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process_data.py

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+import os
+import shutil
+import random
+import argparse
+
+def split_dataset(source_dir, output_dir, split_ratio=0.8):
+    """
+    Split the dataset into training and validation sets.
+
+    :param source_dir: Directory containing the dataset with subfolders for each class.
+    :param output_dir: Base directory where train and val subdirectories will be created.
+    :param split_ratio: Ratio of the training set size to the total dataset size.
+    """
+    # 创建训练集和验证集目录
+    train_dir = os.path.join(output_dir, "train")
+    val_dir = os.path.join(output_dir, "val")
+    
+    if not os.path.exists(train_dir):
+        os.makedirs(train_dir)
+    if not os.path.exists(val_dir):
+        os.makedirs(val_dir)
+
+    for class_dir in os.listdir(source_dir):
+        class_path = os.path.join(source_dir, class_dir)
+        if os.path.isdir(class_path):
+            files = os.listdir(class_path)
+            random.shuffle(files)
+            split_point = int(len(files) * split_ratio)
+            train_files = files[:split_point]
+            val_files = files[split_point:]
+
+            train_class_dir = os.path.join(train_dir, class_dir)
+            val_class_dir = os.path.join(val_dir, class_dir)
+            if not os.path.exists(train_class_dir):
+                os.makedirs(train_class_dir)
+            if not os.path.exists(val_class_dir):
+                os.makedirs(val_class_dir)
+
+            for file in train_files:
+                shutil.copy(os.path.join(class_path, file), os.path.join(train_class_dir, file))
+            for file in val_files:
+                shutil.copy(os.path.join(class_path, file), os.path.join(val_class_dir, file))
+    
+    # 打印数据集统计信息
+    print(f"数据集分割完成:")
+    print(f"- 训练集路径: {train_dir}")
+    print(f"- 验证集路径: {val_dir}")
+    print(f"- 分割比例: {split_ratio:.2f} (训练) / {1-split_ratio:.2f} (验证)")
+    
+    return train_dir, val_dir
+
+if __name__ == "__main__":
+    # 创建命令行参数解析器
+    parser = argparse.ArgumentParser(description="将情感语音数据集分割为训练集和验证集")
+    parser.add_argument("--source", type=str, default="wav", 
+                      help="源数据目录 (默认: wav)")
+    parser.add_argument("--output", type=str, default="dataset", 
+                      help="输出基础目录，将在其中创建train和val子目录 (默认: dataset)")
+    parser.add_argument("--ratio", type=float, default=0.8, 
+                      help="训练集比例 (0-1之间的浮点数, 默认: 0.8)")
+    parser.add_argument("--seed", type=int, default=42, 
+                      help="随机种子 (默认: 无)")
+    parser.add_argument("--force", action="store_true", 
+                      help="如果目标目录已存在则强制删除")
+    
+    # 解析命令行参数
+    args = parser.parse_args()
+    
+    # 设置随机种子(如果提供)
+    if args.seed is not None:
+        random.seed(args.seed)
+        print(f"已设置随机种子: {args.seed}")
+    
+    # 检查分割比例是否有效
+    if args.ratio <= 0 or args.ratio >= 1:
+        parser.error("分割比例必须在0和1之间")
+    
+    # 构建实际的训练和验证目录路径
+    train_dir = os.path.join(args.output, "train")
+    val_dir = os.path.join(args.output, "val")
+    
+    # 处理目标目录
+    if args.force:
+        if os.path.exists(train_dir):
+            shutil.rmtree(train_dir)
+        if os.path.exists(val_dir):
+            shutil.rmtree(val_dir)
+    else:
+        # 检查目标目录是否已存在
+        if os.path.exists(train_dir) or os.path.exists(val_dir):
+            response = input(f"目标目录 ({train_dir} 或 {val_dir}) 已存在，是否继续并覆盖? (y/n): ")
+            if response.lower() != 'y':
+                print("操作已取消")
+                exit(0)
+            else:
+                if os.path.exists(train_dir):
+                    shutil.rmtree(train_dir)
+                if os.path.exists(val_dir):
+                    shutil.rmtree(val_dir)
+    
+    # 执行数据集分割
+    split_dataset(args.source, args.output, args.ratio)