Tensorflow-Musikvorhersage

import os import sys from typing import List import librosa import numpy as np from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense from tensorflow.keras.models import Model from tensorflow.keras.optimizers import Adam from sklearn.model_selection import train_test_split from tensorflow.keras.utils import to_categorical from tensorflow.image import resize class 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 List import librosa import numpy as np from tensorflow.keras.models import load_model from tensorflow.image import resize import tensorflow as tf class 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}')