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import pandas as pd
import pyarrow.parquet as pq
import pyarrow as pa
import os
import json
import argparse
from tqdm import tqdm
def pack_to_parquet(json_path, audio_dir, tokens_dir, output_dir, batch_size=1000):
os.makedirs(output_dir, exist_ok=True)
with open(json_path, 'r') as f:
data = json.load(f)
schema = pa.schema([
('id', pa.string()),
('speech_path', pa.string()),
('units_path', pa.string()),
('audio_data', pa.binary()),
('tokens_data', pa.binary())
])
records = []
batch_count = 0
for item in tqdm(data, desc="Processing records"):
speech_filename = os.path.basename(item['speech'])
units_filename = os.path.basename(item['units'])
audio_path = os.path.join(audio_dir, speech_filename)
tokens_path = os.path.join(tokens_dir, units_filename)
audio_data = None
tokens_data = None
if os.path.exists(audio_path):
with open(audio_path, 'rb') as f:
audio_data = f.read()
if os.path.exists(tokens_path):
with open(tokens_path, 'rb') as f:
tokens_data = f.read()
record = {
'id': item['id'],
'speech_path': speech_filename,
'units_path': units_filename,
'audio_data': audio_data,
'tokens_data': tokens_data
}
records.append(record)
if len(records) >= batch_size:
df = pd.DataFrame(records)
table = pa.Table.from_pandas(df, schema=schema)
output_parquet = os.path.join(output_dir, f'batch_{batch_count}.parquet')
pq.write_table(table, output_parquet)
print(f"Parquet file saved to: {output_parquet}")
batch_count += 1
records = []
if records:
df = pd.DataFrame(records)
table = pa.Table.from_pandas(df, schema=schema)
output_parquet = os.path.join(output_dir, f'batch_{batch_count}.parquet')
pq.write_table(table, output_parquet)
print(f"Parquet file saved to: {output_parquet}")
def restore_from_parquet(parquet_dir, output_audio_dir, output_tokens_dir):
os.makedirs(output_audio_dir, exist_ok=True)
os.makedirs(output_tokens_dir, exist_ok=True)
parquet_files = [f for f in os.listdir(parquet_dir) if f.endswith('.parquet')]
for parquet_file in tqdm(parquet_files, desc="Restoring Parquet files"):
parquet_path = os.path.join(parquet_dir, parquet_file)
table = pq.read_table(parquet_path)
df = table.to_pandas()
for _, row in df.iterrows():
if row['audio_data'] is not None:
audio_path = os.path.join(output_audio_dir, row['speech_path'])
with open(audio_path, 'wb') as f:
f.write(row['audio_data'])
if row['tokens_data'] is not None:
tokens_path = os.path.join(output_tokens_dir, row['units_path'])
with open(tokens_path, 'wb') as f:
f.write(row['tokens_data'])
print(f"Files restored to: {output_audio_dir} and {output_tokens_dir}")
def main():
parser = argparse.ArgumentParser(description='Pack or restore audio and token files using Parquet.')
parser.add_argument('--mode', choices=['pack', 'restore'], required=True, help='Mode to run: "pack" to create Parquet files, "restore" to restore files')
args = parser.parse_args()
json_path = 'VoiceAssistant-430K.json'
audio_dir = 'audios'
tokens_dir = 'cosyvoice2_tokens'
output_parquet_dir = 'cosyvoice2_tokens_and_audios_parquet_files'
if args.mode == 'pack':
# python pack_restore_parquet.py --mode pack
pack_to_parquet(json_path, audio_dir, tokens_dir, output_parquet_dir, batch_size=1000)
elif args.mode == 'restore':
# python pack_restore_parquet.py --mode restore
restore_from_parquet(output_parquet_dir, audio_dir, tokens_dir)
if __name__ == '__main__':
main() |