我在Python 3.6中使用了包含隐马尔可夫模型（HMM）的语音识别代码。训练数据（输入文件夹）由.wav文件组成，组织结构如下

train   pineapple   apple   banana   orange   kiwi   peach   lime

test数据文件夹使用了类似的模式。

代码在命令提示符下运行：

python Speech-Recognizer.py --input-folder train

代码如下所示：

import osimport argparseimport numpy as npfrom scipy.io import wavfilefrom hmmlearn import hmmfrom python_speech_features import mfcc# Function to parse input argumentsdef build_arg_parser():    parser = argparse.ArgumentParser(description='Trains the HMM classifier')    parser.add_argument("--input-folder", dest="input_folder", required=True,                        help="Input folder containing the audio files in subfolders")    return parser# Class to handle all HMM related processingclass HMMTrainer(object):    def __init__(self, model_name='GaussianHMM', n_components=4, cov_type='diag', n_iter=1000):        self.model_name = model_name        self.n_components = n_components        self.cov_type = cov_type        self.n_iter = n_iter        self.models = []        if self.model_name == 'GaussianHMM':            self.model = hmm.GaussianHMM(n_components=self.n_components,                                         covariance_type=self.cov_type, n_iter=self.n_iter)        else:            raise TypeError('Invalid model type')    # X is a 2D numpy array where each row is 13D    def train(self, X):        np.seterr(all='ignore')        self.models.append(self.model.fit(X))    # Run the model on input data    def get_score(self, input_data):        return self.model.score(input_data)if __name__ == '__main__':    args = build_arg_parser().parse_args()    input_folder = args.input_folder    hmm_models = []    # Parse the input directory    for dirname in os.listdir(input_folder):        # Get the name of the subfolder        subfolder = os.path.join(input_folder, dirname)        if not os.path.isdir(subfolder):            continue        # Extract the label        label = subfolder[subfolder.rfind('/') + 1:]        # Initialize variables        X = np.array([])        y_words = []        # Iterate through the audio files (leaving 1 file for testing in each class)        for filename in [x for x in os.listdir(subfolder) if x.endswith('.wav')][:-1]:            # Read the input file            filepath = os.path.join(subfolder, filename)            sampling_freq, audio = wavfile.read(filepath)            # Extract MFCC features            mfcc_features = mfcc(audio, sampling_freq)            # Append to the variable X            if len(X) == 0:                X = mfcc_features            else:                X = np.append(X, mfcc_features, axis=0)            # Append the label            y_words.append(label)        print('X.shape =', X.shape)        # Train and save HMM model        hmm_trainer = HMMTrainer()        hmm_trainer.train(X)        hmm_models.append((hmm_trainer, label))        hmm_trainer = None    # Test files    input_files = [        'test/pineapple/pineapple15.wav',        'test/orange/orange15.wav',        'test/apple/apple15.wav',        'test/kiwi/kiwi15.wav'    ]    # Classify input data    for input_file in input_files:        # Read input file        sampling_freq, audio = wavfile.read(input_file)        # Extract MFCC features        mfcc_features = mfcc(audio, sampling_freq)        # Define variables        max_score = None        output_label = None        # Iterate through all HMM models and pick        # the one with the highest score        for item in hmm_models:            hmm_model, label = item            score = hmm_model.get_score(mfcc_features)            if score > max_score:                max_score = score                output_label = label        # Print the output        print("\nTrue:", input_file[input_file.find('/') + 1:input_file.rfind('/')])        print("Predicted:", output_label)

运行上述代码时，我得到了以下错误：

Traceback (most recent call last):  File "Speech-Recognizer.py", line 113, in <module>    if score > max_score:TypeError: '>' not supported between instances of 'float' and 'NoneType'

回答：

        max_score = None...            if score > max_score:

你正在尝试将一个浮点数与None进行比较。

你可以将max_score = None改为max_score = 0，试试看怎么样？