Python初学者ML项目问题

Question

所以我复制了一些代码来尝试在 python 中弄清楚机器学习。总的来说效果很好，但现在我不知道如何使用它（输入我自己的文件并分析它）。import librosaimport numpy as npimport soundfileimport sklearnimport os, glob, picklefrom sklearn.model_selection import train_test_splitfrom sklearn.neural_network import MLPClassifierfrom sklearn.metrics import accuracy_scoredef extract_feture(filepath,mfcc,chroma,mel):    with soundfile.SoundFile(filepath) as sound_file:        X = sound_file.read(dtype="float32")        sample_rate=sound_file.samplerate        if chroma:            stft = np.abs(librosa.stft(X))        result = np.array([])        if mfcc:            mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40) .T, axis=0)            result = np.hstack((result, mfccs))        if chroma:            chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T, axis=0)            result = np.hstack((result, chroma))        if mel:            mel = np.mean(librosa.feature.melspectrogram(X, sr= sample_rate).T,axis=0)            result = np.hstack((result, mel))    return resultemotions = {    '01':'neutral',    '02':'calm',    '03':'happy',    '04':'sad',    '05': 'angry',    '06': 'fearful',    '07': 'disgust',    '08': 'surprised'}observed_emotions =['calm', 'happy', 'fearful', 'disgust']def load_data(test_size=0.2):    x, y = [], []    for file in glob.glob("/home/adobug2/Documents/ravdess-data/Actor_*/*.wav"):        file_name = os.path.basename(file)        emotion = emotions[file_name.split("-")[2]]        if emotion not in observed_emotions:            continue        feature = extract_feture(file, mfcc=True, chroma=True, mel=True)        x.append(feature)        y.append(emotion)    return train_test_split(np.array(x), y, test_size=test_size, random_state=9)x_train,x_test,y_train,y_test=load_data(test_size=0.25)print((x_train.shape[0], x_test.shape[0]))

Answer 1

model.predict()在您的新音频文件上使用。那应该返回您想要的输出。