chore(i18n): tidy text

i18n/locale/en_US.json

@@ -4,10 +4,10 @@
     "### 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 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.",
     "### 模型融合\n可用于测试音色融合": "### Model fusion\nCan be used to test timbre fusion.",
-    "### 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.": "### 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.",
-    "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.": "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.",
-    "### 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.": "### 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.",
-    "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).": "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).",
+    "### 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 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.",
+    "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.": "### Step 3. Start training.\nFill in the training settings and start training the model and index.",
+    "### 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 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.",
+    "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).": "#### 2. Feature extraction.\nUse CPU to extract pitch (if the model has pitch), use GPU to extract features (select GPU index).",
     ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音": "If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness.",
     "A模型ID(长)": "ID of model A (long)",
     "A模型权重": "Weight (w) for Model A",
@@ -94,8 +94,8 @@
     "查看": "View",
     "检索特征占比": "Search feature ratio (controls accent strength, too high has artifacting)",
     "模型": "Model",
-    "模型作者": "模型作者",
-    "模型作者(可空)": "模型作者(可空)",
+    "模型作者": "Model Author",
+    "模型作者(可空)": "Model Author (Nullable)",
     "模型信息": "Model info",
     "模型名": "Model name",
     "模型推理": "Model Inference",