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Running
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Zero
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import json
import argparse
from pathlib import Path
from typing import List, Dict, Set
from tqdm import tqdm
import soundfile as sf
from datasets import load_dataset
import logging
import os
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Prepare the CapSpeech dataset")
parser.add_argument('--hub', type=str, required=True, help='Huggingface repo')
parser.add_argument('--save_dir', type=str, required=True, help='Directory to save the JSON files')
parser.add_argument('--cache_dir', type=str, required=True, help='Cache directory for datasets')
parser.add_argument('--libriR_wav_dir', type=str, required=True, help='Directories containing WAV files')
parser.add_argument('--other_wav_dir', type=str, required=True, help='Directories containing WAV files')
parser.add_argument('--audio_min_length', type=float, default=3.0, help='Minimum audio duration in seconds')
parser.add_argument('--audio_max_length', type=float, default=18.0, help='Maximum audio duration in seconds')
parser.add_argument('--splits', type=str, nargs='+',
default=['train', 'val'],
help='List of splits to process')
parser.add_argument('--debug', action='store_true', help='Enable debug mode with limited data processing')
return parser.parse_args()
def setup_logging() -> None:
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s [%(levelname)s] %(message)s',
handlers=[
logging.StreamHandler()
]
)
def process_dataset_split(split, dataset_split, args) -> List[Dict]:
"""
Process a single dataset split and extract relevant records.
Args:
split: The name of the split (e.g., 'train').
dataset_split: The dataset split object.
args: Parsed command-line arguments.
Returns:
A list of dictionaries containing the processed records.
"""
logging.info(f"Processing split: {split}")
filelist: List[Dict] = []
total_duration: float = 0.0
num_samples: int = len(dataset_split) if not args.debug else 500
source_path = {
'libritts-r': args.libriR_wav_dir,
'voxceleb': args.other_wav_dir,
'expresso': args.other_wav_dir,
'ears': args.other_wav_dir,
'vctk': args.other_wav_dir,
}
for idx in tqdm(range(num_samples), desc=f"Processing {split}"):
try:
data = dataset_split[idx]
except IndexError:
logging.warning(f"Index {idx} out of range for split '{split}'. Skipping.")
continue
audio_path: str = data.get("audio_path", "")
duration: float = data.get("speech_duration", 0.0)
source: str = data.get("source", "")
audio_path = os.path.join(source_path[source], audio_path)
if not audio_path:
logging.warning(f"Missing audio_path at index {idx} in split '{split}'. Skipping.")
continue
if not os.path.exists(audio_path):
logging.warning(f"WAV file does not exist: {audio_path}")
continue
if not (args.audio_min_length <= duration <= args.audio_max_length):
continue
record: Dict = {
"segment_id": audio_path.split('/')[-1].split('.')[0],
"audio_path": audio_path,
"text": data.get('text', ''),
"caption": data.get('caption', ''),
"duration": duration,
"source": source
}
filelist.append(record)
total_duration += duration
logging.info(f"Total duration for split '{split}': {total_duration / 3600:.2f} hrs.")
logging.info(f"Total records for split '{split}': {len(filelist)}")
return filelist
def save_json(filelist: List[Dict], output_path: Path) -> None:
"""
Save the list of records to a JSON file.
Args:
filelist: List of dictionaries containing the records.
output_path: Path to the output JSON file.
"""
try:
with output_path.open('w', encoding='utf-8') as json_file:
json.dump(filelist, json_file, ensure_ascii=False, indent=4)
logging.info(f"Saved {len(filelist)} records to '{output_path}'")
except Exception as e:
logging.error(f"Failed to save JSON to '{output_path}': {e}")
def main() -> None:
args = parse_args()
setup_logging()
save_dir: Path = Path(args.save_dir)
jsons_dir: Path = save_dir / 'jsons'
jsons_dir.mkdir(parents=True, exist_ok=True)
logging.info(f"JSON files will be saved to '{jsons_dir}'")
logging.info("Loading dataset...")
try:
ds = load_dataset(args.hub)
# ds = load_dataset(args.hub, cache_dir=args.cache_dir)
except Exception as e:
logging.error(f"Failed to load dataset: {e}")
return
splits_to_process = args.splits
available_splits = set(ds.keys())
selected_splits = [split for split in splits_to_process if split in available_splits]
missing_splits = set(splits_to_process) - available_splits
if missing_splits:
logging.warning(f"The following splits were not found in the dataset and will be skipped: {missing_splits}")
for split in selected_splits:
dataset_split = ds[split]
filelist = process_dataset_split(split, dataset_split, args)
output_file: Path = jsons_dir / f"{split}.json"
save_json(filelist, output_file)
if __name__ == "__main__":
main()
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