Question

我想创建一个在RAVDESS数据集（https://smartlaboratory.org/ravdess/）上训练过的神经网络：想法是使用该数据集来检测与我的应用程序的麦克风说话的人的情绪。

使用下面的librosa和for循环，我提取了要用于分析的标签和功能。

# I started with only one folder to fasten the operations
oneActorPath = '/content/drive/My Drive/RAVDESS/Audio_Speech_Actors_01-24/Actor_01/'
lst = []

# Loop through each folder to find the wavs

for subdir, dirs, files in os.walk(oneActorPath):
  for file in files:
    if file == '.DS_Store':
      continue
    else:
      # Check if the format of the file is valid
      try:
        #Load librosa array
        data, rate = librosa.load(os.path.join(subdir,file))
        #Using the name of the file I can understand the emotion that contains
        file = file[6:8]
        arr = data, file
        lst.append(arr)
        #print(list)

      # If is not valid, skip it
      except ValueError:
        continue

此循环的输出是以下格式的数组列表：

[(array([-8.1530527e-10,  8.9952795e-10, -9.1185753e-10, ...,
          0.0000000e+00,  0.0000000e+00,  0.0000000e+00], dtype=float32),
  '08'),
 (array([0., 0., 0., ..., 0., 0., 0.], dtype=float32), '08'),
 (array([0., 0., 0., ..., 0., 0., 0.], dtype=float32), '06'),
 (array([-0.00050612, -0.00057967, -0.00035985, ...,  0.        ,
          0.        ,  0.        ], dtype=float32), '05'),
 (array([ 6.8139506e-08, -2.3837963e-05, -2.4622474e-05, ...,
          3.1678758e-06, -2.4535689e-06,  0.0000000e+00], dtype=float32),
  '05'),
 (array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
          6.9306935e-07, -6.6020442e-07,  0.0000000e+00], dtype=float32),
  '04'),
 (array([-7.30260945e-05, -1.18022966e-04, -1.08280736e-04, ...,
          8.83421380e-05,  4.97258679e-06,  0.00000000e+00], dtype=float32),
  '06'),
 (array([0., 0., 0., ..., 0., 0., 0.], dtype=float32), '07'),
 (array([ 2.3406714e-05,  3.1186773e-05,  4.9467826e-06, ...,
          1.2180173e-07, -9.2944845e-08,  0.0000000e+00], dtype=float32),
  '01'),
 (array([ 1.1845550e-06, -1.6399191e-06,  2.5565218e-06, ...,
         -8.7445065e-09,  5.9859917e-09,  0.0000000e+00], dtype=float32),
  '04'),
 (array([0., 0., 0., ..., 0., 0., 0.], dtype=float32), '03'),
 (array([-1.3284328e-05, -7.4090644e-07,  7.2679302e-07, ...,
          0.0000000e+00,  0.0000000e+00,  0.0000000e+00], dtype=float32),
  '07'),
 (array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
          5.0694009e-08, -3.4546797e-08,  0.0000000e+00], dtype=float32),
  '03'),
 (array([ 1.5591205e-07, -1.5845627e-07,  1.5362870e-07, ...,
          0.0000000e+00,  0.0000000e+00,  0.0000000e+00], dtype=float32),
  '01'),
 (array([0., 0., 0., ..., 0., 0., 0.], dtype=float32), '03'),
 (array([0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.1608539e-05,
         8.2463991e-09, 0.0000000e+00], dtype=float32), '03'),
 (array([-3.6192148e-07, -1.4590451e-05, -5.3999561e-06, ...,
         -1.9935460e-05, -3.4417746e-05,  0.0000000e+00], dtype=float32),
  '02'),
 (array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
         -2.5319534e-07,  2.6521766e-07,  0.0000000e+00], dtype=float32),
  '02'),
 (array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
         -2.5055220e-08,  1.2936166e-08,  0.0000000e+00], dtype=float32)
...

上方列表中每个元素的第二个元素（第一行中的“ 08”）代表根据以下字典的数据集标签

emotions = {
    "neutral": "01",
    "calm": "02",
    "happy": "03",
    "sad": "04",
    "angry": "05", 
    "fearful": "06", 
    "disgust": "07", 
    "surprised": "08"
}

这时，我有了标签和数据：如何拆分该数据集以获得训练和测试集？

EDIT1 ：我需要了解如何从此结构中获取X和y，以便对数据使用train_test_split。

Answer 1

您可以使用scikit-learn的train_test_split函数（relevant docs)。文档中的示例非常简单：

import numpy as np
from sklearn.model_selection import train_test_split

X, y = np.arange(10).reshape((5, 2)), range(5)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

对于您而言，您可能需要进行一些数据操作才能从输出列表中获取X和y向量：

X, y = zip(*lst)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

Python-机器学习：根据数组列表创建训练和测试集

1 个答案: