def main(_):
print('Parsed arguments: ', FLAGS.__flags)
# make save path if it is required
if not os.path.exists(FLAGS.save_path):
os.makedirs(FLAGS.save_path)
if not os.path.exists(FLAGS.synthesis_path):
os.makedirs(FLAGS.synthesis_path)
np.random.seed(FLAGS.seed)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
udevices = []
for device in devices:
if len(devices) > 1 and 'CPU' in device.name:
# Use cpu only when we dont have gpus
# udevices.append(device.name)
continue
print('Using device: ', device.name)
udevices.append(device.name)
# execute the session
with tf.Session(config=config) as sess:
if FLAGS.model == 'gan':
print('Creating GAN model')
se_model = SEGAN(sess, FLAGS, udevices)
elif FLAGS.model == 'ae':
print('Creating AE model')
se_model = SEAE(sess, FLAGS, udevices)
else:
raise ValueError('{} model type not understood!'.format(
FLAGS.model))
if FLAGS.test_wav is None:
se_model.train(FLAGS, udevices)
else:
if FLAGS.weights is None:
raise ValueError('weights must be specified!')
print('Loading model weights...')
se_model.load(FLAGS.save_path, FLAGS.weights)
fm, wav_data = wavfile.read(FLAGS.test_wav)
print(fm)
wavname = FLAGS.test_wav.split('/')[-1]
if fm != 16000:
raise ValueError('16kHz required! Test file is different')
wave = (2. / 65535.) * (wav_data.astype(np.float32) - 32767) + 1.
if FLAGS.preemph > 0:
print('preemph test wave with {}'.format(FLAGS.preemph))
x_pholder, preemph_op = pre_emph_test(FLAGS.preemph,
wave.shape[0])
wave = sess.run(preemph_op, feed_dict={x_pholder: wave})
print('test wave shape: ', wave.shape)
print('test wave min:{} max:{}'.format(np.min(wave),
np.max(wave)))
c_wave = se_model.clean(wave)
print('c wave min:{} max:{}'.format(np.min(c_wave),
np.max(c_wave)))
wavfile.write(os.path.join(FLAGS.save_clean_path, wavname), 16000,
c_wave)
print('Done cleaning {} and saved '
'to {}'.format(FLAGS.test_wav,
os.path.join(FLAGS.save_clean_path, wavname)))
def main(_):
print('Parsed arguments: ', FLAGS.__flags)
# make save path if it is required
if not os.path.exists(FLAGS.save_path):
os.makedirs(FLAGS.save_path)
if not os.path.exists(FLAGS.synthesis_path):
os.makedirs(FLAGS.synthesis_path)
np.random.seed(FLAGS.seed)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement=True
udevices = []
for device in devices:
if len(devices) > 1 and 'cpu' in device.name:
# Use cpu only when we dont have gpus
continue
print('Using device: ', device.name)
udevices.append(device.name)
# execute the session
with tf.Session(config=config) as sess:
if FLAGS.model == 'gan':
print('Creating GAN model')
se_model = SEGAN(sess, FLAGS, udevices)
elif FLAGS.model == 'ae':
print('Creating AE model')
se_model = SEAE(sess, FLAGS, udevices)
else:
raise ValueError('{} model type not understood!'.format(FLAGS.model))
if FLAGS.test_wavs is None:
se_model.train(FLAGS, udevices)
else:
if FLAGS.weights is None:
raise ValueError('weights must be specified!')
print('Loading model weights...')
se_model.load(FLAGS.save_path, FLAGS.weights)
FLAGS.test_wavs = FLAGS.test_wavs.split(',')
for i in range(len(FLAGS.test_wavs)):
wav_data, fm = librosa.load(FLAGS.test_wavs[i], sr=16000)
wavname = FLAGS.test_wavs[i].split('/')[-1].split('.')[0] +'_segan.wav'
wave = (2./65535.) * (wav_data.astype(np.float32) - 32767) + 1.
