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## LJSpeech
import torch
import commons
import utils
from models import SynthesizerTrn
from text.symbols import symbols
from text import text_to_sequence
from scipy.io.wavfile import write
def get_text(text, hps):
text_norm = text_to_sequence(text, 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
CONFIG_PATH = "./configs/vits2_ljs_nosdp.json"
MODEL_PATH = "./logs/G_114000.pth"
TEXT = "VITS-2 is Awesome!"
OUTPUT_WAV_PATH = "sample_vits2.wav"
hps = utils.get_hparams_from_file(CONFIG_PATH)
if (
"use_mel_posterior_encoder" in hps.model.keys()
and hps.model.use_mel_posterior_encoder == True
):
print("Using mel posterior encoder for VITS2")
posterior_channels = 80 # vits2
hps.data.use_mel_posterior_encoder = True
else:
print("Using lin posterior encoder for VITS1")
posterior_channels = hps.data.filter_length // 2 + 1
hps.data.use_mel_posterior_encoder = False
net_g = SynthesizerTrn(
len(symbols),
posterior_channels,
hps.train.segment_size // hps.data.hop_length,
**hps.model
).cuda()
_ = net_g.eval()
_ = utils.load_checkpoint(MODEL_PATH, net_g, None)
stn_tst = get_text(TEXT, hps)
with torch.no_grad():
x_tst = stn_tst.cuda().unsqueeze(0)
x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cuda()
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(data=audio, rate=hps.data.sampling_rate, filename=OUTPUT_WAV_PATH)
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