这是我的代码，你能告诉我如何执行一个文件，让我的人工智能猜测它的东西，然后移动到下一个文件。我已经添加了一些描述，以显示我的代码是做什么

import os
import time

import matplotlib.pyplot as plt
import librosa
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import librosa.display

directory = 'Music'

# Load audio file
for filename in os.listdir(directory):
    if filename.endswith('.mp3'):
        audio_file, sampling_rate = librosa.load(os.path.join(directory, filename))
# Extract features using librosa
mfccs = librosa.feature.mfcc(y=audio_file, sr=sampling_rate)
chroma = librosa.feature.chroma_stft(y=audio_file, sr=sampling_rate)

plt.figure(figsize=(12,4))
# Combine features into one array
features = np.vstack((mfccs, chroma))
librosa.display.specshow(features, x_axis="time", y_axis="chroma", vmin=0, vmax=1)
plt.title('Mfccs')
plt.tight_layout()
plt.show()
# Load labels
labels = pd.read_csv('csvloader/tableConvert.com_y1m7y5.csv')
print(labels.head())
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2,                                         random_state=42)

# Standardize the data
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Train the model using a random forest classifier
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
# Test the model on the test data
accuracy = clf.score(X_test, y_test)
print(f"Accuracy: {accuracy * 100:.2f}%")
print(confusion_matrix(y_test, pred))
print(classification_report(y_test, pred))

我试着查找，但找不到解决方案