def start_training(self, itt_no_improve, batch_size, target_sample_rate):
epoch = 1
left_itt = itt_no_improve
dio = DatasetIO()
self._render_devset()
sys.stdout.write("\n")
self.vocoder.store('data/models/rnn_vocoder')
while left_itt > 0:
sys.stdout.write("Starting epoch " + str(epoch) + "\n")
sys.stdout.write("Shuffling training data\n")
from random import shuffle
shuffle(self.trainset.files)
file_index = 1
total_loss = 0
for file in self.trainset.files:
sys.stdout.write("\t" + str(file_index) + "/" +
str(len(self.trainset.files)) +
" processing file " + file)
sys.stdout.flush()
wav_file = file + ".orig.wav"
mgc_file = file + ".mgc.npy"
mgc = np.load(mgc_file)
file_index += 1
data, sample_rate = dio.read_wave(wav_file)
if self.use_ulaw:
[wave_disc, ulaw_cont] = dio.ulaw_encode(data)
else:
wave_disc = dio.b16_enc(data)
import time
start = time.time()
loss = self.vocoder.learn(wave_disc, mgc, batch_size)
total_loss += loss
stop = time.time()
sys.stdout.write(' avg loss=' + str(loss) +
" execution time=" + str(stop - start))
sys.stdout.write('\n')
sys.stdout.flush()
if file_index % 50 == 0:
self.synth_devset(batch_size, target_sample_rate)
self.vocoder.store('data/models/rnn_vocoder')
self.synth_devset(batch_size, target_sample_rate)
self.vocoder.store('data/models/rnn_vocoder')
epoch += 1
def start_training(self,
itt_no_improve,
batch_size,
target_sample_rate,
params=None):
epoch = 1
left_itt = itt_no_improve
dio = DatasetIO()
self._render_devset()
sys.stdout.write("\n")
if self.vocoder.sparse:
print("Setting sparsity at: " + str(params.sparsity_step) + "%")
sparsity = params.sparsity_step
self.vocoder.rnnFine.set_sparsity(float(sparsity) / 100)
self.vocoder.rnnCoarse.set_sparsity(float(sparsity) / 100)
if self.vocoder.sparse:
self.vocoder.store('data/models/rnn_vocoder_sparse')
else:
self.vocoder.store('data/models/rnn_vocoder')
num_files = 0
while left_itt > 0:
sys.stdout.write("Starting epoch " + str(epoch) + "\n")
sys.stdout.write("Shuffling training data\n")
from random import shuffle
shuffle(self.trainset.files)
file_index = 1
total_loss = 0
for file in self.trainset.files:
num_files += 1
if num_files == params.sparsity_increase:
sparsity += params.sparsity_step
num_files = 0
if sparsity <= params.sparsity_target:
print("Setting sparsity at " + str(sparsity) + "%")
self.vocoder.rnnFine.set_sparsity(
float(sparsity) / 100)
self.vocoder.rnnCoarse.set_sparsity(
float(sparsity) / 100)
else:
sparsity = params.sparsity_target
sys.stdout.write("\t" + str(file_index) + "/" +
str(len(self.trainset.files)) +
" processing file " + file)
sys.stdout.flush()
wav_file = file + ".orig.wav"
mgc_file = file + ".mgc.npy"
mgc = np.load(mgc_file)
file_index += 1
data, sample_rate = dio.read_wave(wav_file)
if self.use_ulaw:
[wave_disc, ulaw_cont] = dio.ulaw_encode(data)
else:
wave_disc = dio.b16_enc(data)
import time
start = time.time()
loss = self.vocoder.learn(wave_disc, mgc, batch_size)
total_loss += loss
stop = time.time()
sys.stdout.write(' avg loss=' + str(loss) +
" execution time=" + str(stop - start))
sys.stdout.write('\n')
sys.stdout.flush()
if file_index % 50 == 0:
self.synth_devset(batch_size, target_sample_rate)
if self.vocoder.sparse:
self.vocoder.store('data/models/rnn_vocoder_sparse')
else:
self.vocoder.store('data/models/rnn_vocoder')
self.synth_devset(batch_size, target_sample_rate)
if self.vocoder.sparse:
self.vocoder.store('data/models/rnn_vocoder_sparse')
else:
self.vocoder.store('data/models/rnn_vocoder')
epoch += 1