def main():
print('Initializing Training Process..')
parser = argparse.ArgumentParser()
parser.add_argument('--group_name', default=None)
parser.add_argument('--input_wavs_dir',
default='LJSpeech-1.1/wavs',
help='')
parser.add_argument('--input_mels_dir', default='ft_dataset', help='')
parser.add_argument('--input_training_file',
default='LJSpeech-1.1/training.txt',
help='')
parser.add_argument('--input_validation_file',
default='LJSpeech-1.1/validation.txt',
help='')
parser.add_argument('--checkpoint_path', default='cp_hifigan')
parser.add_argument('--config', default='')
parser.add_argument('--training_epochs', default=3100, type=int)
parser.add_argument('--stdout_interval', default=5, type=int)
parser.add_argument('--checkpoint_interval', default=5000, type=int)
parser.add_argument('--summary_interval', default=100, type=int)
parser.add_argument('--validation_interval', default=1000, type=int)
parser.add_argument('--fine_tuning', default=False, type=bool)
a = parser.parse_args()
with open(a.config) as f:
data = f.read()
json_config = json.loads(data)
h = AttrDict(json_config)
build_env(a.config, 'config.json', a.checkpoint_path)
torch.manual_seed(h.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(h.seed)
h.num_gpus = torch.cuda.device_count()
h.batch_size = int(h.batch_size / h.num_gpus)
print('Batch size per GPU :', h.batch_size)
else:
pass
if h.num_gpus > 1:
mp.spawn(train, nprocs=h.num_gpus, args=(
a,
h,
))
else:
train(0, a, h)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--group_name', default=None)
parser.add_argument('--input_wavs_dir', default='LJSpeech-1.1/wavs')
parser.add_argument('--input_validation_file', default='LJSpeech-1.1/validation.txt')
parser.add_argument('--checkpoint_path', default='cp_hifigan')
parser.add_argument('--config', default='')
parser.add_argument('--fine_tuning', default=False, type=bool)
parser.add_argument('--input_mels_dir', default='ft_dataset')
parser.add_argument('--speakers_json', default=None, type=str)
parser.add_argument('--batch_size', default=15, type=int)
# for train script compatibility
parser.add_argument('--input_training_file', default='LJSpeech-1.1/training.txt')
a = parser.parse_args()
with open(a.config) as f:
data = f.read()
if a.speakers_json:
with open(a.speakers_json) as f:
speaker_mapping = json.load(f)
else:
speaker_mapping = None
json_config = json.loads(data)
h = AttrDict(json_config)
torch.manual_seed(h.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(h.seed)
h.num_gpus = torch.cuda.device_count()
h.batch_size = int(h.batch_size / h.num_gpus)
print('Batch size per GPU :', h.batch_size)
else:
pass
h.segment_size = h.segment_size*8
eval(0, a, h, speaker_mapping)
def main():
print('Initializing the Training Process..')
parser = argparse.ArgumentParser()
parser.add_argument('--input_wavs_dir', default='data/recordings')
parser.add_argument('--input_mels_dir', default='processed_spokenDigits_np')
parser.add_argument('--config', default='processed_spokenDigits_np')
parser.add_argument('--training_epochs', default='1000')
a = parser.parse_args()
with open(a.config) as f:
data = f.read()
json_config = json.loads(data)
h = AttrDict(json_config)
build_env(a.config, 'config.json', a.checkpoint_path)
torch.manual_seed(h.seed):
if torch.cuda.is_availale(h.seed):
torch.cuda.manual_seeed(h.seed)
h.batch_size = int(h.batch_size / h.num_gpu)
else:
print('\nRunning on cpu')
# train now--
g_losses, d_losses, generated_mels = train(h)
# visualize the loss as the network trained
plt.plot(g_losses, d_losses)
plt.xlabel('100\'s of batches')
plt.ylabel('loss')
plt.grid(True)
# plt.ylim(0, 2.5) # consistent scale
plt.show()