def __call__(self):
# load all train data
provider = DataProvider()
test_bass_list, test_drums_list, test_other_list, test_vocals_list = provider.load_all_test_data(
)
# define model
tf_mix = tf.placeholder(tf.float32,
(1, self.sample_len)) #Batch, Sample
tf_est_source = self.__model(tf_mix)
# GPU config
config = tf.ConfigProto(gpu_options=tf.GPUOptions(
visible_device_list='0', # specify GPU number
allow_growth=True))
saver = tf.train.import_meta_graph(
'./../results/model/FFN_ver2/ffn_ver2_3999.ckpt.meta')
with tf.Session(config=config) as sess:
saver.restore(sess,
'./../results/model/FFN_ver2/ffn_ver2_3999.ckpt')
total_parameters = 0
parameters_string = ""
for variable in tf.trainable_variables():
shape = variable.get_shape()
variable_parameters = 1
for dim in shape:
variable_parameters *= dim.value
total_parameters += variable_parameters
if len(shape) == 1:
parameters_string += ("%s %d, " %
(variable.name, variable_parameters))
else:
parameters_string += (
"%s %s=%d, " %
(variable.name, str(shape), variable_parameters))
print(parameters_string)
print("Total %d variables, %s params" % (len(
tf.trainable_variables()), "{:,}".format(total_parameters)))
def __call__(self):
# load all train data
provider = DataProvider()
test_bass_list, test_drums_list, test_other_list, test_vocals_list = provider.load_all_test_data()
# define model
tf_mix = tf.placeholder(tf.float32, (None, self.sample_len)) #Batch, Sample
tf_est_source = self.__model(tf_mix)
# GPU config
config = tf.ConfigProto(
gpu_options=tf.GPUOptions(
visible_device_list='0', # specify GPU number
allow_growth = True
)
)
saver = tf.train.import_meta_graph('./../results/model/mini-U-Net/mini-U-Net_1999.ckpt.meta')
with tf.Session(config = config) as sess:
saver.restore(sess, './../results/model/mini-U-Net/mini-U-Net_1999.ckpt')
test_mixed_list = []
for bass, drums, other, vocals in zip(test_bass_list, test_drums_list, test_other_list, test_vocals_list):
test_mixed_list.append(AudioModule.mixing(
bass,
drums,
other,
vocals
))
test_target_list = test_vocals_list
tf.keras.backend.set_learning_phase(0)
# make mix audio
est_start = time.time()
for mix in test_mixed_list:
cutted_mix_array = provider.test_data_split_and_pad(mix, self.sample_len)
tmp_est_data_array = np.zeros((len(cutted_mix_array), self.sample_len))
for index, mix_packet in enumerate(cutted_mix_array):
mix_packet = mix_packet.reshape(1,-1)
est_source = sess.run(tf_est_source, feed_dict = {
tf_mix: mix_packet[:,:]
}
)
tmp_est_data_array[index,:] = est_source
self.est_audio_list.append(tmp_est_data_array.reshape(1,-1))
est_end = time.time()
print("excuted time", est_end - est_start)
evaluate_start = time.time()
for est, target, mix in zip(self.est_audio_list, test_target_list, test_mixed_list):
target = target.reshape(1,-1)
mix = mix.reshape(1,-1)
est_array = np.zeros((2, target.shape[1]))
est_array[0,:] = est[:, :target.shape[1]]
est_array[1,:] = mix[:, :target.shape[1]] - est[:, :target.shape[1]]
target_array = np.zeros((2, target.shape[1]))
target_array[0,:] = target
target_array[1,:] = mix[:, :target.shape[1]] - target
sdr, sir, sar, perm = mir_eval.separation.bss_eval_sources(target_array, est_array)
self.sdr_list.append(sdr[0])
self.sir_list.append(sir[0])
self.sar_list.append(sar[0])
print('sdr mean',np.mean(self.sdr_list))
print('sir mean',np.mean(self.sir_list))
print('sar mean',np.mean(self.sar_list))
print('sdr median', np.median(self.sdr_list))
print('sir median', np.median(self.sir_list))
print('sar median', np.median(self.sar_list))
evaluate_end = time.time()
print('evaluate time', evaluate_end - evaluate_start)
return self.est_audio_list, test_target_list, test_mixed_list