From 5fbd786f299c78d4220c0cfdb7b6b7bc972289fb Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=BA=90=E6=96=87=E9=9B=A8?=
 <41315874+fumiama@users.noreply.github.com>
Date: Tue, 11 Jun 2024 15:53:42 +0900
Subject: [PATCH 1/7] fix(i18n): standard_file in locale_diff

---
 i18n/locale_diff.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/i18n/locale_diff.py b/i18n/locale_diff.py
index 674f7dd..6407346 100644
--- a/i18n/locale_diff.py
+++ b/i18n/locale_diff.py
@@ -3,7 +3,7 @@ import os
 from collections import OrderedDict
 
 # Define the standard file name
-standard_file = "locale/zh_CN.json"
+standard_file = "locale/en_US.json"
 
 # Find all JSON files in the directory
 dir_path = "locale/"

From 2f2fae3698fca4d2b98a7a6086609df8ac15bdd0 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]"
 <41898282+github-actions[bot]@users.noreply.github.com>
Date: Tue, 11 Jun 2024 15:59:14 +0900
Subject: [PATCH 2/7] chore(i18n): sync locale on dev (#34)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
---
 i18n/locale/en_US.json | 9 ++++++---
 i18n/locale/es_ES.json | 9 ++++++---
 i18n/locale/fr_FR.json | 9 ++++++---
 i18n/locale/it_IT.json | 9 ++++++---
 i18n/locale/ja_JP.json | 9 ++++++---
 i18n/locale/ko_KR.json | 9 ++++++---
 i18n/locale/pt_BR.json | 9 ++++++---
 i18n/locale/ru_RU.json | 9 ++++++---
 i18n/locale/tr_TR.json | 9 ++++++---
 i18n/locale/zh_CN.json | 9 ++++++---
 i18n/locale/zh_HK.json | 9 ++++++---
 i18n/locale/zh_SG.json | 9 ++++++---
 i18n/locale/zh_TW.json | 9 ++++++---
 13 files changed, 78 insertions(+), 39 deletions(-)

