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')