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Running on Zero
Running on Zero
| from __future__ import annotations | |
| import argparse | |
| import random | |
| from pathlib import Path | |
| import pandas as pd | |
| AUDIO_EXTENSIONS = { | |
| ".wav", ".flac", ".mp3", ".m4a", ".ogg", ".opus", ".aac" | |
| } | |
| def find_audio_files(audio_root: Path): | |
| files = [] | |
| for p in audio_root.rglob("*"): | |
| if p.is_file() and p.suffix.lower() in AUDIO_EXTENSIONS: | |
| files.append(p) | |
| return sorted(files) | |
| def make_rows(files, split_name: str): | |
| rows = [] | |
| for p in files: | |
| rows.append({ | |
| "file_path": p.resolve().as_posix(), | |
| "binary_label": 0, | |
| "attack_type": "bonafide", | |
| "start_fake": -1, | |
| "end_fake": -1, | |
| "dataset": "GLOBE", | |
| "split": split_name, | |
| "source_file": p.name, | |
| "source_folder": p.parent.name, | |
| }) | |
| return rows | |
| def save_csv(rows, out_csv: Path): | |
| out_csv.parent.mkdir(parents=True, exist_ok=True) | |
| df = pd.DataFrame(rows) | |
| df.to_csv(out_csv, index=False) | |
| print(f"Saved: {out_csv}") | |
| print(f"Rows: {len(df)}") | |
| if len(df): | |
| print(df[["dataset", "split", "binary_label", "attack_type"]].value_counts()) | |
| def main(): | |
| parser = argparse.ArgumentParser( | |
| description=( | |
| "Create small real/bonafide GLOBE metadata CSVs for AuralGuard. " | |
| "This does not train anything; it only creates train/val/test CSV files." | |
| ) | |
| ) | |
| parser.add_argument( | |
| "--audio-root", | |
| required=True, | |
| help="Folder containing GLOBE audio files. The script searches recursively." | |
| ) | |
| parser.add_argument( | |
| "--out-dir", | |
| default="data/metadata", | |
| help="Where to save train_globe.csv, val_globe.csv, globe_test.csv" | |
| ) | |
| parser.add_argument("--max-train", type=int, default=2000) | |
| parser.add_argument("--max-val", type=int, default=300) | |
| parser.add_argument("--max-test", type=int, default=300) | |
| parser.add_argument("--seed", type=int, default=42) | |
| args = parser.parse_args() | |
| audio_root = Path(args.audio_root) | |
| if not audio_root.exists(): | |
| raise FileNotFoundError(f"Audio root does not exist: {audio_root}") | |
| files = find_audio_files(audio_root) | |
| print(f"Found audio files: {len(files)}") | |
| if len(files) == 0: | |
| raise RuntimeError( | |
| "No audio files found. Check the folder path or supported extensions: " | |
| + ", ".join(sorted(AUDIO_EXTENSIONS)) | |
| ) | |
| rng = random.Random(args.seed) | |
| rng.shuffle(files) | |
| n_train = min(args.max_train, len(files)) | |
| n_val = min(args.max_val, max(0, len(files) - n_train)) | |
| n_test = min(args.max_test, max(0, len(files) - n_train - n_val)) | |
| train_files = files[:n_train] | |
| val_files = files[n_train:n_train + n_val] | |
| test_files = files[n_train + n_val:n_train + n_val + n_test] | |
| out_dir = Path(args.out_dir) | |
| save_csv(make_rows(train_files, "train"), out_dir / "train_globe.csv") | |
| save_csv(make_rows(val_files, "val"), out_dir / "val_globe.csv") | |
| save_csv(make_rows(test_files, "test"), out_dir / "globe_test.csv") | |
| print("\nDone.") | |
| print("Recommended next step:") | |
| print('python scripts\\check_metadata.py --csv "data\\metadata\\train_globe.csv"') | |
| print('python scripts\\check_metadata.py --csv "data\\metadata\\val_globe.csv"') | |
| print('python scripts\\check_metadata.py --csv "data\\metadata\\globe_test.csv"') | |
| if __name__ == "__main__": | |
| main() | |