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predict.py
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277
predict.py
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"""
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预测脚本
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用于对单个音频文件进行情感预测
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"""
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import os
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import pickle
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import numpy as np
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import librosa
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from tensorflow.keras.models import load_model
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import sys
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# 导入自定义模块
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from data_utils.feature_extractor import (
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extract_features, features_to_matrix,
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normalize_features_with_params, reshape_for_lstm
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)
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def load_model_files(model_dir):
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"""
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加载模型和相关文件
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Args:
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model_dir: 模型目录
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Returns:
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model: Keras模型
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emotion_encoder: 情感标签编码器
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feature_scaler: 特征缩放器
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language_encoder: 语言标签编码器（如果有）
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feature_names: 特征名称列表
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"""
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try:
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# 确保模型目录存在
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if not os.path.exists(model_dir):
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os.makedirs(model_dir, exist_ok=True)
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print(f"创建模型目录: {model_dir}")
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# 加载模型
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model_path = os.path.join(model_dir, 'emotion_model.h5')
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if not os.path.exists(model_path):
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# 尝试加载权重文件
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weights_path = os.path.join(model_dir, 'best_model_weights.h5')
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if not os.path.exists(weights_path):
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raise FileNotFoundError(f"无法找到模型文件: {model_path} 或权重文件: {weights_path}")
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# 尝试从配置重建模型，然后加载权重
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config_path = os.path.join(model_dir, 'config.pkl')
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if os.path.exists(config_path):
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with open(config_path, 'rb') as f:
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config = pickle.load(f)
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from models.lstm_model import EmotionModel
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# 尝试获取必要的参数
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try:
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input_shape = (1, config.get('num_features', 193)) # 默认特征数
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num_emotions = config.get('num_emotions', 6) # 默认情感类别数
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include_language = config.get('include_language', False)
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num_languages = 2 # 默认语言数
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model = EmotionModel(
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input_shape=input_shape,
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num_emotions=num_emotions,
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num_languages=num_languages if include_language else None,
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include_language=include_language
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)
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model.build_model()
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model.compile_model()
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model.model.load_weights(weights_path)
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print(f"已从权重文件加载模型: {weights_path}")
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except Exception as e:
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raise ValueError(f"从配置重建模型失败: {e}")
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else:
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raise FileNotFoundError(f"找不到模型配置文件: {config_path}")
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else:
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model = load_model(model_path)
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print(f"已加载模型: {model_path}")
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# 加载情感编码器
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encoder_path = os.path.join(model_dir, 'emotion_encoder.pkl')
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if not os.path.exists(encoder_path):
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raise FileNotFoundError(f"找不到情感编码器文件: {encoder_path}")
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with open(encoder_path, 'rb') as f:
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emotion_encoder = pickle.load(f)
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# 加载特征名称，用于确保特征提取一致性
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feature_names_path = os.path.join(model_dir, 'feature_names.pkl')
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if os.path.exists(feature_names_path):
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with open(feature_names_path, 'rb') as f:
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feature_names = pickle.load(f)
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else:
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feature_names = None
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print("警告: 找不到特征名称文件，可能影响预测准确性")
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# 检查是否有语言编码器
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language_encoder_path = os.path.join(model_dir, 'language_encoder.pkl')
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if os.path.exists(language_encoder_path):
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with open(language_encoder_path, 'rb') as f:
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language_encoder = pickle.load(f)
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print("检测到多语言模型")
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else:
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language_encoder = None
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# 加载特征缩放参数
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scaler_path = os.path.join(model_dir, 'feature_scaler.pkl')
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if os.path.exists(scaler_path):
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with open(scaler_path, 'rb') as f:
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feature_scaler = pickle.load(f)
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else:
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print("警告: 找不到特征缩放参数文件，将使用默认缩放")
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feature_scaler = None
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return model, emotion_encoder, feature_scaler, language_encoder, feature_names
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except Exception as e:
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print(f"加载模型文件时出错: {e}")
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raise
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def predict_emotion(audio_file, model_dir):
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"""
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预测单个音频文件的情感
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Args:
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audio_file: 音频文件路径
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model_dir: 模型目录
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Returns:
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dict: 预测结果，包含情感标签和概率
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"""
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try:
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# 检查文件是否存在
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if not os.path.exists(audio_file):
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raise FileNotFoundError(f"音频文件不存在: {audio_file}")
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# 加载模型和相关文件
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model, emotion_encoder, feature_scaler, language_encoder, feature_names = load_model_files(model_dir)
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# 加载音频
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print(f"加载音频文件: {audio_file}")
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try:
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audio, sr = librosa.load(audio_file, sr=22050, res_type='kaiser_fast')
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except Exception as e:
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raise ValueError(f"加载音频文件失败: {e}")
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# 提取特征
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print("提取音频特征...")