diff --git a/i18n/locale/en_US.json b/i18n/locale/en_US.json
index 6cf6a47..3bd384e 100644
--- a/i18n/locale/en_US.json
+++ b/i18n/locale/en_US.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.": "### 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.",
-    "### 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.": "### Modify model information\n> Only supported for small model files extracted from the 'weights' folder.",
+    "### 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.": "### 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",
     "#### 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",
@@ -48,8 +50,8 @@
     "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)",
-    "ID(长)": "ID(长)",
     "If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness.": "If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness.",
     "Inference time (ms)": "Inference time (ms)",
     "Inferencing voice": "Inferencing voice",
@@ -153,5 +155,6 @@
     "ckpt Processing": "ckpt Processing",
     "index path cannot contain unicode characters": "index path cannot contain unicode characters",
     "pth path cannot contain unicode characters": "pth path cannot contain unicode characters",
-    "step2:Pitch extraction & feature extraction": "step2:Pitch extraction & feature extraction"
+    "step2:Pitch extraction & feature extraction": "step2:Pitch extraction & feature extraction",
+    "模型作者": "模型作者"
 }
diff --git a/i18n/locale/es_ES.json b/i18n/locale/es_ES.json
index 5ab3e23..fdc2e01 100644
--- a/i18n/locale/es_ES.json
+++ b/i18n/locale/es_ES.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.": "### Extracción de modelo\n> Ingrese la ruta de un archivo de modelo grande en la carpeta 'logs'.\n\nAplicable cuando desea extraer un archivo de modelo pequeño después de entrenar a mitad de camino y no se guardó automáticamente, o cuando desea probar un modelo intermedio.",
-    "### 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.": "### Modificar la información del modelo\n> Solo admite archivos de modelos pequeños extraídos en la carpeta 'weights'.",
+    "### 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.": "### Paso dos: Procesamiento de audio\n#### 1. Segmentación de audio\nRecorre automáticamente todos los archivos que se pueden decodificar en audio en la carpeta de entrenamiento y realiza la segmentación y normalización, generando 2 carpetas wav en el directorio del experimento; por ahora solo se admite el entrenamiento individual.",
     "### Step 3. Start training.\nFill in the training settings and start training the model and index.": "### Paso tres: Comienza el entrenamiento\nCompleta la configuración de entrenamiento, comienza a entrenar el modelo y el índice.",
     "### View model information\n> Only supported for small model files extracted from the 'weights' folder.": "### Ver información del modelo\n> Solo aplicable a archivos de modelos pequeños extraídos de la carpeta 'weights'.",
+    "### 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. Extracción de características\nUtiliza la CPU para extraer el tono (si el modelo tiene tono), utiliza la GPU para extraer características (selecciona el número de tarjeta).",
     "Actually calculated": "Valor realmente calculado",
     "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": "Proporción de fusión para reemplazar el sobre de volumen de entrada con el sobre de volumen de salida, cuanto más cerca de 1, más se utiliza el sobre de salida",
@@ -48,8 +50,8 @@
     "Hidden": "Oculto",
     "ID of model A (long)": "ID del modelo A (largo)",
     "ID of model B (long)": "ID del modelo B (largo)",
+    "ID(long)": "ID(long)",
     "ID(short)": "ID (corto)",
-    "ID(长)": "ID(长)",
     "If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness.": "Si es >=3, entonces use el resultado del reconocimiento de tono de 'harvest' con filtro de mediana, el valor es el radio del filtro, su uso puede debilitar el sonido sordo",
     "Inference time (ms)": "Inferir tiempo (ms)",
     "Inferencing voice": "inferencia de voz",
@@ -153,5 +155,6 @@
     "ckpt Processing": "Procesamiento de recibos",
     "index path cannot contain unicode characters": "La ruta del archivo .index no debe contener caracteres chinos.",
     "pth path cannot contain unicode characters": "La ruta del archivo .pth no debe contener caracteres chinos.",
-    "step2:Pitch extraction & feature extraction": "Paso 2: Extracción del tono y extracción de características"
+    "step2:Pitch extraction & feature extraction": "Paso 2: Extracción del tono y extracción de características",
+    "模型作者": "模型作者"
 }
diff --git a/i18n/locale/fr_FR.json b/i18n/locale/fr_FR.json
index 6f92f19..b07f0ef 100644
--- a/i18n/locale/fr_FR.json
+++ b/i18n/locale/fr_FR.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.": "### Extraction du modèle\n> Saisissez le chemin d'accès au modèle du grand fichier dans le dossier \"logs\".\n\nCette fonction est utile si vous souhaitez arrêter l'entrainement à mi-chemin et extraire et enregistrer manuellement un petit fichier de modèle, ou si vous souhaitez tester un modèle intermédiaire.",
-    "### 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.": "### Modifier les informations du modèle\n> Uniquement pris en charge pour les petits fichiers de modèle extraits du dossier 'weights'.",
+    "### 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.": "### Deuxième étape : Traitement audio\n#### 1. Découpage de l'audio\nParcourez automatiquement tous les fichiers qui peuvent être décodés en audio dans le dossier d'entraînement et effectuez le découpage et la normalisation. Deux dossiers wav sont générés dans le répertoire de l'expérience. Pour le moment, seul l'entraînement individuel est pris en charge.",
