Improve: transc order by path, train.list preserves order

preprocess_text.py

@@ -2,7 +2,7 @@ import argparse
 import json
 from collections import defaultdict
 from pathlib import Path
-from random import shuffle
+from random import shuffle, sample
 from typing import Optional
 
 from tqdm import tqdm
@@ -156,16 +156,26 @@ def preprocess(
     train_list: list[str] = []
     val_list: list[str] = []
 
-    # 各話者ごとにシャッフルして、val_per_lang個をval_listに、残りをtrain_listに追加
+    # 各話者ごとに発話リストを処理
     for spk, utts in spk_utt_map.items():
-        shuffle(utts)
+        if val_per_lang == 0:
-        val_list += utts[:val_per_lang]
+            train_list.extend(utts)
-        train_list += utts[val_per_lang:]
+            continue
+        # ランダムにval_per_lang個のインデックスを選択
+        val_indices = set(sample(range(len(utts)), val_per_lang))
+        # 元の順序を保ちながらリストを分割
+        for index, utt in enumerate(utts):
+            if index in val_indices:
+                val_list.append(utt)
+            else:
+                train_list.append(utt)
 
-    shuffle(val_list)
+    # バリデーションリストのサイズ調整
     if len(val_list) > max_val_total:
-        train_list += val_list[max_val_total:]
+        extra_val = val_list[max_val_total:]
         val_list = val_list[:max_val_total]
+        # 余剰のバリデーション発話をトレーニングリストに追加（元の順序を保持）
+        train_list.extend(extra_val)
 
     with train_path.open("w", encoding="utf-8") as f:
         for line in train_list:

transcribe.py

@@ -152,7 +152,7 @@ if __name__ == "__main__":
     output_file.parent.mkdir(parents=True, exist_ok=True)
 
     wav_files = [f for f in input_dir.rglob("*.wav") if f.is_file()]
-    wav_files = sorted(wav_files, key=lambda x: x.name)
+    wav_files = sorted(wav_files, key=lambda x: str(x))
 
     if output_file.exists():
         logger.warning(f"{output_file} exists, backing up to {output_file}.bak")