| import ONNXVITS_models | |
| import utils | |
| from text import text_to_sequence | |
| import torch | |
| import commons | |
| def get_text(text, hps): | |
| text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners) | |
| if hps.data.add_blank: | |
| text_norm = commons.intersperse(text_norm, 0) | |
| text_norm = torch.LongTensor(text_norm) | |
| return text_norm | |
| hps = utils.get_hparams_from_file("../vits/pretrained_models/uma87.json") | |
| symbols = hps.symbols | |
| net_g = ONNXVITS_models.SynthesizerTrn( | |
| len(symbols), | |
| hps.data.filter_length // 2 + 1, | |
| hps.train.segment_size // hps.data.hop_length, | |
| n_speakers=hps.data.n_speakers, | |
| **hps.model) | |
| _ = net_g.eval() | |
| _ = utils.load_checkpoint("../vits/pretrained_models/uma_1153000.pth", net_g) | |
| text1 = get_text("γγγγ¨γγγγγΎγγ", hps) | |
| stn_tst = text1 | |
| with torch.no_grad(): | |
| x_tst = stn_tst.unsqueeze(0) | |
| x_tst_lengths = torch.LongTensor([stn_tst.size(0)]) | |
| sid = torch.tensor([0]) | |
| o = net_g(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1) |