chuban
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217
main.py
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217
main.py
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"""
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多语言语音情感识别系统主程序
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"""
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import os
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import argparse
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import sys
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import importlib
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def check_dependencies():
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"""
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检查所需的Python依赖是否安装
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Returns:
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bool: 如果所有依赖都已安装，则返回True
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"""
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required_packages = [
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'numpy', 'pandas', 'matplotlib', 'librosa',
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'sklearn', 'tensorflow', 'tqdm', 'soundfile',
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'resampy' # 添加resampy作为必需依赖
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]
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missing_packages = []
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for package in required_packages:
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try:
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importlib.import_module(package)
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except ImportError:
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missing_packages.append(package)
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if missing_packages:
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print("错误: 缺少以下Python依赖包:")
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for pkg in missing_packages:
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print(f" - {pkg}")
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print("\n请使用以下命令安装缺少的依赖:")
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print(f"pip install {' '.join(missing_packages)}")
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return False
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return True
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def parse_args():
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"""
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解析命令行参数
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"""
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parser = argparse.ArgumentParser(description='多语言语音情感识别系统')
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# 模式选择
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parser.add_argument('--mode', type=str, default='train',
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choices=['train', 'predict'],
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help='运行模式: train-训练模型, predict-使用模型预测')
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# 数据路径
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parser.add_argument('--casia_path', type=str, default='./CAISA',
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help='CASIA数据集路径')
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parser.add_argument('--savee_path', type=str, default='./SAVEE',
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help='SAVEE数据集路径')
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parser.add_argument('--ravdess_path', type=str, default='./RAVDESS',
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help='RAVDESS数据集路径')
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# 模型参数
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parser.add_argument('--lstm_units', type=int, default=128,
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help='LSTM层的单元数')
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parser.add_argument('--dropout', type=float, default=0.3,
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help='Dropout比率')
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parser.add_argument('--learning_rate', type=float, default=0.001,
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help='学习率')
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parser.add_argument('--batch_size', type=int, default=32,
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help='批量大小')
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parser.add_argument('--epochs', type=int, default=100,
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help='训练轮数')
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# 预测参数
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parser.add_argument('--audio_file', type=str, default=None,
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help='要预测的音频文件路径')
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parser.add_argument('--model_path', type=str, default='./output/emotion_model',
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help='模型路径')
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# 其他参数
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parser.add_argument('--include_language', action='store_true',
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help='是否将语言作为额外特征')
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parser.add_argument('--output_dir', type=str, default='./output',
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help='输出目录')
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parser.add_argument('--seed', type=int, default=42,
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help='随机种子')
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parser.add_argument('--regularization', type=float, default=0.001,
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help='L2正则化系数')
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parser.add_argument('--model_name', type=str, default='emotion_model',
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help='模型名称')
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args = parser.parse_args()
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return args
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def verify_file_exists(file_path, description="文件"):
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"""
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验证文件是否存在
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Args:
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file_path: 文件路径
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description: 文件描述
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Returns:
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bool: 文件存在则返回True，否则返回False
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"""
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if not os.path.exists(file_path):
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print(f"错误: {description}不存在: {file_path}")
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return False
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return True
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def main():
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"""
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主函数
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"""
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# 检查依赖
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if not check_dependencies():
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print("请安装必要的依赖后再运行程序。")
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sys.exit(1)
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# 解析命令行参数
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args = parse_args()
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# 根据模式选择执行的功能
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if args.mode == 'train':
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# 导入训练模块
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try:
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from train import train_model
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except ImportError as e:
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print(f"错误: 无法导入训练模块: {e}")
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sys.exit(1)
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print("="*50)
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print("多语言语音情感识别系统 - 训练模式")
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print("="*50)
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print(f"CASIA路径: {args.casia_path}")
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print(f"SAVEE路径: {args.savee_path}")
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print(f"RAVDESS路径: {args.ravdess_path}")
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print(f"输出目录: {args.output_dir}")
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print("-"*50)
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# 检查至少有一个数据集路径存在
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has_valid_path = False
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if os.path.exists(args.casia_path):
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has_valid_path = True
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if os.path.exists(args.savee_path):
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has_valid_path = True
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if os.path.exists(args.ravdess_path):
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has_valid_path = True
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if not has_valid_path:
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print("错误: 未找到任何有效的数据集路径，请检查--casia_path、--savee_path、--ravdess_path参数")
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print("提示: 路径可以是绝对路径，例如 C:/数据集/CASIA")
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sys.exit(1)
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# 开始训练
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try:
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train_model(args)
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except Exception as e:
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print(f"训练过程中出现错误: {e}")
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import traceback
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traceback.print_exc()
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sys.exit(1)
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elif args.mode == 'predict':
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# 导入预测模块
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try:
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from predict import predict_emotion
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except ImportError as e:
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print(f"错误: 无法导入预测模块: {e}")
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sys.exit(1)
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# 检查音频文件是否存在
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if not args.audio_file:
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print("错误: 预测模式下需要指定音频文件，请使用--audio_file参数")
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sys.exit(1)
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if not verify_file_exists(args.audio_file, "音频文件"):
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sys.exit(1)
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# 检查模型目录是否存在
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model_path = os.path.join(args.output_dir, args.model_name)
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if not os.path.exists(model_path):
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print(f"警告: 模型目录不存在: {model_path}，将尝试使用指定的model_path参数: {args.model_path}")
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model_path = args.model_path
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print("="*50)
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print("多语言语音情感识别系统 - 预测模式")
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print("="*50)
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print(f"音频文件: {args.audio_file}")
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print(f"模型路径: {model_path}")
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print("-"*50)
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# 开始预测
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try:
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result = predict_emotion(args.audio_file, model_path)
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print("\n预测结果:")
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print(f"情感: {result['emotion']}")
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print(f"置信度: {result['probability']:.2f}")
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if 'language' in result:
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print(f"语言: {result['language']}")
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print(f"语言置信度: {result['language_probability']:.2f}")
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print("\n情感概率分布:")
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for emotion, prob in result['all_emotions'].items():
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print(f" {emotion}: {prob:.4f}")
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except Exception as e:
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print(f"预测过程中出现错误: {e}")
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import traceback
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traceback.print_exc()
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sys.exit(1)
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else:
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print(f"错误: 未知的模式 '{args.mode}'")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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