if FLAGS.preemph > 0:
print('preemph test wave with {}'.format(FLAGS.preemph))
x_pholder, preemph_op = pre_emph_test(FLAGS.preemph, wav_data.shape[0])
wave = sess.run(preemph_op, feed_dict={x_pholder: wav_data})
print("【*】Clean start time ...", time.asctime(time.localtime(time.time())))
c_wave = se_model.clean(wave)
print("【*】Clean end time ...", time.asctime(time.localtime(time.time())))
print('c wave min:{} max:{}'.format(np.min(c_wave), np.max(c_wave)))
print(c_wave)
librosa.output.write_wav(os.path.join(FLAGS.save_clean_path, wavname), c_wave, sr=16000)
print('Done cleaning {} and saved '
'to {}'.format(FLAGS.test_wavs[i], os.path.join(FLAGS.save_clean_path, wavname)))
def main(_):
print('Parsed arguments: ', FLAGS.__flags)
# make save path if it is required
if not os.path.exists(FLAGS.save_path):
os.makedirs(FLAGS.save_path)
if not os.path.exists(FLAGS.synthesis_path):
os.makedirs(FLAGS.synthesis_path)
np.random.seed(FLAGS.seed)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement=True
udevices = []
for device in devices:
if len(devices) > 1 and 'cpu' in device.name:
# Use cpu only when we dont have gpus
continue
print('Using device: ', device.name)
udevices.append(device.name)
# execute the session
with tf.Session(config=config) as sess:
if FLAGS.model == 'gan':
print('Creating GAN model')
se_model = SEGAN(sess, FLAGS, udevices)
elif FLAGS.model == 'ae':
print('Creating AE model')
se_model = SEAE(sess, FLAGS, udevices)
else:
raise ValueError('{} model type not understood!'.format(FLAGS.model))
if FLAGS.test_wav is None:
se_model.train(FLAGS, udevices)
else:
if FLAGS.weights is None:
raise ValueError('weights must be specified!')
print('Loading model weights...')
se_model.load(FLAGS.save_path, FLAGS.weights)
fm, wav_data = wavfile.read(FLAGS.test_wav)
wavname = FLAGS.test_wav.split('/')[-1]
if fm != 16000:
raise ValueError('16kHz required! Test file is different')
wave = (2./65535.) * (wav_data.astype(np.float32) - 32767) + 1.
if FLAGS.preemph > 0:
print('preemph test wave with {}'.format(FLAGS.preemph))
x_pholder, preemph_op = pre_emph_test(FLAGS.preemph, wave.shape[0])
wave = sess.run(preemph_op, feed_dict={x_pholder:wave})
print('test wave shape: ', wave.shape)
print('test wave min:{} max:{}'.format(np.min(wave), np.max(wave)))
c_wave = se_model.clean(wave)
print('c wave min:{} max:{}'.format(np.min(c_wave), np.max(c_wave)))
wavfile.write(os.path.join(FLAGS.save_clean_path, wavname), 16e3, c_wave)
print('Done cleaning {} and saved '
'to {}'.format(FLAGS.test_wav,
os.path.join(FLAGS.save_clean_path, wavname)))
def main(_):
if FLAGS.feature_type == 'wavform':
from model import SEGAN, SEAE
elif FLAGS.feature_type == 'logspec':
from spec_model import SEGAN, SEAE
print('Parsed arguments: ', FLAGS.__flags)
# make save path if it is required
if not os.path.exists(FLAGS.save_path):
os.makedirs(FLAGS.save_path)
if not os.path.exists(FLAGS.synthesis_path):
os.makedirs(FLAGS.synthesis_path)
np.random.seed(FLAGS.seed)
#gpu_options = tf.GPUOptions(allow_growth=True)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.gpu_options.allocator_type = 'BFC'
udevices = []
for device in devices:
print("Device lists:{}".format(devices))
if len(devices) > 1 and 'cpu' in device.name:
# Use cpu only when we dont have gpus
continue
print('Using device: ', device.name)
udevices.append(device.name)
print("device:{}".format(udevices))
# execute the session
with tf.Session(config=config) as sess:
if FLAGS.model == 'gan':
print('Creating GAN model')
se_model = SEGAN(sess, FLAGS, udevices)
elif FLAGS.model == 'ae':
print('Creating AE model')
se_model = SEAE(sess, FLAGS, udevices)
else:
raise ValueError('{} model type not understood!'.format(
FLAGS.model))
if FLAGS.test_wav is None:
mode = 'stage2'
se_model.train(FLAGS, udevices, mode)
else:
if FLAGS.weights is None:
raise ValueError('weights must be specified!')
