| import logging |
| logging.getLogger('numba').setLevel(logging.WARNING) |
| import IPython.display as ipd |
| import torch |
| import commons |
| import utils |
| import ONNXVITS_infer |
| from text import text_to_sequence |
|
|
| 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") |
|
|
| net_g = ONNXVITS_infer.SynthesizerTrn( |
| len(hps.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("γγ―γγγγγγΎγγ", 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.LongTensor([0]) |
| audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy() |
| print(audio) |
