speech_recognizer.py 文件源码

def run_tests(test_files):
    # Classify input data
    for test_file in test_files:
        # Read input file
        sampling_freq, signal = wavfile.read(test_file)

        # Extract MFCC features
        with warnings.catch_warnings():
            warnings.simplefilter('ignore')
            features_mfcc = mfcc(signal, sampling_freq)

        # Define variables
        max_score = -float('inf') 
        output_label = None 

        # Run the current feature vector through all the HMM
        # models and pick the one with the highest score
        for item in speech_models:
            model, label = item
            score = model.compute_score(features_mfcc)
            if score > max_score:
                max_score = score
                predicted_label = label

        # Print the predicted output 
        start_index = test_file.find('/') + 1
        end_index = test_file.rfind('/')
        original_label = test_file[start_index:end_index]
        print('\nOriginal: ', original_label) 
        print('Predicted:', predicted_label)