print('Loading model weights...')
se_model.load(FLAGS.save_path, FLAGS.weights)
noisy_test_filelist = []
for (dirpath, dirnames, filenames) in os.walk(FLAGS.test_wav):
# print('dirpath = ' + dirpath)
for filename in filenames:
file_path = os.path.join(dirpath, filename)
noisy_test_filelist.append(file_path)
nlist = noisy_test_filelist
for name in nlist:
t1 = time.time()
fm, wav_data = wavfile.read(name)
wavname = name.split('/')[-1]
if fm != 16000:
raise ValueError('16kHz required! Test file is different')
#import librosa
#print('16kHz is required: test file is {}kHz, have to resample to the required samplerate')
#wav_data = librosa.resample(wav_data, fm, 16000)
if FLAGS.feature_type == 'wavform':
wave = (2. / 65535.) * (wav_data.astype(np.float32) -
32767) + 1.
if FLAGS.preemph > 0:
print('preemph test wave with {}'.format(
FLAGS.preemph))
x_pholder, preemph_op = pre_emph_test(
FLAGS.preemph, wave.shape[0])
wave = sess.run(preemph_op,
feed_dict={x_pholder: wave})
print('test wave shape: ', wave.shape)
print('test wave min:{} max:{}'.format(
np.min(wave), np.max(wave)))
c_wave = se_model.clean(wave)
print('c wave min:{} max:{}'.format(
np.min(c_wave), np.max(c_wave)))
wavfile.write(
os.path.join(FLAGS.save_clean_path, wavname), 16e3,
np.int16(c_wave *
32767)) #(0.9*c_wave/max(abs(c_wave)))
t2 = time.time(
) #np.int16((1.0*c_wave/max(abs(c_wave)))*32767)
print('Done cleaning {}/{}s and saved '
'to {}'.format(
name, t2 - t1,
os.path.join(FLAGS.save_clean_path, wavname)))
if FLAGS.feature_type == 'logspec':
if wav_data.dtype != 'float32':
wave = np.float32(wav_data / 32767.)
#wave = (2./65535.) * (wav_data.astype(np.float32) - 32767) + 1.
if FLAGS.preemph > 0:
print('preemph test wave with {}'.format(
FLAGS.preemph))
x_pholder, preemph_op = pre_emph_test(
FLAGS.preemph, wave.shape[0])
wave = sess.run(preemph_op,
feed_dict={x_pholder: wave})
print('test wave shape: ', wave.shape)
print('test wave min:{} max:{}'.format(
np.min(wave), np.max(wave)))
c_wave = se_model.clean(wave)
print('c wave min:{} max:{}'.format(
np.min(c_wave), np.max(c_wave)))
wavfile.write(
os.path.join(FLAGS.save_clean_path, wavname), 16e3,
np.int16(c_wave *
32767)) #(0.9*c_wave/max(abs(c_wave)))
t2 = time.time()
print('Done cleaning {}/{}s and saved '
'to {}'.format(
name, t2 - t1,
os.path.join(FLAGS.save_clean_path, wavname)))
'''
