VITS-Umamusume-voice-synthesizer / ONNXVITS_inference.py
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Duplicate from Plachta/VITS-Umamusume-voice-synthesizer
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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("γŠγ―γ‚ˆγ†γ”γ–γ„γΎγ™γ€‚", 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)