chore(i18n): fix lost locale entries from other languages (except zh_*) (#39)

2026-06-10 04:50:26 +08:00 · 2024-06-12 15:33:13 +07:00
parent 913040806c
commit aec56ec0b4
7 changed files with 184 additions and 184 deletions
--- a/i18n/locale/ko_KR.json
+++ b/i18n/locale/ko_KR.json
@@ -1,14 +1,14 @@
 {
    "### Model comparison\n> You can get model ID (long) from `View model information` below.\n\nCalculate a similarity between two models.": "### Model comparison\n> You can get model ID (long) from `View model information` below.\n\nCalculate a similarity between two models.",
    "### 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.": "### Model fusion\nCan be used to test timbre fusion.",
+    "### Model fusion\nCan be used to test timbre fusion.": "### 모델 융합\n음색 융합 테스트에 사용 가능",
    "### 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.",
+    "### 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. 실험 구성 작성\n실험 데이터는 logs에 저장, 각 실험은 하나의 폴더, 수동으로 실험 이름 경로 입력 필요, 실험 구성, 로그, 훈련된 모델 파일 포함.",
+    "### 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.": "### 모델 정보 보기\n> 오직 weights 폴더에서 추출된 소형 모델 파일만 지원",
-    "#### 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",
+    "#### 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": "입력 소스 볼륨 엔벨로프와 출력 볼륨 엔벨로프의 결합 비율 입력, 1에 가까울수록 출력 엔벨로프 사용",
    "Algorithmic delays (ms)": "알고리즘 지연(ms)",
    "All processes have been completed!": "전체 과정 완료!",
@@ -47,11 +47,11 @@
    "Fusion": "융합",
    "GPU Information": "그래픽 카드 정보",
    "General settings": "일반 설정",
-    "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)",
+    "Hidden": "Hidden",
+    "ID of model A (long)": "ID of model A (long)",
+    "ID of model B (long)": "ID of model B (long)",
+    "ID(long)": "ID (long)",
+    "ID(short)": "ID (short)",
    "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",
@@ -59,25 +59,25 @@
    "Input device": "입력 장치",
    "Input noise reduction": "입력 노이즈 감소",
    "Input voice monitor": "입력 모니터링",
-    "Link index to outside folder": "链接索引到外部",
+    "Link index to outside folder": "Link index to outside folder",
    "Load model": "모델 로드",
    "Load pre-trained base model D path": "미리 훈련된 베이스 모델 D 경로 로드",
    "Load pre-trained base model G path": "미리 훈련된 베이스 모델 G 경로 로드",
    "Loudness factor": "음량 인자",
    "Model": "모델",
-    "Model Author": "模型作者",
-    "Model Author (Nullable)": "模型作者(可空)",
+    "Model Author": "Model Author",
+    "Model Author (Nullable)": "Model Author (Nullable)",
    "Model Inference": "모델 추론",
    "Model architecture version": "모델 버전 및 모델",
    "Model info": "모델 정보",
    "Model information to be modified": "변경할 모델 정보",
    "Model information to be placed": "삽입할 모델 정보",
-    "Model name": "模型名",
+    "Model name": "Model name",
    "Modify": "수정",
    "Multiple audio files can also be imported. If a folder path exists, this input is ignored.": "여러 오디오 파일을 일괄 입력할 수도 있음, 둘 중 하나 선택, 폴더 우선 읽기",
    "No": "아니오",
    "None": "None",
-    "Not exist": "无",
+    "Not exist": "Not exist",
    "Number of CPU processes used for harvest pitch algorithm": "harvest 프로세스 수",
    "Number of CPU processes used for pitch extraction and data processing": "음높이 추출 및 데이터 처리에 사용되는 CPU 프로세스 수",
    "One-click training": "원클릭 훈련",
@@ -113,15 +113,15 @@
    "Save name": "저장 이름",
    "Save only the latest '.ckpt' file to save disk space": "디스크 공간을 절약하기 위해 최신 ckpt 파일만 저장할지 여부",
    "Saved model name (without extension)": "저장된 모델명은 접미사 없음",
-    "Sealing date": "封装时间",
+    "Sealing date": "Sealing date",
    "Search feature ratio (controls accent strength, too high has artifacting)": "검색 특징 비율",
    "Select Speaker/Singer ID": "화자 ID 선택",
    "Select the .index file": ".index 파일 선택",
    "Select the .pth file": ".pth 파일 선택",
    "Select the pitch extraction algorithm ('pm': faster extraction but lower-quality speech; 'harvest': better bass but extremely slow; 'crepe': better quality but GPU intensive), 'rmvpe': best quality, and little GPU requirement": "음높이 추출 알고리즘 선택, 노래 입력 시 pm으로 속도 향상, harvest는 저음이 좋지만 매우 느림, crepe는 효과가 좋지만 GPU 사용, rmvpe는 효과가 가장 좋으며 GPU를 적게 사용",
    "Select the pitch extraction algorithm: when extracting singing, you can use 'pm' to speed up. For high-quality speech with fast performance, but worse CPU usage, you can use 'dio'. 'harvest' results in better quality but is slower.  'rmvpe' has the best results and consumes less CPU/GPU": "음높이 추출 알고리즘 선택: 노래 입력 시 pm으로 속도 향상, 고품질 음성에는 CPU가 부족할 때 dio 사용, harvest는 품질이 더 좋지만 느림, rmvpe는 효과가 가장 좋으며 CPU/GPU를 적게 사용",
-    "Similarity": "相似度",
-    "Similarity (from 0 to 1)": "相似度(0到1)",
+    "Similarity": "Similarity",
+    "Similarity (from 0 to 1)": "Similarity (from 0 to 1)",
    "Single inference": "단일 추론",
    "Specify output folder": "출력 파일 폴더 지정",
    "Specify the output folder for accompaniment": "주된 목소리가 아닌 출력 폴더 지정",
@@ -130,10 +130,10 @@
    "Step 1: Processing data": "step1: 데이터 처리 중",
    "Step 3a: Model training started": "step3a: 모델 훈련 중",
    "Stop audio conversion": "오디오 변환 중지",
-    "Successfully built index into": "成功构建索引到",
-    "Takeover WASAPI device": "独占 WASAPI 设备",
+    "Successfully built index into": "Successfully built index into",
+    "Takeover WASAPI device": "Takeover WASAPI device",
    "Target sample rate": "목표 샘플링률",
-    "The audio file to be processed": "待处理音频文件",
+    "The audio file to be processed": "The audio file to be processed",
    "This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible. <br>If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory <b>Agreement-LICENSE.txt</b> for details.": "이 소프트웨어는 MIT 라이선스로 공개되며, 저자는 소프트웨어에 대해 어떠한 통제권도 가지지 않습니다. 모든 귀책사유는 소프트웨어 사용자 및 소프트웨어에서 생성된 결과물을 사용하는 당사자에게 있습니다. <br>해당 조항을 인정하지 않는 경우, 소프트웨어 패키지의 어떠한 코드나 파일도 사용하거나 인용할 수 없습니다. 자세한 내용은 루트 디렉토리의 <b>LICENSE</b>를 참조하세요.",
    "Total training epochs (total_epoch)": "총 훈련 라운드 수 total_epoch",
    "Train": "훈련",