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"""该模块用于生成VITS文件
使用方法
python cmd_inference.py -m 模型路径 -c 配置文件路径 -o 输出文件路径 -l 输入的语言 -t 输入文本 -s 合成目标说话人名称
可选参数
-ns 感情变化程度
-nsw 音素发音长度
-ls 整体语速
-on 输出文件的名称
"""
from pathlib import Path
import utils
from models import SynthesizerTrn
import torch
from torch import no_grad, LongTensor
import librosa
from text import text_to_sequence, _clean_text
import commons
import scipy.io.wavfile as wavf
import os
device = "cuda:0" if torch.cuda.is_available() else "cpu"
language_marks = {
"Japanese": "",
"日本語": "[JA]",
"简体中文": "[ZH]",
"English": "[EN]",
"Mix": "",
}
def get_text(text, hps, is_symbol):
text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
if hps.data.add_blank:
text_norm = commons.intersperse(text_norm, 0)
text_norm = LongTensor(text_norm)
return text_norm
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description='vits inference')
#必须参数
parser.add_argument('-m', '--model_path', type=str, default="logs/44k/G_0.pth", help='模型路径')
parser.add_argument('-c', '--config_path', type=str, default="configs/config.json", help='配置文件路径')
parser.add_argument('-o', '--output_path', type=str, default="output/vits", help='输出文件路径')
parser.add_argument('-l', '--language', type=str, default="日本語", help='输入的语言')
parser.add_argument('-t', '--text', type=str, help='输入文本')
parser.add_argument('-s', '--spk', type=str, help='合成目标说话人名称')
#可选参数
parser.add_argument('-on', '--output_name', type=str, default="output", help='输出文件的名称')
parser.add_argument('-ns', '--noise_scale', type=float,default= .667,help='感情变化程度')
parser.add_argument('-nsw', '--noise_scale_w', type=float,default=0.6, help='音素发音长度')
parser.add_argument('-ls', '--length_scale', type=float,default=1, help='整体语速')
args = parser.parse_args()
model_path = args.model_path
config_path = args.config_path
output_dir = Path(args.output_path)
output_dir.mkdir(parents=True, exist_ok=True)
language = args.language
text = args.text
spk = args.spk
noise_scale = args.noise_scale
noise_scale_w = args.noise_scale_w
length = args.length_scale
output_name = args.output_name
hps = utils.get_hparams_from_file(config_path)
net_g = SynthesizerTrn(
len(hps.symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model).to(device)
_ = net_g.eval()
_ = utils.load_checkpoint(model_path, net_g, None)
speaker_ids = hps.speakers
if language is not None:
text = language_marks[language] + text + language_marks[language]
speaker_id = speaker_ids[spk]
stn_tst = get_text(text, hps, False)
with no_grad():
x_tst = stn_tst.unsqueeze(0).to(device)
x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
sid = LongTensor([speaker_id]).to(device)
audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
length_scale=1.0 / length)[0][0, 0].data.cpu().float().numpy()
del stn_tst, x_tst, x_tst_lengths, sid
wavf.write(str(output_dir)+"/"+output_name+".wav",hps.data.sampling_rate,audio)
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