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