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synthesize.py
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synthesize.py
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import torch
import torch.nn as nn
import numpy as np
import hparams as hp
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]=hp.synth_visible_devices
import argparse
import re
from string import punctuation
from fastspeech2 import FastSpeech2
from vocoder import vocgan_generator
from text import text_to_sequence, sequence_to_text
import utils
import audio as Audio
import codecs
from g2pk import G2p
from jamo import h2j
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def kor_preprocess(text):
g2p=G2p()
phone = g2p(text)
print('after g2p: ',phone)
phone = h2j(phone)
print('after h2j: ',phone)
phone = list(filter(lambda p: p != ' ', phone))
phone = '{' + '}{'.join(phone) + '}'
print('phone: ',phone)
phone = re.sub(r'\{[^\w\s]?\}', '{sil}', phone)
print('after re.sub: ',phone)
phone = phone.replace('}{', ' ')
print('|' + phone + '|')
sequence = np.array(text_to_sequence(phone,hp.text_cleaners))
sequence = np.stack([sequence])
return torch.from_numpy(sequence).long().to(device)
def get_FastSpeech2(num):
checkpoint_path = os.path.join(hp.checkpoint_path, "checkpoint_{}.pth.tar".format(num))
model = nn.DataParallel(FastSpeech2())
model.load_state_dict(torch.load(checkpoint_path)['model'])
model.requires_grad = False
model.eval()
return model
def synthesize(model, vocoder, text, sentence, dur_pitch_energy_aug, prefix=''):
sentence = sentence[:10] # long filename will result in OS Error
mean_mel, std_mel = torch.tensor(np.load(os.path.join(hp.preprocessed_path, "mel_stat.npy")), dtype=torch.float).to(device)
mean_f0, std_f0 = f0_stat = torch.tensor(np.load(os.path.join(hp.preprocessed_path, "f0_stat.npy")), dtype=torch.float).to(device)
mean_energy, std_energy = energy_stat = torch.tensor(np.load(os.path.join(hp.preprocessed_path, "energy_stat.npy")), dtype=torch.float).to(device)
mean_mel, std_mel = mean_mel.reshape(1, -1), std_mel.reshape(1, -1)
mean_f0, std_f0 = mean_f0.reshape(1, -1), std_f0.reshape(1, -1)
mean_energy, std_energy = mean_energy.reshape(1, -1), std_energy.reshape(1, -1)
src_len = torch.from_numpy(np.array([text.shape[1]])).to(device)
mel, mel_postnet, log_duration_output, f0_output, energy_output, _, _, mel_len = model(text, src_len, dur_pitch_energy_aug=dur_pitch_energy_aug, f0_stat=f0_stat, energy_stat=energy_stat)
mel_torch = mel.transpose(1, 2).detach()
mel_postnet_torch = mel_postnet.transpose(1, 2).detach()
f0_output = f0_output[0]
energy_output = energy_output[0]
mel_torch = utils.de_norm(mel_torch.transpose(1, 2), mean_mel, std_mel)
mel_postnet_torch = utils.de_norm(mel_postnet_torch.transpose(1, 2), mean_mel, std_mel).transpose(1, 2)
f0_output = utils.de_norm(f0_output, mean_f0, std_f0).squeeze().detach().cpu().numpy()
energy_output = utils.de_norm(energy_output, mean_energy, std_energy).squeeze().detach().cpu().numpy()
if not os.path.exists(hp.test_path):
os.makedirs(hp.test_path)
Audio.tools.inv_mel_spec(mel_postnet_torch[0], os.path.join(hp.test_path, '{}_griffin_lim_{}.wav'.format(prefix, sentence)))
if vocoder is not None:
if hp.vocoder.lower() == "vocgan":
utils.vocgan_infer(mel_postnet_torch, vocoder, path=os.path.join(hp.test_path, '{}_{}_{}.wav'.format(prefix, hp.vocoder, sentence)))
utils.plot_data([(mel_postnet_torch[0].detach().cpu().numpy(), f0_output, energy_output)], ['Synthesized Spectrogram'], filename=os.path.join(hp.test_path, '{}_{}.png'.format(prefix, sentence)))
if __name__ == "__main__":
# Test
parser = argparse.ArgumentParser()
parser.add_argument('--step', type=int, default=30000)
args = parser.parse_args()
dur_pitch_energy_aug = [1.0, 1.0, 1.0] # [duration, pitch, energy]
model = get_FastSpeech2(args.step).to(device)
if hp.vocoder == 'vocgan':
vocoder = utils.get_vocgan(ckpt_path=hp.vocoder_pretrained_model_path)
else:
vocoder = None
#kss
eval_sentence=['그는 괜찮은 척하려고 애쓰는 것 같았다','그녀의 사랑을 얻기 위해 애썼지만 헛수고였다','용돈을 아껴써라','그는 아내를 많이 아낀다','요즘 공부가 안돼요','한 여자가 내 옆에 앉았다']
train_sentence=['가까운 시일 내에 한번, 댁으로 찾아가겠습니다','우리의 승리는 기적에 가까웠다','아이들의 얼굴에는 행복한 미소가 가득했다','헬륨은 공기보다 가볍다','이것은 간단한 문제가 아니다']
test_sentence=['안녕하세요, 한동대학교 딥러닝 연구실입니다.', '이 프로젝트가 여러분에게 도움이 되었으면 좋겠습니다.', '시간이 촉박해요','이런, 큰일 났어','좀 더 먹지 그래?','제가 뭘 잘못했죠?','더 이상 묻지마']
g2p=G2p()
print('which sentence do you want?')
print('1.eval_sentence 2.train_sentence 3.test_sentence 4.create new sentence')
mode=input()
print('you went for mode {}'.format(mode))
if mode=='4':
print('input sentence')
sentence = input()
elif mode=='1':
sentence = eval_sentence
elif mode=='2':
sentence = train_sentence
elif mode=='3':
sentence = test_sentence
print('sentence that will be synthesized: ')
print(sentence)
if mode != '4':
for s in sentence:
text = kor_preprocess(s)
synthesize(model, vocoder, text, s, dur_pitch_energy_aug, prefix='step_{}-duration_{}-pitch_{}-energy_{}'.format(args.step, dur_pitch_energy_aug[0], dur_pitch_energy_aug[1], dur_pitch_energy_aug[2]))
else:
text = kor_preprocess(sentence)
synthesize(model, vocoder, text, sentence, dur_pitch_energy_aug, prefix='step_{}-pitch_{}-energy_{}'.format(args.step, dur_pitch_energy_aug[0], dur_pitch_energy_aug[1], dur_pitch_energy_aug[2]))