Upload pack_restore_parquet.py with huggingface_hub

pack_restore_parquet.py

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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()