Python 3.x 名称错误：名称'；型号'；未在python代码中为孤立语音识别定义

我从网上下载了代码

该代码与Python2兼容，我是为Python3编写的

但我得到了这个错误：

~/Desktop/IsolatedSpeechRecognition-master $ python Isolated_Speech_Recognition.py 
    List of spoken words: ['dog', 'human', 'god', 'eye', 'book', 'fast', 'apple', 'cat']
    {'book', 'human', 'fast', 'apple', 'god', 'cat', 'dog', 'eye'}
    Number of total files: 120
    Labels and label indices [6. 6. 6. 6. 6. 6. 6. 6. 6. 6. 6. 6. 6. 6. 6. 1. 1. 1. 1. 1. 1. 1. 1. 1.
     1. 1. 1. 1. 1. 1. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 7. 7. 7.
     7. 7. 7. 7. 7. 7. 7. 7. 7. 7. 7. 7. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
     0. 0. 0. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 3. 3. 3. 3. 3. 3.
     3. 3. 3. 3. 3. 3. 3. 3. 3. 5. 5. 5. 5. 5. 5. 5. 5. 5. 5. 5. 5. 5. 5. 5.]
    (120, 3841)
    Processed observation 0
    Processed observation 10
    Processed observation 20
    Processed observation 30
    Processed observation 40
    Processed observation 50
    Processed observation 60
    Processed observation 70
    Processed observation 80
    Processed observation 90
    Processed observation 100
    Processed observation 110
    (120, 7, 33)
    Traceback (most recent call last):
      File "Isolated_Speech_Recognition.py", line 269, in <module>
        _ = [model.train(X_train[y_train == y, :, :]) for m, y in zip(ms, ys)]
      File "Isolated_Speech_Recognition.py", line 269, in <listcomp>
        _ = [model.train(X_train[y_train == y, :, :]) for m, y in zip(ms, ys)]
    NameError: name 'model' is not defined

我尝试了，但无法找到出现此错误的原因以及如何解决此问题。

您调用

model.train（）

，但没有如错误消息所示定义

model

如果您想在列表中训练的模型是

m

，那么它应该是：

[m.train（X_train[y_train==y，：，：]）用于邮政编码中的m，y（ms，ys）]

以下行需要相同的更改：

[m.test（X_test）用于m in ms]

您调用

model.train（）

，但未定义

model

，如错误消息所示

如果您想在列表中训练的模型是

m

，那么它应该是：

[m.train（X_train[y_train==y，：，：]）用于邮政编码中的m，y（ms，ys）]

以下行需要相同的更改：

[m.test（X_测试）用于m（毫秒）]

from sklearn.model_selection import StratifiedShuffleSplit
sss = StratifiedShuffleSplit(test_size=0.1, random_state=0)

for n,i in enumerate(all_obs):
    all_obs[n] /= all_obs[n].sum(axis=0)


for train_index, test_index in sss.split(all_obs, all_labels):
    X_train, X_test = all_obs[train_index, ...], all_obs[test_index, ...]
    y_train, y_test = all_labels[train_index], all_labels[test_index]
ys = set(all_labels)
ms = [gmmhmm(7) for y in ys]

_ = [model.train(X_train[y_train == y, :, :]) for m, y in zip(ms, ys)]
ps1 = [model.test(X_test) for m in ms]
res1 = np.vstack(ps1)
predicted_label1 = np.argmax(res1, axis=0)
dictionary = ['apple', 'banana', 'elephant', 'dog', 'frog', 'cat', 'jack', 'god', 'Intelligent', 'hello']
spoken_word = []
for i in predicted_label1:
    spoken_word.append(dictionary[i])
print(spoken_word)
missed = (predicted_label1 != y_test)
print('Test accuracy: %.2f percent' % (100 * (1 - np.mean(missed))))