forked from Open-CT/openbrain
150 lines
4.5 KiB
Python
150 lines
4.5 KiB
Python
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import librosa
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import os
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from random import shuffle
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import numpy as np
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from sklearn import svm
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import joblib
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import sklearn
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import warnings
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warnings.filterwarnings('ignore')
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path = r'trainset/casia2'
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EMOTION_LABEL = {
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'angry': '1',
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'fear': '2',
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'happy': '3',
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'neutral': '4',
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'sad': '5',
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'surprise': '6',
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'interact':'7',
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'nouse':'8'
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}
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# C:误差项惩罚参数,对误差的容忍程度。C越大，越不能容忍误差
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# gamma：选择RBF函数作为kernel，越大，支持的向量越少；越小，支持的向量越多
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# kernel: linear, poly, rbf, sigmoid, precomputed
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# decision_function_shape: ovo, ovr(default)
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#
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# #
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'''
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这个模块包含了导入模块和svm模块
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导入模块需要librosa，始终有问题，草。
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'''
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def getFeature(path, mfcc_feature_num=16):
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y, sr = librosa.load(path)
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# 对于每一个音频文件提取其mfcc特征
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# y:音频时间序列;
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# n_mfcc:要返回的MFCC数量
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mfcc_feature = librosa.feature.mfcc(y, sr, n_mfcc=16)
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zcr_feature = librosa.feature.zero_crossing_rate(y)
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energy_feature = librosa.feature.rms(y)
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rms_feature = librosa.feature.rms(y)
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mfcc_feature = mfcc_feature.T.flatten()[:mfcc_feature_num]
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zcr_feature = zcr_feature.flatten()
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energy_feature = energy_feature.flatten()
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rms_feature = rms_feature.flatten()
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zcr_feature = np.array([np.mean(zcr_feature)])
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energy_feature = np.array([np.mean(energy_feature)])
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rms_feature = np.array([np.mean(rms_feature)])
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data_feature = np.concatenate((mfcc_feature, zcr_feature, energy_feature,
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rms_feature))
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return data_feature
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def getData(mfcc_feature_num=16):
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"""找到数据集中的所有语音文件的特征以及语音的情感标签"""
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wav_file_path = []
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person_dirs = os.listdir(path)
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for person in person_dirs:
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if person.endswith('txt'):
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continue
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emotion_dir_path = os.path.join(path, person)
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emotion_dirs = os.listdir(emotion_dir_path)
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for emotion_dir in emotion_dirs:
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if emotion_dir.endswith('.ini'):
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continue
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emotion_file_path = os.path.join(emotion_dir_path, emotion_dir)
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emotion_files = os.listdir(emotion_file_path)
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for file in emotion_files:
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if not file.endswith('wav'):
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continue
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wav_path = os.path.join(emotion_file_path, file)
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wav_file_path.append(wav_path)
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# 将语音文件随机排列
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shuffle(wav_file_path)
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data_feature = []
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data_labels = []
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for wav_file in wav_file_path:
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data_feature.append(getFeature(wav_file, mfcc_feature_num))
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data_labels.append(int(EMOTION_LABEL[wav_file.split('/')[-2]]))
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return np.array(data_feature), np.array(data_labels)
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def train():
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# 使用svm进行预测
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best_acc = 0
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best_mfcc_feature_num = 0
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best_C = 0
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for C in range(13, 20):
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for i in range(40, 55):
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data_feature, data_labels = getData(i)
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split_num = 200
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train_data = data_feature[:split_num, :]
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train_label = data_labels[:split_num]
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test_data = data_feature[split_num:, :]
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test_label = data_labels[split_num:]
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clf = svm.SVC(
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decision_function_shape='ovo',
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kernel='rbf',
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C=C,
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gamma=0.0001,
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probability=True)
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print("train start")
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clf.fit(train_data, train_label)
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print("train over")
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print(C, i)
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acc_dict = {}
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for test_x, test_y in zip(test_data, test_label):
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pre = clf.predict([test_x])[0]
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if pre in acc_dict.keys():
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continue
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acc_dict[pre] = test_y
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acc = sklearn.metrics.accuracy_score(
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clf.predict(test_data), test_label)
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if acc > best_acc:
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best_acc = acc
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best_C = C
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best_mfcc_feature_num = i
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print('best_acc', best_acc)
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print('best_C', best_C)
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print('best_mfcc_feature_num', best_mfcc_feature_num)
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print()
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# 保存模型
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joblib.dump(clf,
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'Models/C_' + str(C) + '_mfccNum_' + str(i) + '.m')
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print('most_best_acc', best_acc)
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print('best_C', best_C)
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print('best_mfcc_feature_num', best_mfcc_feature_num)
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if __name__ == "__main__":
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train()
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