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from models import SynthesizerTrn
from scipy.io.wavfile import write
from khmer_phonemizer import phonemize_single
import utils
import commons
import torch
_pad = '_'
_punctuation = '. '
_letters_ipa = 'acefhijklmnoprstuwzĕŋŏŭɑɓɔɗəɛɡɨɲʋʔʰː'
# Export all symbols:
symbols = [_pad] + list(_punctuation) + list(_letters_ipa)
# Special symbol ids
SPACE_ID = symbols.index(" ")
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
def text_to_sequence(text):
sequence = []
for symbol in text:
symbol_id = _symbol_to_id[symbol]
sequence += [symbol_id]
return sequence
def get_text(text, hps):
text_norm = text_to_sequence(text)
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("config.json")
net_g = SynthesizerTrn(
len(symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
**hps.model
)
_ = net_g.eval()
_ = utils.load_checkpoint("G_22000.pth", net_g, None)
text = " ".join(phonemize_single("នឹកណាស់") + ["."])
stn_tst = get_text(text, hps)
with torch.no_grad():
x_tst = stn_tst.unsqueeze(0)
x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
audio = (
net_g.infer(
x_tst, x_tst_lengths, noise_scale=0.667, noise_scale_w=0.8, length_scale=1
)[0][0, 0]
.data.cpu()
.float()
.numpy()
)
write("audio.wav", rate=hps.data.sampling_rate, data=audio)
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