In this article, I show how to use tensorflow to predict a style of music.
In my example, I compare techno and classical music.
You can find the code on my github :
https://github.com/victordalet/sound_to_partition
I - Dataset
For the first step, you need to create one dataset forlder and inside add one folder for music style, for example i add one techno folder and classic folder in which in put my wav soung.
II - Train
I create a train file, with the arguments max_epochs to be completed.
Modify the classes in the constructor that correspond to your directory in the dataset folder.
In the loading and processing method, I retrieve the wav file from a different directory and obtain the spectogram.
For training purposes, I use the Keras convolutions and model.
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() III - Test
To test and use the model, I've created this class to retrieve the weight and predict the style of the music.
Don't forget to add the right classes to the constructor.
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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