fix(i18n): missing translations

Author: fumiama

i18n/locale/en_US.json

@@ -6,7 +6,7 @@
     "### Step 2. Audio processing. \n#### 1. Slicing.\nAutomatically traverse all files in the training folder that can be decoded into audio and perform slice normalization. Generates 2 wav folders in the experiment directory. Currently, only single-singer/speaker training is supported.": "### Step 2. Audio processing. \n#### 1. Slicing.\nAutomatically traverse all files in the training folder that can be decoded into audio and perform slice normalization. Generates 2 wav folders in the experiment directory. Currently, only single-singer/speaker training is supported.",
     "### Step 3. Start training.\nFill in the training settings and start training the model and index.": "### Step 3. Start training.\nFill in the training settings and start training the model and index.",
     "### View model information\n> Only supported for small model files extracted from the 'weights' folder.": "### View model information\n> Only supported for small model files extracted from the 'weights' folder.",
-    "### 模型比较\n> 模型ID(长)请于下方`查看模型信息`中获得\n\n可用于比较两模型推理相似度": "### 模型比较\n> 模型ID(长)请于下方`查看模型信息`中获得\n\n可用于比较两模型推理相似度",
+    "### Model comparison\n> You can get model ID (long) from `View model information` below.\n\nCalculate a similarity between two models.": "### 模型比较\n> 模型ID(长)请于下方`查看模型信息`中获得\n\n可用于比较两模型推理相似度",
     "#### 2. Feature extraction.\nUse CPU to extract pitch (if the model has pitch), use GPU to extract features (select GPU index).": "#### 2. Feature extraction.\nUse CPU to extract pitch (if the model has pitch), use GPU to extract features (select GPU index).",
     "Actually calculated": "Actually calculated",
     "Adjust the volume envelope scaling. Closer to 0, the more it mimicks the volume of the original vocals. Can help mask noise and make volume sound more natural when set relatively low. Closer to 1 will be more of a consistently loud volume": "Adjust the volume envelope scaling. Closer to 0, the more it mimicks the volume of the original vocals. Can help mask noise and make volume sound more natural when set relatively low. Closer to 1 will be more of a consistently loud volume",

i18n/locale/zh_CN.json

@@ -1,8 +1,8 @@
 {
     "### Model extraction\n> Enter the path of the large file model under the 'logs' folder.\n\nThis is useful if you want to stop training halfway and manually extract and save a small model file, or if you want to test an intermediate model.": "### 模型提取\n> 输入logs文件夹下大文件模型路径\n\n适用于训一半不想训了模型没有自动提取保存小文件模型, 或者想测试中间模型的情况",
-    "### Model fusion\nCan be used to test timbre fusion.": "### Model fusion\nCan be used to test timbre fusion.",
+    "### Model fusion\nCan be used to test timbre fusion.": "### 模型融合\n可用于测试音色融合",
     "### Modify model information\n> Only supported for small model files extracted from the 'weights' folder.": "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件",
-    "### Step 1. Fill in the experimental configuration.\nExperimental data is stored in the 'logs' folder, with each experiment having a separate folder. Manually enter the experiment name path, which contains the experimental configuration, logs, and trained model files.": "### Step 1. Fill in the experimental configuration.\nExperimental data is stored in the 'logs' folder, with each experiment having a separate folder. Manually enter the experiment name path, which contains the experimental configuration, logs, and trained model files.",
+    "### Step 1. Fill in the experimental configuration.\nExperimental data is stored in the 'logs' folder, with each experiment having a separate folder. Manually enter the experiment name path, which contains the experimental configuration, logs, and trained model files.": "### 第一步 填写实验配置\n实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件.",
     "### Step 2. Audio processing. \n#### 1. Slicing.\nAutomatically traverse all files in the training folder that can be decoded into audio and perform slice normalization. Generates 2 wav folders in the experiment directory. Currently, only single-singer/speaker training is supported.": "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.",
     "### Step 3. Start training.\nFill in the training settings and start training the model and index.": "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.",
     "### View model information\n> Only supported for small model files extracted from the 'weights' folder.": "### 查看模型信息\n> 仅支持weights文件夹下提取的小模型文件",
@@ -76,7 +76,7 @@
     "Modify": "修改",
     "Multiple audio files can also be imported. If a folder path exists, this input is ignored.": "也可批量输入音频文件, 二选一, 优先读文件夹",
     "No": "否",
-    "None": "None",
+    "None": "空",
     "Not exist": "无",
     "Number of CPU processes used for harvest pitch algorithm": "harvest进程数",
     "Number of CPU processes used for pitch extraction and data processing": "提取音高和处理数据使用的CPU进程数",
@@ -142,7 +142,7 @@
     "Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder.": "训练结束, 您可查看控制台训练日志或实验文件夹下的train.log",
     "Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)": "变调(整数, 半音数量, 升八度12降八度-12)",
     "Unfortunately, there is no compatible GPU available to support your training.": "很遗憾您这没有能用的显卡来支持您训练",
-    "Unknown": "Unknown",
+    "Unknown": "未知",
     "Unload model to save GPU memory": "卸载音色省显存",
     "Version": "版本",
     "View": "查看",

infer/modules/vc/info.py

@@ -48,7 +48,7 @@ def show_model_info(cpt, show_long_id=False):
             )
         txt = f"""{i18n("Model name")}: %s
 {i18n("Sealing date")}: %s
-{i18n("模型作者")}: %s
+{i18n("Model Author")}: %s
 {i18n("Information")}: %s
 {i18n("Sampling rate")}: %s
 {i18n("Pitch guidance (f0)")}: %s

web.py

@@ -1463,7 +1463,7 @@ def change_f0_method(f0method8):
             with gr.Group():
                 gr.Markdown(
                     value=i18n(
-                        "### 模型比较\n> 模型ID(长)请于下方`查看模型信息`中获得\n\n可用于比较两模型推理相似度"
+                        "### Model comparison\n> You can get model ID (long) from `View model information` below.\n\nCalculate a similarity between two models."
                     )
                 )
                 with gr.Row():