chuban

data_utils/data_loader.py

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+"""
+数据加载模块，用于读取并处理语音情感数据集
+支持CASIA、SAVEE和RAVDESS三种数据集
+"""
+
+import os
+import glob
+import numpy as np
+import pandas as pd
+import librosa
+import re
+from sklearn.model_selection import train_test_split
+import warnings
+
+# 定义常量
+SAMPLE_RATE = 22050  # 统一采样率
+MAX_DURATION = 5     # 最大音频长度（秒）
+MAX_SAMPLES = SAMPLE_RATE * MAX_DURATION  # 最大样本数
+
+# 情绪映射字典
+EMOTION_MAPPING = {
+    # CASIA 情感映射
+    'angry': 'angry',
+    'fear': 'fear',
+    'happy': 'happy',
+    'neutral': 'neutral',
+    'sad': 'sad',
+    'surprise': 'surprise',
+    
+    # SAVEE 情感映射
+    'a': 'angry',
+    'f': 'fear',
+    'h': 'happy',
+    'n': 'neutral',
+    'sa': 'sad',
+    'su': 'surprise',
+    'd': 'disgust',  # 注意：SAVEE有厌恶情绪，但CASIA没有
+    
+    # RAVDESS 情感映射
+    '01': 'neutral',
+    '02': 'calm',    # 注意：RAVDESS有平静情绪，但CASIA没有
+    '03': 'happy',
+    '04': 'sad',
+    '05': 'angry',
+    '06': 'fear',
+    '07': 'disgust', # 注意：RAVDESS有厌恶情绪，但CASIA没有
+    '08': 'surprise'
+}
+
+# 语言映射
+LANGUAGE_MAPPING = {
+    'casia': 'zh',  # 中文
+    'savee': 'en',  # 英文
+    'ravdess': 'en'  # 英文
+}
+
+def load_casia(data_path):
+    """
+    加载CASIA中文情感语音数据集
+    
+    Args:
+        data_path: CASIA数据集路径
+    
+    Returns:
+        data_list: 包含(音频数据, 情感标签, 语言标签)的列表
+    """
+    data_list = []
+    
+    # 确保路径存在
+    if not os.path.exists(data_path):
+        print(f"警告: CASIA数据路径不存在: {data_path}")
+        return data_list
+        
+    # 尝试获取演员列表
+    try:
+        actors = os.listdir(data_path)
+    except Exception as e:
+        print(f"错误: 无法读取CASIA目录: {e}")
+        return data_list
+    
+    success_count = 0
+    error_count = 0
+    
+    for actor in actors:
+        # 跳过隐藏文件或非目录
+        if actor.startswith('_') or not os.path.isdir(os.path.join(data_path, actor)):
+            continue
+        
+        actor_path = os.path.join(data_path, actor)
+        emotions = os.listdir(actor_path)
+        
+        for emotion in emotions:
+            # 跳过隐藏文件或非目录
+            if emotion.startswith('_') or not os.path.isdir(os.path.join(actor_path, emotion)):
+                continue
+                
+            emotion_path = os.path.join(actor_path, emotion)
+            audio_files = glob.glob(os.path.join(emotion_path, "*.wav"))
+            
+            for audio_file in audio_files:
+                # 读取音频文件
+                try:
+                    audio, sr = librosa.load(audio_file, sr=SAMPLE_RATE, res_type='kaiser_fast')
+                    
+                    # 统一音频长度
+                    if len(audio) < MAX_SAMPLES:
+                        # 音频太短，用0填充
+                        padding = MAX_SAMPLES - len(audio)
+                        audio = np.pad(audio, (0, padding), 'constant')
+                    else:
+                        # 音频太长，截断
+                        audio = audio[:MAX_SAMPLES]
+                    
+                    data_list.append((audio, EMOTION_MAPPING[emotion], LANGUAGE_MAPPING['casia']))
+                    success_count += 1
+                    
+                except Exception as e:
+                    error_count += 1
+                    if error_count < 10:  # 只显示前10个错误，避免日志过多
+                        print(f"Error loading {audio_file}: {e}")
+                    elif error_count == 10:
+                        print("过多加载错误，后续错误将不再显示...")
