manifest_tools.py
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"""External dataset manifest conversion templates."""
from __future__ import annotations
import argparse
import csv
import json
import random
from pathlib import Path
from typing import List, Dict
import soundfile as sf
def write_catalog(records: List[Dict], output_path: Path):
output_path.parent.mkdir(parents=True, exist_ok=True)
with open(output_path, "w") as f:
json.dump(records, f, indent=2, ensure_ascii=False)
def csv_to_catalog(csv_path: Path, output_path: Path, path_field: str = "audio_path", id_field: str = "song_id"):
records = []
with open(csv_path, newline="") as f:
reader = csv.DictReader(f)
for row in reader:
records.append(
{
"song_id": row[id_field],
"audio_path": row[path_field],
"duration": float(row.get("duration", 0.0) or 0.0),
"type": "reference",
"source_dataset": row.get("source_dataset", "external"),
}
)
write_catalog(records, output_path)
return len(records)
def build_train_eval_from_audio_dir(
audio_dir: Path,
output_dir: Path,
source_dataset: str,
exts: tuple[str, ...] = (".wav", ".mp3", ".flac", ".ogg"),
eval_ratio: float = 0.2,
query_duration: float = 8.0,
seed: int = 42,
):
rng = random.Random(seed)
files = [p for p in sorted(audio_dir.rglob("*")) if p.suffix.lower() in exts]
output_dir.mkdir(parents=True, exist_ok=True)
manifests_dir = output_dir / "manifests"
manifests_dir.mkdir(parents=True, exist_ok=True)
refs = []
train = []
test = []
for idx, path in enumerate(files):
rel = path.relative_to(output_dir.parent if output_dir.parent in path.parents else audio_dir.parent)
song_id = f"{source_dataset}_{idx:05d}"
try:
info = sf.info(str(path))
duration = float(info.duration)
except Exception:
duration = 0.0
ref = {
"song_id": song_id,
"audio_path": str(rel),
"duration": duration,
"type": "reference",
"source_dataset": source_dataset,
}
refs.append(ref)
if duration >= query_duration:
max_offset = max(0.0, duration - query_duration)
offset = rng.uniform(0.0, max_offset) if max_offset > 0 else 0.0
query = {
"song_id": song_id,
"audio_path": str(rel),
"duration": query_duration,
"type": "clean",
"offset": round(offset, 3),
"segment_type": "external_query",
"source_dataset": source_dataset,
}
if rng.random() < eval_ratio:
test.append(query)
else:
train.append(query)
if len(files) >= 2 and not train and test:
train.append(test.pop())
if len(files) >= 2 and not test and train:
test.append(train.pop())
write_catalog(refs, manifests_dir / "catalog.json")
write_catalog(train + refs, manifests_dir / "train.json")
write_catalog(test + refs, manifests_dir / "test.json")
write_catalog([], manifests_dir / "val.json")
return {
"catalog": len(refs),
"train_queries": len(train),
"test_queries": len(test),
"output_dir": str(manifests_dir),
}
def inspect_audio_dir(
audio_dir: Path,
exts: tuple[str, ...] = (".wav", ".mp3", ".flac", ".ogg"),
query_duration: float = 8.0,
eval_ratio: float = 0.2,
):
files = [p for p in sorted(audio_dir.rglob("*")) if p.suffix.lower() in exts]
durations = []
eligible = 0
for path in files:
try:
duration = float(sf.info(str(path)).duration)
except Exception:
duration = 0.0
durations.append(duration)
if duration >= query_duration:
eligible += 1
durations_sorted = sorted(durations)
total = len(files)
train_queries = max(0, eligible - max(1 if eligible >= 2 else 0, round(eligible * eval_ratio)))
test_queries = 0 if eligible == 0 else max(1 if eligible >= 2 else eligible, round(eligible * eval_ratio))
return {
"audio_dir": str(audio_dir),
"num_audio_files": total,
"eligible_query_files": eligible,
"query_duration": query_duration,
"recommended_train_queries": train_queries,
"recommended_test_queries": test_queries,
"duration_stats": {
"min": round(durations_sorted[0], 3) if durations_sorted else 0.0,
"median": round(durations_sorted[len(durations_sorted) // 2], 3) if durations_sorted else 0.0,
"max": round(durations_sorted[-1], 3) if durations_sorted else 0.0,
},
}
def main():
parser = argparse.ArgumentParser()
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("csv-to-catalog")
p.add_argument("csv_path")
p.add_argument("output_path")
p.add_argument("--path-field", default="audio_path")
p.add_argument("--id-field", default="song_id")
p = sub.add_parser("audio-dir-to-splits")
p.add_argument("audio_dir")
p.add_argument("output_dir")
p.add_argument("--source-dataset", required=True)
p.add_argument("--eval-ratio", type=float, default=0.2)
p.add_argument("--query-duration", type=float, default=8.0)
p.add_argument("--seed", type=int, default=42)
p = sub.add_parser("inspect-audio-dir")
p.add_argument("audio_dir")
p.add_argument("--query-duration", type=float, default=8.0)
p.add_argument("--eval-ratio", type=float, default=0.2)
args = parser.parse_args()
if args.cmd == "csv-to-catalog":
count = csv_to_catalog(Path(args.csv_path), Path(args.output_path), args.path_field, args.id_field)
print(json.dumps({"status": "ok", "records": count}, ensure_ascii=False))
elif args.cmd == "audio-dir-to-splits":
summary = build_train_eval_from_audio_dir(
Path(args.audio_dir),
Path(args.output_dir),
source_dataset=args.source_dataset,
eval_ratio=args.eval_ratio,
query_duration=args.query_duration,
seed=args.seed,
)
print(json.dumps({"status": "ok", **summary}, ensure_ascii=False))
elif args.cmd == "inspect-audio-dir":
summary = inspect_audio_dir(
Path(args.audio_dir),
query_duration=args.query_duration,
eval_ratio=args.eval_ratio,
)
print(json.dumps({"status": "ok", **summary}, ensure_ascii=False))
if __name__ == "__main__":
main()