def setKerasMemory(limit=0.3):
from tensorflow import ConfigProto as tf_ConfigProto
from tensorflow import Session as tf_Session
from keras.backend.tensorflow_backend import set_session
config = tf_ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = limit
set_session(tf_Session(config=config))
def __init__(self,graph_path,label_path):
self.graph_path = graph_path
self.label_path = label_path
self.graph = tf_Graph()
self.sample_rate= 16000 # Samle Rate: 16000
self.window_len = 0.03 # Window Size: 30ms = 480 Samples 960 Bytes
self.frame_shift_ms= 0.01 # Frame Shift: 10ms = 160 Samples 320 Bytes
self.melcount = 40
self.frame_shift = int(self.frame_shift_ms*self.sample_rate)
self.bitsize = 2
self.blocksize = 20
self.recognition_threshold = 0.9
self.lower_frequency = 20
self.higher_frequency = 8000
self.prediction_every = 20 #Number of mel steps between predictions
self.gain = 1.0
self.detection_cooldown = 8
self.cooldown = 0
self.sensitivity = 0.5
self.mel_spectrogram = np.zeros((1,self.melcount*98), dtype=np.float32)
self.mel = FeatureExtraction(nfilt=self.melcount,lowerf=self.lower_frequency,upperf=self.higher_frequency,
samprate=self.sample_rate,wlen=self.window_len,nfft=512,datalen=512)
self.input_name = "fingerprint_input:0"
self.output_name = "labels_softmax:0"
self.sess = tf_Session(graph=self.graph)
self.labels_list = self._load_labels(label_path)
self._load_graph(graph_path)
self.last_frames = {}
self.softmax_tensor = self.sess.graph.get_tensor_by_name(self.output_name)
self._warmup()
def setKerasMemory(limit=0.3):
from tensorflow import ConfigProto as tf_ConfigProto
from tensorflow import Session as tf_Session
from keras.backend.tensorflow_backend import set_session
config = tf_ConfigProto()
# config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.per_process_gpu_memory_fraction = limit
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
set_session(tf_Session(config=config))