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()