torch.manual_seed(opts['seed'])
if opts['gpu'] >= 0:
torch.cuda.set_device(opts['gpu'])
torch.cuda.manual_seed(opts['seed'])
group = opts['group']
# dataset #
data_cfg = yaml.load(open(opts['data_cfg']))
feat_iterator = DataLoader.load_feat(data_cfg,
data_type=LoaderDataType.TYPE_NP,
in_memory=opts['mem'])
text_iterator = DataLoader.load_text(data_cfg)
feat_stat = pickle.load(open(data_cfg['feat']['stat'], 'rb'))
feat_sil = feat_sil_from_stat(feat_stat)
print("Finish loading dataset ...")
NDIM = feat_iterator['train'].get_feat_dim() * group
NVOCAB = len(text_iterator['train'].get_map_text2idx())
if opts['model_pt'] is not None:
model = ModelSerializer.load_config(
os.path.join(opts['model_pt'], 'model.cfg'))
model.load_state_dict(
torch.load(os.path.join(opts['model_pt'], 'model.mdl')))
assert model.TYPE in [
TacotronType.SINGLE_SPEAKER, TacotronType.MULTI_SPEAKER
]
print('[info] load pretrained model')
group = opts['group']
# dataset #
data_in_cfg = yaml.load(open(opts['data_in_cfg']))
data_out_cfg = yaml.load(open(opts['data_out_cfg']))
feat_in_iterator = DataLoader.load_feat(data_in_cfg,
data_type=LoaderDataType.TYPE_NP,
in_memory=opts['mem'])
feat_out_iterator = DataLoader.load_feat(data_out_cfg,
data_type=LoaderDataType.TYPE_NP,
in_memory=opts['mem'])
feat_in_stat = pickle.load(open(data_in_cfg['feat']['stat'], 'rb'))
feat_in_sil = feat_sil_from_stat(feat_in_stat)
feat_out_stat = pickle.load(open(data_out_cfg['feat']['stat'], 'rb'))
feat_out_sil = feat_sil_from_stat(feat_out_stat)
print("Finish loading dataset ...")
NDIM_IN = feat_in_iterator['train'].get_feat_dim() * group
NDIM_OUT = feat_out_iterator['train'].get_feat_dim() * group
if opts['model_pt'] is not None:
model = ModelSerializer.load_config(
os.path.join(opts['model_pt'], 'model.cfg'))
model.load_state_dict(
torch.load(os.path.join(opts['model_pt'], 'model.mdl')))
print('[info] load pretrained model')