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Running on Zero
Running on Zero
| from __future__ import annotations | |
| import argparse | |
| from pathlib import Path | |
| import numpy as np | |
| import pandas as pd | |
| import soundfile as sf | |
| import torchaudio | |
| from tqdm import tqdm | |
| def load_audio(path, target_sr=16000): | |
| wav, sr = torchaudio.load(path) | |
| wav = wav.mean(dim=0) | |
| if sr != target_sr: | |
| wav = torchaudio.functional.resample(wav, sr, target_sr) | |
| return wav.numpy(), target_sr | |
| def augment(x, kind): | |
| x = x.astype(np.float32) | |
| if kind == "noise": | |
| noise = np.random.normal(0, 0.005, size=x.shape).astype(np.float32) | |
| return np.clip(x + noise, -1, 1) | |
| if kind == "volume_low": | |
| return np.clip(x * 0.65, -1, 1) | |
| if kind == "volume_high": | |
| return np.clip(x * 1.25, -1, 1) | |
| if kind == "clip": | |
| return np.clip(x * 1.8, -0.8, 0.8) | |
| if kind == "dropout": | |
| y = x.copy() | |
| if len(y) > 1000: | |
| start = np.random.randint(0, max(1, len(y) - len(y)//10)) | |
| y[start:start + len(y)//20] = 0 | |
| return y | |
| return x | |
| def main(): | |
| p = argparse.ArgumentParser(description="Create simple offline audio augmentations and a matching metadata CSV.") | |
| p.add_argument("--csv", required=True, help="Input metadata CSV") | |
| p.add_argument("--out-audio-dir", required=True) | |
| p.add_argument("--out-csv", required=True) | |
| p.add_argument("--limit", type=int, default=2000) | |
| p.add_argument("--target-sr", type=int, default=16000) | |
| p.add_argument("--augmentations", nargs="+", default=["noise", "volume_low", "volume_high", "clip", "dropout"]) | |
| p.add_argument("--seed", type=int, default=42) | |
| args = p.parse_args() | |
| np.random.seed(args.seed) | |
| df = pd.read_csv(args.csv, low_memory=False) | |
| if args.limit > 0: | |
| df = df.sample(n=min(len(df), args.limit), random_state=args.seed).reset_index(drop=True) | |
| out_dir = Path(args.out_audio_dir) | |
| out_dir.mkdir(parents=True, exist_ok=True) | |
| rows = [] | |
| for i, row in tqdm(df.iterrows(), total=len(df), desc="Augmenting"): | |
| x, sr = load_audio(row["file_path"], args.target_sr) | |
| for kind in args.augmentations: | |
| y = augment(x, kind) | |
| out_path = out_dir / f"aug_{i:06d}_{kind}.wav" | |
| sf.write(out_path, y, sr) | |
| new_row = row.to_dict() | |
| new_row["file_path"] = out_path.resolve().as_posix() | |
| new_row["dataset"] = str(row.get("dataset", "")) + "_Aug" | |
| rows.append(new_row) | |
| out_df = pd.DataFrame(rows) | |
| Path(args.out_csv).parent.mkdir(parents=True, exist_ok=True) | |
| out_df.to_csv(args.out_csv, index=False) | |
| print("Saved:", args.out_csv) | |
| print("Rows:", len(out_df)) | |
| if __name__ == "__main__": | |
| main() | |