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