Merge pull request #209 from svc-develop-team/4.1-Stable

stable to latest

README.md

@@ -236,7 +236,13 @@ If the speech_encoder argument is omitted, the default value is vec768l12
 
 **Use loudness embedding**
 
-If loudness embedding is used, the 'vol_aug' and 'vol_embedding' in config.json will be set to true. After use, the trained model will match the loudness of the input source; otherwise, it will be the loudness of the training set.
+Add `--vol_aug` if you want to enable loudness embedding:
+
+```shell
+python preprocess_flist_config.py --speech_encoder vec768l12 --vol_aug
+```
+
+After enabling loudness embedding, the trained model will match the loudness of the input source; otherwise, it will be the loudness of the training set.
 
 ### 3. Generate hubert and f0
 

README_zh_CN.md

@@ -238,7 +238,13 @@ whisper-ppg
 
 **使用响度嵌入**
 
-若使用响度嵌入，需要将config.json中的`vol_aug`,`vol_embedding`设置为true.使用后训练出的模型将匹配到输入源响度，否则为训练集响度。
+若使用响度嵌入，需要增加`--vol_aug`参数，比如：
+
+```shell
+python preprocess_flist_config.py --speech_encoder vec768l12 --vol_aug
+```
+
+使用后训练出的模型将匹配到输入源响度，否则为训练集响度。
 
 ### 3. 生成hubert与f0
 

preprocess_flist_config.py

@@ -29,6 +29,7 @@ if __name__ == "__main__":
     parser.add_argument("--val_list", type=str, default="./filelists/val.txt", help="path to val list")
     parser.add_argument("--source_dir", type=str, default="./dataset/44k", help="path to source dir")
     parser.add_argument("--speech_encoder", type=str, default="vec768l12", help="choice a speech encoder|'vec768l12','vec256l9','hubertsoft','whisper-ppg'")
+    parser.add_argument("--vol_aug", action="store_true", help="Whether to use volume embedding and volume augmentation")
     args = parser.parse_args()
     
     train = []
@@ -90,6 +91,9 @@ if __name__ == "__main__":
         config_template["model"]["ssl_dim"] = config_template["model"]["filter_channels"] = config_template["model"]["gin_channels"] = 1024
         d_config_template["data"]["encoder_out_channels"] = 1024
     
+    if args.vol_aug:
+        config_template["train"]["vol_aug"] = config_template["model"]["vol_embedding"] = True
+
     print("Writing configs/config.json")
     with open("configs/config.json", "w") as f:
         json.dump(config_template, f, indent=2)