chore(i18n): sync locale on dev (#2100)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

i18n/locale/en_US.json

@@ -4,6 +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).",
     ">=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",
@@ -14,20 +18,18 @@
     "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调": "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation",
     "ID(短)": "ID(short)",
     "ID(长)": "ID(long)",
+    "None": "None",
     "Onnx导出": "Export Onnx",
     "Onnx输出路径": "Onnx Export Path",
     "RVC模型路径": "RVC Model Path",
+    "Unknown": "Unknown",
     "ckpt处理": "ckpt Processing",
     "harvest进程数": "Number of CPU processes used for harvest pitch algorithm",
     "index文件路径不可包含中文": "index path cannot contain unicode characters",
     "pth文件路径不可包含中文": "pth path cannot contain unicode characters",
     "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程": "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1",
-    "step1: 填写实验配置. 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. Experimental 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.",
     "step1:正在处理数据": "Step 1: Processing data",
     "step2:正在提取音高&正在提取特征": "step2:Pitch extraction & feature extraction",
-    "step2a: 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 2a: Automatically 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.",
-    "step2b: 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)": "Step 2b: Use CPU to extract pitch (if the model has pitch), use GPU to extract features (select GPU index)",
-    "step3: 填写训练设置, 开始训练模型和索引": "Step 3: Fill in the training settings and start training the model and index",
     "step3a:正在训练模型": "Step 3a: Model training started",
     "一键训练": "One-click training",
     "不显示": "Hidden",
@@ -92,6 +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)",
     "模型信息": "Model info",
     "模型名": "Model name",
     "模型推理": "Model Inference",