chore(i18n): sync locale on dev (#34)

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

i18n/locale/ko_KR.json

@@ -1,10 +1,12 @@
 {
     "### 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.": "### 모델 추출\n> logs 폴더 아래의 큰 파일 모델 경로 입력\n\n훈련 중간에 중단한 모델의 자동 추출 및 소형 파일 모델 저장이 안 되거나 중간 모델을 테스트하고 싶은 경우에 적합",
-    "### Model fusion\nCan be used to test timbre fusion.### 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.": "### Model fusion\nCan be used to test timbre fusion.### 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.",
+    "### Model fusion\nCan be used to test timbre fusion.": "### Model fusion\nCan be used to test timbre fusion.",
     "### Modify model information\n> Only supported for small model files extracted from the 'weights' folder.": "### 모델 정보 수정\n> 오직 weights 폴더 아래에서 추출된 작은 모델 파일만 지원",
+    "### 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 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.": "### 모델 정보 보기\n> 오직 weights 폴더에서 추출된 소형 모델 파일만 지원",
+    "### 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",
     "#### 2. Feature extraction.\nUse CPU to extract pitch (if the model has pitch), use GPU to extract features (select GPU index).": "#### 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).",
     "Actually calculated": "实际计算",
     "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": "입력 소스 볼륨 엔벨로프와 출력 볼륨 엔벨로프의 결합 비율 입력, 1에 가까울수록 출력 엔벨로프 사용",
@@ -48,8 +50,8 @@
     "Hidden": "不显示",
     "ID of model A (long)": "A模型ID(长)",
     "ID of model B (long)": "B模型ID(长)",
+    "ID(long)": "ID(long)",
     "ID(short)": "ID(短)",
-    "ID(长)": "ID(长)",
     "If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness.": ">=3인 경우 harvest 피치 인식 결과에 중간값 필터 적용, 필터 반경은 값으로 지정, 사용 시 무성음 감소 가능",
     "Inference time (ms)": "추론 시간 (ms)",
     "Inferencing voice": "추론 음색",
@@ -153,5 +155,6 @@
     "ckpt Processing": "ckpt 처리",
     "index path cannot contain unicode characters": "index 파일 경로는 중국어를 포함할 수 없음",
     "pth path cannot contain unicode characters": "pth 파일 경로는 중국어를 포함할 수 없음",
-    "step2:Pitch extraction & feature extraction": "step2: 음높이 추출 & 특징 추출 중"
+    "step2:Pitch extraction & feature extraction": "step2: 음높이 추출 & 특징 추출 중",
+    "模型作者": "模型作者"
 }