-
Notifications
You must be signed in to change notification settings - Fork 220
/
Copy pathopensmile.py
162 lines (132 loc) · 5.38 KB
/
opensmile.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
import os
import csv
import sys
from typing import Tuple, Union
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import joblib
import utils
# 每个特征集的特征数量
FEATURE_NUM = {
'IS09_emotion': 384,
'IS10_paraling': 1582,
'IS11_speaker_state': 4368,
'IS12_speaker_trait': 6125,
'IS13_ComParE': 6373,
'ComParE_2016': 6373
}
def get_feature_opensmile(config, filepath: str) -> list:
"""
用 Opensmile 提取一个音频的特征
Args:
config: 配置项
file_path (str): 音频路径
Returns:
vector (list): 该音频的特征向量
"""
# 项目路径
BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))
# single_feature.csv 路径
single_feat_path = os.path.join(BASE_DIR, config.feature_folder, 'single_feature.csv')
# Opensmile 配置文件路径
opensmile_config_path = os.path.join(config.opensmile_path, 'config', config.opensmile_config + '.conf')
# Opensmile 命令
cmd = 'cd ' + config.opensmile_path + ' && ./SMILExtract -C ' + opensmile_config_path + ' -I ' + filepath + ' -O ' + single_feat_path + ' -appendarff 0'
print("Opensmile cmd: ", cmd)
os.system(cmd)
reader = csv.reader(open(single_feat_path,'r'))
rows = [row for row in reader]
last_line = rows[-1]
return last_line[1: FEATURE_NUM[config.opensmile_config] + 1]
def load_feature(config, train: bool) -> Union[Tuple[np.ndarray], np.ndarray]:
"""
从 "{config.feature_folder}/*.csv" 文件中加载特征数据
Args:
config: 配置项
train (bool): 是否为训练数据
Returns:
- X (Tuple[np.ndarray]): 训练特征、测试特征和对应的标签
- X (np.ndarray): 预测特征
"""
feature_path = os.path.join(config.feature_folder, "train.csv" if train == True else "predict.csv")
# 加载特征数据
df = pd.read_csv(feature_path)
features = [str(i) for i in range(1, FEATURE_NUM[config.opensmile_config] + 1)]
X = df.loc[:,features].values
Y = df.loc[:,'label'].values
# 标准化模型路径
scaler_path = os.path.join(config.checkpoint_path, 'SCALER_OPENSMILE.m')
if train == True:
# 标准化数据
scaler = StandardScaler().fit(X)
# 保存标准化模型
utils.mkdirs(config.checkpoint_path)
joblib.dump(scaler, scaler_path)
X = scaler.transform(X)
# 划分训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 42)
return x_train, x_test, y_train, y_test
else:
# 标准化数据
# 加载标准化模型
scaler = joblib.load(scaler_path)
X = scaler.transform(X)
return X
def get_data(config, data_path: str, train: bool) -> Union[Tuple[np.ndarray], np.ndarray]:
"""
用 Opensmile 提取所有音频的特征: 遍历所有文件夹, 读取每个文件夹中的音频, 提取每个音频的
特征，把所有特征保存在 "{config.feature_path}/*.csv" 文件中。
Args:
config: 配置项
data_path (str): 数据集文件夹 / 测试文件路径
train (bool): 是否为训练数据
Returns:
- train = True: 训练特征、测试特征和对应的标签
- train = False: 预测特征
"""
# 如果 config.feature_folder 文件夹不存在，则新建一个
utils.mkdirs(config.feature_folder)
# 特征存储路径
feature_path = os.path.join(config.feature_folder, "train.csv" if train == True else "predict.csv")
# 写表头
writer = csv.writer(open(feature_path, 'w'))
first_row = ['label']
for i in range(1, FEATURE_NUM[config.opensmile_config] + 1):
first_row.append(str(i))
writer.writerow(first_row)
writer = csv.writer(open(feature_path, 'a+'))
print('Opensmile extracting...')
if train == True:
cur_dir = os.getcwd()
sys.stderr.write('Curdir: %s\n' % cur_dir)
os.chdir(data_path)
# 遍历文件夹
for i, directory in enumerate(config.class_labels):
sys.stderr.write("Started reading folder %s\n" % directory)
os.chdir(directory)
# label_name = directory
label = config.class_labels.index(directory)
# 读取该文件夹下的音频
for filename in os.listdir('.'):
if not filename.endswith('wav'):
continue
filepath = os.path.join(os.getcwd(), filename)
# 提取该音频的特征
feature_vector = get_feature_opensmile(config, filepath)
feature_vector.insert(0, label)
# 把每个音频的特征整理到一个 csv 文件中
writer.writerow(feature_vector)
sys.stderr.write("Ended reading folder %s\n" % directory)
os.chdir('..')
os.chdir(cur_dir)
else:
feature_vector = get_feature_opensmile(config, data_path)
feature_vector.insert(0, '-1')
writer.writerow(feature_vector)
print('Opensmile extract done.')
# 一个玄学 bug 的暂时性解决方案
# 这里无法直接加载除了 IS10_paraling 以外的其他特征集的预测数据特征，非常玄学
if train == True:
return load_feature(config, train=train)