def main():
testset = TextDataset(args.testset)
test_loader = DataLoader(dataset=testset,
batch_size=args.test_batch,
drop_last=False,
shuffle=False,
collate_fn=synth_collate_fn,
pin_memory=True)
t2m = Text2Mel().to(DEVICE)
ssrn = SSRN().to(DEVICE)
mname = type(t2m).__name__
ckpt = sorted(glob.glob(os.path.join(args.logdir, mname, '*k.pth.tar')))
state = torch.load(ckpt[-1])
t2m.load_state_dict(state['model'])
args.global_step = state['global_step']
mname = type(ssrn).__name__
ckpt = sorted(glob.glob(os.path.join(args.logdir, mname, '*k.pth.tar')))
state = torch.load(ckpt[-1])
ssrn.load_state_dict(state['model'])
print('All of models are loaded.')
t2m.eval()
ssrn.eval()
if not os.path.exists(os.path.join(args.sampledir, 'A')):
os.makedirs(os.path.join(args.sampledir, 'A'))
synthesize(t2m, ssrn, test_loader, args.test_batch)
from playsound import playsound
import numpy as np
import torch
from num2words import num2words
from hparams import HParams as hp
from audio import save_to_wav
from models import SSRN,Text2Mel
from lj_speech import vocab, idx2char, get_test_data
torch.set_grad_enabled(False)
text2mel = Text2Mel(vocab)
text2mel.load_state_dict(torch.load("ljspeech-text2mel.pth").state_dict())
text2mel = text2mel.eval()
ssrn = SSRN()
ssrn.load_state_dict(torch.load("ljspeech-ssrn.pth").state_dict())
ssrn = ssrn.eval()
def say(sentence):
new_sentence=" " .join([num2words(w) if w.isdigit() else w for w in sentence.split()])
normalized_sentence = "".join([c if c.lower() in vocab else '' for c in new_sentence])
print(normalized_sentence)
sentences = [normalized_sentence]
max_N = len(normalized_sentence)
L = torch.from_numpy(get_test_data(sentences, max_N))
zeros = torch.from_numpy(np.zeros((1, hp.n_mels, 1), np.float32))
Y = zeros
A = None
for t in range(hp.max_T):
_, Y_t, A = text2mel(L, Y, monotonic_attention=True)