ab_smoke_segmentation.py
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#!/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("--strategies", nargs="*", default=DEFAULT_STRATEGIES)
parser.add_argument("--output-json", default=None)
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)
results = []
for strategy in args.strategies:
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,
"--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],
})
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,
"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()