("--input-dir", default="data/raw/fma_small_audio")
parser.add_argument("--work-root", default="data/ab_smoke_segmentation")
parser.add_argument("--subset-size", type=int, default=12)
parser.add_argument("--query-duration", type=float, default=8.0)
parser.add_argument("--query-stride", type=float, default=None)
parser.add_argument("--train-epochs", type=int, default=1)
parser.add_argument("--batch-size", type=int, default=2)
parser.add_argument("--device", default="cpu")
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--max-test-queries", type=int, default=None)
parser.add_argument("--strategies", nargs="*", default=DEFAULT_STRATEGIES)
parser.add_argument("--output-json", default=None)
parser.add_argument("--resume", action="store_true")
args = parser.parse_args()
repo = Path(__file__).resolve().parents[1]
input_dir = (repo / args.input_dir).resolve()
work_root = (repo / args.work_root).resolve()
subset_dir = work_root / "subset_audio"
subset_info = prepare_subset(input_dir, subset_dir, args.subset_size)
progress_path = work_root / "progress.json"
cached_results = {}
if args.resume and progress_path.exists():
try:
payload = json.loads(progress_path.read_text())
cached_results = {item["strategy"]: item for item in payload.get("strategies", [])}
except Exception:
cached_results = {}
results = []
for strategy in args.strategies:
if strategy in cached_results:
results.append(cached_results[strategy])
continue
smoke_root = work_root / strategy
if smoke_root.exists():
shutil.rmtree(smoke_root)
smoke_root.mkdir(parents=True, exist_ok=True)
cmd = [
PYTHON,
"src/data/external_adapters.py",
"smoke-local",
args.dataset,
str(subset_dir),
"--output-root",
str(smoke_root),
"--eval-ratio",
"0.2",
"--query-duration",
str(args.query_duration),
"--query-strategy",
strategy,
"--segment-strategy",
train_strategy_for_query(strategy),
"--train-epochs",
str(args.train_epochs),
"--batch-size",
str(args.batch_size),
"--device",
args.device,
*([] if args.max_test_queries is None else ["--max-test-queries", str(args.max_test_queries)]),
"--seed",
str(args.seed),
]
if args.query_stride is not None:
cmd.extend(["--query-stride", str(args.query_stride)])
output = run(cmd, cwd=repo)
summary = parse_last_json(output)
eval_json = Path(summary["eval_json"])
eval_report = json.loads(eval_json.read_text())
results.append({
"strategy": strategy,
"train_segment_strategy": train_strategy_for_query(strategy),
"num_queries": eval_report["num_queries"],
"top1": eval_report["top1"],
"topk": eval_report["topk"],
"eval_json": str(eval_json),
"report_dir": summary["report_dir"],
"sample_failures": eval_report.get("sample_failures", [])[:3],
})
progress_payload = {
"dataset": args.dataset,
"subset": subset_info,
"query_duration": args.query_duration,
"query_stride": args.query_stride,
"train_epochs": args.train_epochs,
"batch_size": args.batch_size,
"device": args.device,
"strategies": results,
}
progress_path.write_text(json.dumps(progress_payload, ensure_ascii=False, indent=2))
results.sort(key=lambda x: (x["top1"], x["topk"], x["num_queries"]), reverse=True)
report = {
"dataset": args.dataset,
"subset": subset_info,
"query_duration": args.query_duration,
"query_stride": args.query_stride,
"train_epochs": args.train_epochs,
"batch_size": args.batch_size,
"device": args.device,
"max_test_queries": args.max_test_queries,
"strategies": results,
"winner": results[0] if results else None,
}
text = json.dumps(report, ensure_ascii=False, indent=2)
if args.output_json:
out = Path(args.output_json)
out.parent.mkdir(parents=True, exist_ok=True)
out.write_text(text)
print(text)
if __name__ == "__main__":
main()