Merge pull request #148 from RealityError/main

用命令行方法生成VITS语音
2023-04-04 12:55:39 +08:00
parent 02e3fd0a09 c4ab2501e4
commit c90fb9f63c
1 changed files with 106 additions and 0 deletions
@@ -0,0 +1,106 @@
+"""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)
+    
+
+    
+