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Running on Zero
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127b976 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | import argparse
import os
import csv
import shutil
from pathlib import Path
from datasets import load_dataset, Audio
def safe_name(text):
text = str(text)
keep = []
for ch in text:
if ch.isalnum() or ch in ["-", "_"]:
keep.append(ch)
else:
keep.append("_")
return "".join(keep)
def export_audio_file(audio_info, out_path):
"""
Save/copy audio without decoding.
Hugging Face Audio(decode=False) usually gives:
{
"bytes": ...,
"path": ...
}
"""
out_path = Path(out_path)
out_path.parent.mkdir(parents=True, exist_ok=True)
audio_bytes = audio_info.get("bytes", None)
audio_path = audio_info.get("path", None)
if audio_bytes is not None:
with open(out_path, "wb") as f:
f.write(audio_bytes)
return True
if audio_path is not None and os.path.exists(audio_path):
shutil.copy2(audio_path, out_path)
return True
return False
def save_split(ds, split_name, out_csv, audio_dir, max_items):
rows = []
audio_dir = Path(audio_dir)
audio_dir.mkdir(parents=True, exist_ok=True)
n = len(ds)
if max_items is not None and max_items > 0:
n = min(n, max_items)
print(f"Saving {split_name}: {n} samples")
skipped = 0
for i in range(n):
item = ds[i]
audio_info = item["audio"]
speaker = item.get("speaker", f"{split_name}_{i}")
accent = item.get("accent", "unknown")
text = item.get("text", "")
original_path = audio_info.get("path", None)
if original_path:
base = Path(original_path).stem
else:
base = f"{split_name}_{i:06d}"
file_name = safe_name(base) + ".wav"
out_audio_path = audio_dir / file_name
ok = export_audio_file(audio_info, out_audio_path)
if not ok:
skipped += 1
print(f"Skipping sample {i}: could not save audio")
continue
rows.append({
"file_path": str(out_audio_path).replace("/", "\\"),
"binary_label": 0,
"attack_type": "bonafide",
"start_fake": -1,
"end_fake": -1,
"dataset": "EdAcc",
"split": split_name,
"speaker": speaker,
"accent": accent,
"text": text,
})
if (i + 1) % 500 == 0:
print(f"Saved {i + 1}/{n}")
out_csv = Path(out_csv)
out_csv.parent.mkdir(parents=True, exist_ok=True)
with open(out_csv, "w", newline="", encoding="utf-8") as f:
writer = csv.DictWriter(f, fieldnames=[
"file_path",
"binary_label",
"attack_type",
"start_fake",
"end_fake",
"dataset",
"split",
"speaker",
"accent",
"text",
])
writer.writeheader()
writer.writerows(rows)
print(f"Saved CSV: {out_csv}")
print(f"Rows saved: {len(rows)}")
print(f"Skipped: {skipped}")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--max-train", type=int, default=3000)
parser.add_argument("--max-val", type=int, default=500)
parser.add_argument("--out-train", default="data\\metadata\\train_edacc.csv")
parser.add_argument("--out-val", default="data\\metadata\\val_edacc.csv")
parser.add_argument("--audio-root", default="data\\edacc_audio")
args = parser.parse_args()
print("Downloading/loading EdAcc without audio decoding...")
edacc = load_dataset("edinburghcstr/edacc")
print("Disabling Hugging Face audio decoding...")
for split in edacc.keys():
edacc[split] = edacc[split].cast_column("audio", Audio(decode=False))
print("Available splits:", list(edacc.keys()))
# EdAcc normally has validation and test splits.
# We use validation as our train subset and test as our val subset.
train_source = "validation"
val_source = "test"
save_split(
edacc[train_source],
"train",
args.out_train,
os.path.join(args.audio_root, "train"),
args.max_train,
)
save_split(
edacc[val_source],
"val",
args.out_val,
os.path.join(args.audio_root, "val"),
args.max_val,
)
print("Done.")
if __name__ == "__main__":
main() |