Datasets:
File size: 3,024 Bytes
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"""
Extract tar shards back to original structure
"""
import argparse
import tarfile
import csv
from pathlib import Path
from tqdm import tqdm
from collections import defaultdict
import shutil
def parse_args():
parser = argparse.ArgumentParser(
description="Extract tar shards to recreate dataset"
)
parser.add_argument(
"--sharded-dir",
type=Path,
default=".",
help="Directory containing audio/ and metadata/ folders"
)
parser.add_argument(
"--output-dir",
type=Path,
default="extracted",
help="Output directory for extracted dataset"
)
return parser.parse_args()
def main():
args = parse_args()
audio_dir = args.sharded_dir / "audio"
metadata_file = args.sharded_dir / "metadata" / "metadata.csv"
if not audio_dir.exists():
raise FileNotFoundError(f"Audio directory not found: {audio_dir}")
if not metadata_file.exists():
raise FileNotFoundError(f"Metadata not found: {metadata_file}")
# Read metadata to understand structure
print("Reading metadata...")
with open(metadata_file, 'r', encoding='utf-8') as f:
reader = csv.DictReader(f)
rows = list(reader)
# Group by subset and shard
shards_by_subset = defaultdict(set)
for row in rows:
subset = row['subset']
shard = row['shard']
shards_by_subset[subset].add(shard)
print(f"Found {len(rows):,} samples across {len(shards_by_subset)} subsets")
# Extract all shards
for subset in sorted(shards_by_subset.keys()):
print(f"\nExtracting {subset}...")
subset_output = args.output_dir / subset / "audio"
subset_output.mkdir(parents=True, exist_ok=True)
shards = sorted(shards_by_subset[subset])
for shard_name in tqdm(shards, desc=f" {subset}"):
shard_path = audio_dir / subset / shard_name
if not shard_path.exists():
print(f"Warning: {shard_path} not found, skipping")
continue
# Extract tar
with tarfile.open(shard_path, 'r') as tar:
tar.extractall(path=subset_output)
metadata_dir = Path(args.sharded_dir) / "metadata"
output_dir = Path(args.output_dir)
# Create the output directory if it does not exist
output_dir.mkdir(parents=True, exist_ok=True)
# Copy every CSV file
for csv_file in metadata_dir.glob("*.csv"):
destination = output_dir / csv_file.name
shutil.copy2(csv_file, destination)
print("\n" + "="*60)
print("Extraction complete!")
print("="*60)
print(f"Output directory: {args.output_dir}")
print(f"\nTo process the dataset:")
print(f"python process_dataset.py \\")
print(f" --taskmaster-root-dir {args.output_dir} \\")
print(f" --aug-dataset-save-dir ./processed \\")
print(f" --normalize-text")
if __name__ == "__main__":
main()
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