def get_genre(model, music_path):
"Predict genre of music using a trained model"
prediction = model.predict(extract_audio_features(music_path))
predict_genre = GenreFeatureData().genre_list[np.argmax(prediction)]
return predict_genre
"""
import os
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
from GenreFeatureData import (
GenreFeatureData,
) # local python class with Audio feature extraction (librosa)
genre_features = GenreFeatureData()
# if all of the preprocessed files do not exist, regenerate them all for self-consistency
if (os.path.isfile(genre_features.train_X_preprocessed_data)
and os.path.isfile(genre_features.train_Y_preprocessed_data)
and os.path.isfile(genre_features.dev_X_preprocessed_data)
and os.path.isfile(genre_features.dev_Y_preprocessed_data)
and os.path.isfile(genre_features.test_X_preprocessed_data)
and os.path.isfile(genre_features.test_Y_preprocessed_data)):
print("Preprocessed files exist, deserializing npy files")
genre_features.load_deserialize_data()
else:
print("Preprocessing raw audio files")
genre_features.load_preprocess_data()
train_X = torch.from_numpy(genre_features.train_X).type(torch.Tensor)
import os
from keras.models import Sequential
from keras.layers.recurrent import LSTM
from keras.layers import Dense
from keras.optimizers import Adam
from GenreFeatureData import GenreFeatureData # local python class with Audio feature extraction (librosa)
# Turn off TF verbose logging
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # or any {'0', '1', '2'}
genre_features = GenreFeatureData()
genre_features.load_preprocess_data()
# genre_features.load_deserialize_data()
# Keras optimizer defaults:
# Adam : lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-8, decay=0.
# RMSprop: lr=0.001, rho=0.9, epsilon=1e-8, decay=0.
# SGD : lr=0.01, momentum=0., decay=0.
opt = Adam()
batch_size = 35
nb_epochs = 400
print("Training X shape: " + str(genre_features.train_X.shape))
print("Training Y shape: " + str(genre_features.train_Y.shape))
print("Dev X shape: " + str(genre_features.dev_X.shape))
print("Dev Y shape: " + str(genre_features.dev_Y.shape))
print("Test X shape: " + str(genre_features.test_X.shape))
print("Test Y shape: " + str(genre_features.test_X.shape))
input_shape = (genre_features.train_X.shape[1],