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features = extract_features(audio)
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# 转换为特征矩阵
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X, _ = features_to_matrix([features], feature_names=feature_names)
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# 标准化特征
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if feature_scaler:
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X_norm = normalize_features_with_params(X, feature_scaler)
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else:
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# 如果没有保存的缩放参数，使用简单标准化
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X_norm = (X - np.mean(X, axis=0)) / (np.std(X, axis=0) + 1e-10)
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# 重塑为LSTM输入格式
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X_reshaped = reshape_for_lstm(X_norm)
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# 预测
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print("进行预测...")
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if language_encoder:
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# 如果是多语言模型，需要提供语言特征
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# 这里假设无法确定语言，将尝试所有可能的语言
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languages = language_encoder.classes_
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best_prob = -1
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best_emotion = None
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best_language = None
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best_emotion_probs = None
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best_language_prob = None
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for lang in languages:
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# 为当前语言创建独热编码
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lang_idx = language_encoder.transform([lang])[0]
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lang_one_hot = np.zeros((1, len(languages)))
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lang_one_hot[0, lang_idx] = 1
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# 预测
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probs = model.predict([X_reshaped, lang_one_hot])[0]
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# 找出最可能的情感
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emotion_idx = np.argmax(probs)
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emotion_prob = probs[emotion_idx]
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if emotion_prob > best_prob:
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best_prob = emotion_prob
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best_emotion = emotion_encoder.classes_[emotion_idx]
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best_language = lang
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best_emotion_probs = {emotion: float(probs[i]) for i, emotion in enumerate(emotion_encoder.classes_)}
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best_language_prob = 1.0 # 假设语言预测的置信度
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result = {
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'emotion': best_emotion,
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'probability': float(best_prob),
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'language': best_language,
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'language_probability': float(best_language_prob),
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'all_emotions': best_emotion_probs
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}
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else:
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# 单语言模型
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try:
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probs = model.predict(X_reshaped)[0]
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emotion_idx = np.argmax(probs)
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emotion = emotion_encoder.classes_[emotion_idx]
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probability = probs[emotion_idx]
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# 构建结果
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all_emotions = {emotion: float(probs[i]) for i, emotion in enumerate(emotion_encoder.classes_)}
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result = {
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'emotion': emotion,
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'probability': float(probability),
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'all_emotions': all_emotions
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}
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except Exception as e:
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print(f"预测过程中出错: {e}")
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# 尝试使用模型的model属性（如果是自定义EmotionModel类）
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try:
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if hasattr(model, 'model'):
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probs = model.model.predict(X_reshaped)[0]
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emotion_idx = np.argmax(probs)
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emotion = emotion_encoder.classes_[emotion_idx]
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probability = probs[emotion_idx]
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all_emotions = {emotion: float(probs[i]) for i, emotion in enumerate(emotion_encoder.classes_)}
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result = {
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'emotion': emotion,
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'probability': float(probability),
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'all_emotions': all_emotions
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}
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else:
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raise
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except:
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raise ValueError(f"模型预测失败: {e}")
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return result
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except Exception as e:
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print(f"预测过程中出错: {e}")
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import traceback
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traceback.print_exc()
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raise
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description='预测音频情感')
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parser.add_argument('--audio', type=str, required=True,
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help='音频文件路径')
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parser.add_argument('--model', type=str, default='./output/emotion_model',
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help='模型目录')
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args = parser.parse_args()
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try:
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result = predict_emotion(args.audio, args.model)
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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"错误: {e}")
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sys.exit(1)
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