| 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': |
| |
| pack_to_parquet(json_path, audio_dir, tokens_dir, output_parquet_dir, batch_size=1000) |
| elif args.mode == 'restore': |
| |
| restore_from_parquet(output_parquet_dir, audio_dir, tokens_dir) |
|
|
| if __name__ == '__main__': |
| main() |
