manifest_tools.py
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"""External dataset manifest conversion templates."""
from __future__ import annotations
import argparse
import csv
import json
import random
import shutil
from pathlib import Path
from typing import List, Dict
import numpy as np
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,
query_stride: float | None = None,
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)
audio_out_dir = output_dir / "audio"
audio_out_dir.mkdir(parents=True, exist_ok=True)
refs = []
train = []
test = []
for idx, path in enumerate(files):
target_name = f"{source_dataset}_{idx:05d}{path.suffix.lower()}"
target_path = audio_out_dir / target_name
if not target_path.exists():
shutil.copy2(path, target_path)
rel = target_path.relative_to(output_dir)
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:
if query_stride and query_stride > 0:
max_offset = max(0.0, duration - query_duration)
offsets = [round(x, 3) for x in np.arange(0.0, max_offset + 1e-9, query_stride).tolist()]
if not offsets:
offsets = [0.0]
if offsets[-1] < round(max_offset, 3):
offsets.append(round(max_offset, 3))
else:
max_offset = max(0.0, duration - query_duration)
offsets = [round(rng.uniform(0.0, max_offset) if max_offset > 0 else 0.0, 3)]
for seg_idx, offset in enumerate(offsets):
query = {
"song_id": song_id,
"audio_path": str(rel),
"duration": query_duration,
"type": "clean",
"offset": offset,
"segment_type": "external_query",
"source_dataset": source_dataset,
"query_index": seg_idx,
}
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),
"query_duration": query_duration,
"query_stride": query_stride,
"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 validate_splits(manifests_dir: Path):
required = ["catalog.json", "train.json", "test.json", "val.json"]
missing = [name for name in required if not (manifests_dir / name).exists()]
if missing:
return {"ok": False, "missing_files": missing}
catalog = json.loads((manifests_dir / "catalog.json").read_text())
train = json.loads((manifests_dir / "train.json").read_text())
test = json.loads((manifests_dir / "test.json").read_text())
val = json.loads((manifests_dir / "val.json").read_text())
catalog_refs = [x for x in catalog if x.get("type") == "reference"]
train_queries = [x for x in train if x.get("type") != "reference"]
test_queries = [x for x in test if x.get("type") != "reference"]
val_queries = [x for x in val if x.get("type") != "reference"]
source_values = {
x.get("source_dataset", "unknown")
for x in catalog_refs + train_queries + test_queries + val_queries
}
errors = []
if not catalog_refs:
errors.append("catalog_has_no_references")
if not train_queries:
errors.append("train_has_no_queries")
if not test_queries:
errors.append("test_has_no_queries")
if len(source_values) > 1:
errors.append("mixed_source_dataset_values")
return {
"ok": len(errors) == 0,
"errors": errors,
"catalog_references": len(catalog_refs),
"train_queries": len(train_queries),
"test_queries": len(test_queries),
"val_queries": len(val_queries),
"source_datasets": sorted(source_values),
}
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("--query-stride", type=float, default=None)
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)
p = sub.add_parser("validate-splits")
p.add_argument("manifests_dir")
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,
query_stride=args.query_stride,
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))
elif args.cmd == "validate-splits":
summary = validate_splits(Path(args.manifests_dir))
print(json.dumps(summary, ensure_ascii=False))
if __name__ == "__main__":
main()