Tensorflow 音乐预测

import osimport sysfrom typing import listimport librosaimport numpy as npfrom tensorflow.keras.layers import input, conv2d, maxpooling2d, flatten, densefrom tensorflow.keras.models import modelfrom tensorflow.keras.optimizers import adamfrom sklearn.model_selection import train_test_splitfrom tensorflow.keras.utils import to_categoricalfrom tensorflow.image import resizeclass train:    def __init__(self):        self.x_train = none        self.x_test = none        self.y_train = none        self.y_test = none        self.data_dir: str = 'dataset'        self.classes: list[str] = ['techno','classic']        self.max_epochs: int = int(sys.argv[1])    @staticmethod    def load_and_preprocess_data(data_dir, classes, target_shape=(128, 128)):        data = []        labels = []        for i, class_name in enumerate(classes):            class_dir = os.path.join(data_dir, class_name)            for filename in os.listdir(class_dir):                if filename.endswith('.wav'):                    file_path = os.path.join(class_dir, filename)                    audio_data, sample_rate = librosa.load(file_path, sr=none)                    mel_spectrogram = librosa.feature.melspectrogram(y=audio_data, sr=sample_rate)                    mel_spectrogram = resize(np.expand_dims(mel_spectrogram, axis=-1), target_shape)                    data.append(mel_spectrogram)                    labels.append(i)        return np.array(data), np.array(labels)    def create_model(self):        data, labels = self.load_and_preprocess_data(self.data_dir, self.classes)        labels = to_categorical(labels, num_classes=len(self.classes))  # convert labels to one-hot encoding        self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(data, labels, test_size=0.2,                                                                                random_state=42)        input_shape = self.x_train[0].shape        input_layer = input(shape=input_shape)        x = conv2d(32, (3, 3), activation='relu')(input_layer)        x = maxpooling2d((2, 2))(x)        x = conv2d(64, (3, 3), activation='relu')(x)        x = maxpooling2d((2, 2))(x)        x = flatten()(x)        x = dense(64, activation='relu')(x)        output_layer = dense(len(self.classes), activation='softmax')(x)        self.model = model(input_layer, output_layer)        self.model.compile(optimizer=adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])    def train_model(self):        self.model.fit(self.x_train, self.y_train, epochs=self.max_epochs, batch_size=32,                       validation_data=(self.x_test, self.y_test))        test_accuracy = self.model.evaluate(self.x_test, self.y_test, verbose=0)        print(test_accuracy[1])    def save_model(self):        self.model.save('weight.h5')if __name__ == '__main__':    train = train()    train.create_model()    train.train_model()    train.save_model()

from typing import Listimport librosaimport numpy as npfrom tensorflow.keras.models import load_modelfrom tensorflow.image import resizeimport tensorflow as tfclass Test:    def __init__(self, audio_file_path: str):        self.model = load_model('weight.h5')        self.target_shape = (128, 128)        self.classes: List[str] = ['techno','classic']        self.audio_file_path: str = audio_file_path    def test_audio(self, file_path, model):        audio_data, sample_rate = librosa.load(file_path, sr=None)        mel_spectrogram = librosa.feature.melspectrogram(y=audio_data, sr=sample_rate)        mel_spectrogram = resize(np.expand_dims(mel_spectrogram, axis=-1), self.target_shape)        mel_spectrogram = tf.reshape(mel_spectrogram, (1,) + self.target_shape + (1,))        predictions = model.predict(mel_spectrogram)        class_probabilities = predictions[0]        predicted_class_index = np.argmax(class_probabilities)        return class_probabilities, predicted_class_index    def test(self):        class_probabilities, predicted_class_index = self.test_audio(self.audio_file_path, self.model)        for i, class_label in enumerate(self.classes):            probability = class_probabilities[i]            print(f'Class: {class_label}, Probability: {probability:.4f}')        predicted_class = self.classes[predicted_class_index]        accuracy = class_probabilities[predicted_class_index]        print(f'The audio is classified as: {predicted_class}')        print(f'Accuracy: {accuracy:.4f}')