在本文中，我将展示如何使用张量流来预测音乐风格。
在我的示例中，我比较了电子音乐和古典音乐。
你可以在我的github上找到代码：
https://github.com/victordalet/sound_to_partition
第一步，您需要创建一个数据集文件夹，并在里面添加一个音乐风格文件夹，例如我添加一个 techno 文件夹和 classic 文件夹，其中放置我的 wav 歌曲。
我创建一个训练文件，并使用要完成的参数 max_epochs。
修改构造函数中与数据集文件夹中的目录对应的类。
在加载和处理方法中，我从不同的目录检索wav文件并获取频谱图。
出于训练目的，我使用 Keras 卷积和模型。
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()
为了测试和使用该模型，我创建了此类来检索权重并预测音乐的风格。
不要忘记将正确的类添加到构造函数中。
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}')
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