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# coding:utf-8
import re
import torch
import unicodedata
from pydub import AudioSegment
from scipy.io.wavfile import write
import commons
import config
import utils
from models import SynthesizerTrn
from text import text_to_sequence
pth_path = config.pth_path
config_json = config.config_json
def wav2mp3(file_name):
sourcefile = AudioSegment.from_wav(f"{file_name}.wav")
sourcefile.export(f"{file_name}.mp3", format="mp3")
def get_text(text, hps, cleaned=False):
if cleaned:
text_norm = text_to_sequence(text, hps.symbols, [])
else:
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
def get_label(text, label):
if f'[{label}]' in text:
return True, text.replace(f'[{label}]', '')
else:
return False, text
def clean_text(text):
jap = re.compile(r'[\u3040-\u309F\u30A0-\u30FF\uAC00-\uD7A3]') # \uAC00-\uD7A3为匹配韩文的，其余为日文
text = f"[JA]{text}[JA]" if jap.search(text) else f"[ZH]{text}[ZH]"
text = unicodedata.normalize('NFKC', text)
return text
def load_model(config_json, pth_path):
dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
hps_ms = utils.get_hparams_from_file(f"./configs/{config_json}")
n_speakers = hps_ms.data.n_speakers if 'n_speakers' in hps_ms.data.keys() else 0
n_symbols = len(hps_ms.symbols) if 'symbols' in hps_ms.keys() else 0
net_g_ms = SynthesizerTrn(
n_symbols,
hps_ms.data.filter_length // 2 + 1,
hps_ms.train.segment_size // hps_ms.data.hop_length,
n_speakers=n_speakers,
**hps_ms.model).to(dev)
_ = net_g_ms.eval()
_ = utils.load_checkpoint(pth_path, net_g_ms)
return net_g_ms, hps_ms
def infer(text, net_g_ms, speaker_id, out_name):
dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
hps_ms = utils.get_hparams_from_file(f"./configs/{config_json}")
with torch.no_grad():
stn_tst = get_text(text, hps_ms, cleaned=False)
x_tst = stn_tst.unsqueeze(0).to(dev)
x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(dev)
sid = torch.LongTensor([speaker_id]).to(dev)
audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=0.667, noise_scale_w=0.8, length_scale=1)[0][
0, 0].data.cpu().float().numpy()
write(f"{out_name}.wav", hps_ms.data.sampling_rate, audio)
wav2mp3(out_name)
return audio
if __name__ == "__main__":
out_name = "水调歌头"
speaker_id = 0
text = "明月几时有，把酒问青天。不知天上宫阙，今夕是何年。我欲乘风归去，又恐琼楼玉宇，高处不胜寒。"
text = clean_text(text)
net_g_ms = load_model(config_json, pth_path)
infer(text, net_g_ms, speaker_id, out_name)
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