def main():
block_blob_service = BlockBlobService(account_name=azureStorgeAccountName,
account_key=azureStorageKeyName)
if (block_blob_service.exists(
container_name=azureStorageContainer) == False):
block_blob_service.create_container(
container_name=azureStorageContainer)
audio_path_label_pairs = load_path_labels(str(args.data_dir))
print('loaded: ' + str(len(audio_path_label_pairs)) + ' files')
classifier = Cifar10AudioClassifier()
batch_size = 8
epochs = 50
classifier.fit(audio_path_label_pairs=audio_path_label_pairs,
model_dir_path='model',
batch_size=batch_size,
epochs=epochs)
#zipping and uploading the compiled model to blob storage
shutil.make_archive(base_name='model', format='zip', base_dir='model')
block_blob_service.create_blob_from_path(
azureStorageContainer,
str(int(time.time() * 1000)) + 'model.zip', 'model.zip')
# copying the model contents to the output directory
os.system('cp -r ./model outputs')
def predict(audio_path, model_dir_path):
classifier = Cifar10AudioClassifier()
classifier.load_model(model_dir_path)
predicted_label_id = classifier.predict_class(audio_path)
print(predicted_label_id)
def main():
classifier = Cifar10AudioClassifier()
classifier.load_model(model_dir_path='./models')
classifier.export_tensorflow_model(
output_fld='./models/tensorflow_models/cifar10')
def main():
audio_path_label_pairs = load_audio_path_label_pairs()
print('loaded: ', len(audio_path_label_pairs))
classifier = Cifar10AudioClassifier()
batch_size = 8
epochs = 100
history = classifier.fit(audio_path_label_pairs, model_dir_path='./models_music_speech', batch_size=batch_size, epochs=epochs)
def main():
audio_path_label_pairs = load_audio_path_label_pairs()
print('loaded: ', len(audio_path_label_pairs))
classifier = Cifar10AudioClassifier()
batch_size = 128
epochs = 200
history = classifier.fit(
audio_path_label_pairs,
model_dir_path='/newvolume/keras-audio-master/demo/models',
batch_size=batch_size,
epochs=epochs)
def main():
audio_path_label_pairs = load_audio_path_label_pairs()
shuffle(audio_path_label_pairs)
print('loaded: ', len(audio_path_label_pairs))
classifier = Cifar10AudioClassifier()
classifier.load_model(model_dir_path='./models_music_speech')
for i in range(0, 20):
audio_path, actual_label_id = audio_path_label_pairs[i]
predicted_label_id = classifier.predict_class(audio_path)
print(audio_path)
predicted_label = gtzan_categories[predicted_label_id]
actual_label = gtzan_categories[actual_label_id]
print('predicted: ', predicted_label, 'actual: ', actual_label)
def processFile(filename):
classifier = Cifar10AudioClassifier()
classifier.load_model(model_dir_path='./models')
predicted_label_id = classifier.predict_class(filename)
predicted_label = labels[predicted_label_id]
predicted_scores = classifier.predict(filename)
predicted_score = predicted_scores[predicted_label_id]
data = {}
data['label'] = predicted_label
data['score'] = float(predicted_score)
data['dateTime'] = str(datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
data['modelDate'] = str(time.ctime(os.path.getmtime('models/cifar10-weights.h5')))
data['fileProcessed'] = filename
os.remove(filename + "-mg.npy")
os.remove(filename)
K.clear_session()
return json.dumps(data)
def main():
audio_path_label_pairs = load_path_labels(str(args.data_dir))
print('loaded: ' + str(len(audio_path_label_pairs)) + ' files')
classifier = Cifar10AudioClassifier()
batch_size = 8
epochs = 50000
classifier.fit(audio_path_label_pairs=audio_path_label_pairs,
model_dir_path='model',
batch_size=batch_size,
epochs=epochs)
#zipping compiled model and copy back to mounted blob storage
shutil.make_archive(base_name='model', format='zip', base_dir='model')
os.system('cp -r model.zip ' + args.data_dir + '/' +
str(int(time.time() * 1000)) + 'model.zip')
# copying the model contents to the output directory
os.system('cp -r ./model outputs')