from keras.utils import np_utils
y = np_utils.to_categorical(y, num_classes=len(labels))
all_wave = np.array(all_wave).reshape(-1,8000,1)
print(y.shape)
print(all_wave.shape)
step = all_wave.shape[0]//10
print(step)
for set_len in range(step, all_wave.shape[0], step):
    y_sliced = y[:set_len, :]
    all_wave_sliced = all_wave[:set_len, :, :]
    print(y_sliced.shape)
    print(all_wave_sliced.shape)
    from sklearn.model_selection import train_test_split
    x_tr, x_val, y_tr, y_val = train_test_split(np.array(all_wave_sliced),np.array(y_sliced),stratify=y_sliced,test_size = 0.2,random_state=777,shuffle=True)