+    
+    print(f"CASIA数据集: 成功加载 {success_count} 个文件，失败 {error_count} 个文件")
+    return data_list
+
+def load_savee(data_path):
+    """
+    加载SAVEE英文情感语音数据集
+    
+    Args:
+        data_path: SAVEE数据集路径
+    
+    Returns:
+        data_list: 包含(音频数据, 情感标签, 语言标签)的列表
+    """
+    data_list = []
+    
+    # 确保路径存在
+    if not os.path.exists(data_path):
+        print(f"警告: SAVEE数据路径不存在: {data_path}")
+        return data_list
+    
+    audio_path = os.path.join(data_path, "AudioData")
+    if not os.path.exists(audio_path):
+        print(f"警告: SAVEE AudioData路径不存在: {audio_path}")
+        return data_list
+    
+    # SAVEE数据集中的四个说话者
+    actors = ['DC', 'JE', 'JK', 'KL']
+    
+    success_count = 0
+    error_count = 0
+    
+    for actor in actors:
+        actor_path = os.path.join(audio_path, actor)
+        if not os.path.isdir(actor_path):
+            print(f"警告: SAVEE演员目录不存在: {actor_path}")
+            continue
+            
+        audio_files = glob.glob(os.path.join(actor_path, "*.wav"))
+        
+        for audio_file in audio_files:
+            file_name = os.path.basename(audio_file)
+            # 提取情感标签，SAVEE使用文件名的前1-2个字母作为情感标签
+            if file_name.startswith("sa"):
+                emotion = "sa"
+            elif file_name.startswith("su"):
+                emotion = "su"
+            else:
+                emotion = file_name[0]
+                
+            try:
+                audio, sr = librosa.load(audio_file, sr=SAMPLE_RATE, res_type='kaiser_fast')
+                
+                # 统一音频长度
+                if len(audio) < MAX_SAMPLES:
+                    padding = MAX_SAMPLES - len(audio)
+                    audio = np.pad(audio, (0, padding), 'constant')
+                else:
+                    audio = audio[:MAX_SAMPLES]
+                
+                data_list.append((audio, EMOTION_MAPPING[emotion], LANGUAGE_MAPPING['savee']))
+                success_count += 1
+                
+            except Exception as e:
+                error_count += 1
+                if error_count < 10:  # 只显示前10个错误
+                    print(f"Error loading {audio_file}: {e}")
+                elif error_count == 10:
+                    print("过多加载错误，后续错误将不再显示...")
+    
+    print(f"SAVEE数据集: 成功加载 {success_count} 个文件，失败 {error_count} 个文件")
+    return data_list
+
+def load_ravdess(data_path):
+    """
+    加载RAVDESS英文情感语音数据集
+    
+    Args:
+        data_path: RAVDESS数据集路径
+    
+    Returns:
+        data_list: 包含(音频数据, 情感标签, 语言标签)的列表
+    """
+    data_list = []
+    
+    # 确保路径存在
+    if not os.path.exists(data_path):
+        print(f"警告: RAVDESS数据路径不存在: {data_path}")
+        return data_list
+    
+    # 获取所有演员目录
+    try:
+        actor_dirs = glob.glob(os.path.join(data_path, "Actor_*"))
+    except Exception as e:
+        print(f"错误: 无法获取RAVDESS演员目录: {e}")
+        return data_list
+    
+    if not actor_dirs:
+        print(f"警告: RAVDESS演员目录为空: {data_path}")
+    
+    success_count = 0
+    error_count = 0
+    
+    for actor_dir in actor_dirs:
+        audio_files = glob.glob(os.path.join(actor_dir, "*.wav"))
+        
+        for audio_file in audio_files:
+            file_name = os.path.basename(audio_file)
+            
+            # RAVDESS文件名格式: 03-01-05-01-02-01-12.wav
+            # 05 表示情感类别 (angry)
+            parts = file_name.split('-')
+            if len(parts) >= 3:
+                emotion = parts[2]
+                
+                try:
+                    audio, sr = librosa.load(audio_file, sr=SAMPLE_RATE, res_type='kaiser_fast')
+                    
+                    # 统一音频长度
+                    if len(audio) < MAX_SAMPLES:
+                        padding = MAX_SAMPLES - len(audio)
+                        audio = np.pad(audio, (0, padding), 'constant')
+                    else:
+                        audio = audio[:MAX_SAMPLES]
+                    
+                    data_list.append((audio, EMOTION_MAPPING[emotion], LANGUAGE_MAPPING['ravdess']))
+                    success_count += 1
+                    
+                except Exception as e:
+                    error_count += 1
+                    if error_count < 10:  # 只显示前10个错误
+                        print(f"Error loading {audio_file}: {e}")
+                    elif error_count == 10:
+                        print("过多加载错误，后续错误将不再显示...")
+    
+    print(f"RAVDESS数据集: 成功加载 {success_count} 个文件，失败 {error_count} 个文件")
+    return data_list
+
+def load_all_data(casia_path, savee_path, ravdess_path, selected_emotions=None):
+    """
+    加载所有数据集
+    
+    Args:
+        casia_path: CASIA数据集路径
+        savee_path: SAVEE数据集路径
+        ravdess_path: RAVDESS数据集路径
+        selected_emotions: 要使用的情感列表，如果为None，则使用所有共有的情感
+    
+    Returns:
+        X: 音频数据列表
+        y_emotion: 情感标签列表
+        y_language: 语言标签列表
+    """
+    print("加载CASIA数据集...")
+    casia_data = load_casia(casia_path)
+    
+    print("加载SAVEE数据集...")
+    savee_data = load_savee(savee_path)
+    
+    print("加载RAVDESS数据集...")
+    ravdess_data = load_ravdess(ravdess_path)
+    
+    # 合并所有数据
+    all_data = casia_data + savee_data + ravdess_data
+    
+    # 检查是否有数据被加载
+    if not all_data:
+        raise ValueError("没有成功加载任何数据！请检查数据路径和文件格式。")
+    
+    # 如果指定了要使用的情感列表，筛选数据
+    if selected_emotions:
+        filtered_data = [item for item in all_data if item[1] in selected_emotions]
+        if not filtered_data:
+            print(f"警告: 筛选后没有匹配的情感数据。可用的情感标签: {set(item[1] for item in all_data)}")
+            print(f"您请求的情感标签: {selected_emotions}")
+            # 回退到使用所有数据
+            filtered_data = all_data
+        all_data = filtered_data
+    
+    print(f"总共加载了 {len(all_data)} 个有效音频文件")
+    
+    # 显示各情感类别的数据分布
+    emotion_counts = {}
+    for item in all_data:
+        emotion = item[1]
+        if emotion in emotion_counts:
+            emotion_counts[emotion] += 1
+        else:
+            emotion_counts[emotion] = 1
+            
+    print("数据分布:")
+    for emotion, count in emotion_counts.items():
+        print(f"  {emotion}: {count} 个样本")
+    
+    # 分离数据、情感标签和语言标签
+    X = [item[0] for item in all_data]
+    y_emotion = [item[1] for item in all_data]
+    y_language = [item[2] for item in all_data]
+    
+    return X, y_emotion, y_language
+
+def prepare_data(X, y_emotion, y_language, test_size=0.2, val_size=0.2, random_state=42):
+    """
+    准备训练集、验证集和测试集
+    
+    Args:
+        X: 音频数据
+        y_emotion: 情感标签
+        y_language: 语言标签
+        test_size: 测试集比例
+        val_size: 验证集比例
+        random_state: 随机种子
+        
+    Returns:
+        训练集、验证集和测试集数据和标签
+    """
+    # 确保数据不为空
+    if len(X) == 0:
+        raise ValueError("数据集为空，无法进行划分！请确保至少加载了一些有效的音频文件。")
+    
+    # 先划分出测试集
+    X_train_val, X_test, y_emotion_train_val, y_emotion_test, y_language_train_val, y_language_test = train_test_split(
+        X, y_emotion, y_language, test_size=test_size, random_state=random_state, stratify=y_emotion
+    )
+    
+    # 从剩余数据中划分训练集和验证集
+    val_ratio = val_size / (1 - test_size)
+    X_train, X_val, y_emotion_train, y_emotion_val, y_language_train, y_language_val = train_test_split(
+        X_train_val, y_emotion_train_val, y_language_train_val, 
+        test_size=val_ratio, random_state=random_state, stratify=y_emotion_train_val
+    )
+    
+    # 打印数据集大小
+    print(f"数据集划分: 训练集 {len(X_train)} 个样本, 验证集 {len(X_val)} 个样本, 测试集 {len(X_test)} 个样本")
+    
+    return (
+        X_train, y_emotion_train, y_language_train,
+        X_val, y_emotion_val, y_language_val,
+        X_test, y_emotion_test, y_language_test
+    )