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Running on Zero
Running on Zero
| import argparse | |
| import random | |
| from pathlib import Path | |
| import pandas as pd | |
| def collect_audio_files(chunks_root): | |
| chunks_root = Path(chunks_root) | |
| audio_exts = {".wav", ".flac", ".mp3"} | |
| files = [] | |
| for p in chunks_root.rglob("*"): | |
| if p.is_file() and p.suffix.lower() in audio_exts: | |
| files.append(p) | |
| return sorted(files) | |
| def group_by_original_audio(files): | |
| """ | |
| Groups chunks by their parent folder. | |
| This avoids putting chunks from the same long interview | |
| into both train and validation/test. | |
| """ | |
| groups = {} | |
| for f in files: | |
| group_name = str(f.parent) | |
| groups.setdefault(group_name, []).append(f) | |
| return list(groups.values()) | |
| def split_groups(groups, train_ratio, val_ratio, seed): | |
| random.seed(seed) | |
| random.shuffle(groups) | |
| n = len(groups) | |
| n_train = int(n * train_ratio) | |
| n_val = int(n * val_ratio) | |
| train_groups = groups[:n_train] | |
| val_groups = groups[n_train:n_train + n_val] | |
| test_groups = groups[n_train + n_val:] | |
| return train_groups, val_groups, test_groups | |
| def flatten(groups): | |
| out = [] | |
| for g in groups: | |
| out.extend(g) | |
| return out | |
| def make_df(files, dataset_name, split): | |
| rows = [] | |
| for f in files: | |
| rows.append({ | |
| "file_path": f.resolve().as_posix(), | |
| "binary_label": 0, | |
| "attack_type": "bonafide", | |
| "start_fake": -1, | |
| "end_fake": -1, | |
| "dataset": dataset_name, | |
| "split": split | |
| }) | |
| return pd.DataFrame(rows) | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--chunks-root", required=True) | |
| parser.add_argument("--dataset-name", required=True) | |
| parser.add_argument("--out-dir", required=True) | |
| parser.add_argument("--train-ratio", type=float, default=0.8) | |
| parser.add_argument("--val-ratio", type=float, default=0.1) | |
| parser.add_argument("--seed", type=int, default=42) | |
| args = parser.parse_args() | |
| files = collect_audio_files(args.chunks_root) | |
| if not files: | |
| raise RuntimeError(f"No audio files found in {args.chunks_root}") | |
| groups = group_by_original_audio(files) | |
| train_groups, val_groups, test_groups = split_groups( | |
| groups, | |
| args.train_ratio, | |
| args.val_ratio, | |
| args.seed | |
| ) | |
| out_dir = Path(args.out_dir) | |
| out_dir.mkdir(parents=True, exist_ok=True) | |
| train_df = make_df(flatten(train_groups), args.dataset_name, "train") | |
| val_df = make_df(flatten(val_groups), args.dataset_name, "val") | |
| test_df = make_df(flatten(test_groups), args.dataset_name, "test") | |
| train_df.to_csv(out_dir / f"{args.dataset_name.lower()}_train.csv", index=False) | |
| val_df.to_csv(out_dir / f"{args.dataset_name.lower()}_val.csv", index=False) | |
| test_df.to_csv(out_dir / f"{args.dataset_name.lower()}_test.csv", index=False) | |
| print(f"Dataset: {args.dataset_name}") | |
| print(f"Total audio chunks: {len(files)}") | |
| print(f"Original audio groups: {len(groups)}") | |
| print(f"Train chunks: {len(train_df)}") | |
| print(f"Val chunks: {len(val_df)}") | |
| print(f"Test chunks: {len(test_df)}") | |
| print(f"Saved to: {out_dir}") | |
| if __name__ == "__main__": | |
| main() | |