     "### Step 3. Start training.\nFill in the training settings and start training the model and index.": "### Troisième étape : Commencer l'entraînement\nRemplissez les paramètres d'entraînement, commencez à entraîner le modèle et l'index.",
     "### View model information\n> Only supported for small model files extracted from the 'weights' folder.": "### Afficher les informations sur le modèle\n> Uniquement pour les petits fichiers de modèle extraits du dossier 'weights'.",
+    "### 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. Extraction des caractéristiques\nUtilisez le CPU pour extraire la hauteur tonale (si le modèle a une hauteur tonale), utilisez le GPU pour extraire les caractéristiques (sélectionnez le numéro de carte).",
     "Actually calculated": "Effectivement calculé",
     "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": "Ajustez l'échelle de l'enveloppe de volume. Plus il est proche de 0, plus il imite le volume des voix originales. Cela peut aider à masquer les bruits et à rendre le volume plus naturel lorsqu'il est réglé relativement bas. Plus le volume est proche de 1, plus le volume sera fort et constant :",
@@ -48,8 +50,8 @@
     "Hidden": "Caché",
     "ID of model A (long)": "ID du modèle A (long)",
     "ID of model B (long)": "ID du modèle B (long)",
+    "ID(long)": "ID(long)",
     "ID(short)": "ID (court)",
-    "ID(长)": "ID(长)",
     "If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness.": "Si >=3 : appliquer un filtrage médian aux résultats de la reconnaissance de la hauteur de récolte. La valeur représente le rayon du filtre et peut réduire la respiration.",
     "Inference time (ms)": "Temps d'inférence (ms)",
     "Inferencing voice": "Voix pour l'inférence",
@@ -153,5 +155,6 @@
     "ckpt Processing": "Traitement des fichiers .ckpt",
     "index path cannot contain unicode characters": "Le chemin du fichier d'index ne doit pas contenir de caractères chinois.",
     "pth path cannot contain unicode characters": "Le chemin du fichier .pth ne doit pas contenir de caractères chinois.",
-    "step2:Pitch extraction & feature extraction": "Étape 2 : Extraction de la hauteur et extraction des caractéristiques en cours."
+    "step2:Pitch extraction & feature extraction": "Étape 2 : Extraction de la hauteur et extraction des caractéristiques en cours.",
+    "模型作者": "模型作者"
 }
diff --git a/i18n/locale/it_IT.json b/i18n/locale/it_IT.json
index fb477e6..1afd75b 100644
--- a/i18n/locale/it_IT.json
+++ b/i18n/locale/it_IT.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.": "### Estrazione del modello\n> Inserire il percorso del modello di file di grandi dimensioni nella cartella \"logs\".",
-    "### 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.": "### Modifica le informazioni sul modello\n> Supportato solo per i file di modello di piccole dimensioni estratti dalla cartella 'weights'.",
+    "### 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.": "### Visualizza le informazioni sul modello\n> Supportato solo per file di modello piccoli estratti dalla cartella 'weights'.",
+    "### 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": "Regola il ridimensionamento dell'inviluppo del volume. ",
@@ -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.": "Se >=3: applica il filtro mediano ai risultati del pitch raccolto. ",
     "Inference time (ms)": "Tempo di inferenza (ms)",
     "Inferencing voice": "Voce di inferenza:",
@@ -153,5 +155,6 @@
     "ckpt Processing": "Elaborazione ckpt",
     "index path cannot contain unicode characters": "index文件路径不可包含中文",
     "pth path cannot contain unicode characters": "pth è un'app per il futuro",
-    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征"
+    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征",
+    "模型作者": "模型作者"
 }
diff --git a/i18n/locale/ja_JP.json b/i18n/locale/ja_JP.json
index 3eaaee6..2a055f0 100644
--- a/i18n/locale/ja_JP.json
+++ b/i18n/locale/ja_JP.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.": "### モデル抽出\n> ログフォルダー内の大モデルのパスを入力\n\nモデルを半分まで学習し、小モデルを保存しなかった場合、又は中間モデルをテストしたい場合に適用されます。",
-    "### 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.": "### モデル情報の修正\n> `weights`フォルダから抽出された小モデルのみ対応",
+    "### 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.": "### 第二歩 音声処理\n#### 1. 音声切分\n学習フォルダー内のすべての音声ファイルを自動的に探し出し、切分と正規化を行い、2つのwavフォルダーを実験ディレクトリに生成します。現在は単人モデルの学習のみを支援しています。",
     "### Step 3. Start training.\nFill in the training settings and start training the model and index.": "### 第三歩 学習開始\n学習設定を入力して、モデルと索引の学習を開始します。",
     "### View model information\n> Only supported for small model files extracted from the 'weights' folder.": "### モデル情報を表示\n> `weights`フォルダから抽出された小さなのみ対応",
+    "### 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. 特徴抽出\nCPUで音高を抽出し(モデルに音高がある場合のみ)、GPUで特徴を抽出する(GPU番号を選択すべし)",
     "Actually calculated": "実際計算",
     "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": "入力ソースの音量エンベロープと出力音量エンベロープの融合率 1に近づくほど、出力音量エンベロープの割合が高くなる",
@@ -48,8 +50,8 @@
     "Hidden": "無表示",
     "ID of model A (long)": "AモデルID(長)",
     "ID of model B (long)": "BモデルID(長)",
+    "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.": ">=3 次に、harvestピッチの認識結果に対してメディアンフィルタを使用します。値はフィルター半径で、ミュートを減衰させるために使用します。",
     "Inference time (ms)": "推論時間(ms)",
     "Inferencing voice": "音源推論",
@@ -153,5 +155,6 @@
     "ckpt Processing": "ckptファイルの処理",
     "index path cannot contain unicode characters": "indexファイルのパスに漢字を含んではいけません",
     "pth path cannot contain unicode characters": "pthファイルのパスに漢字を含んではいけません",
-    "step2:Pitch extraction & feature extraction": "step2:ピッチ抽出と特徴抽出"
+    "step2:Pitch extraction & feature extraction": "step2:ピッチ抽出と特徴抽出",
+    "模型作者": "模型作者"
 }
diff --git a/i18n/locale/ko_KR.json b/i18n/locale/ko_KR.json
index 96ee976..b5032c2 100644
--- a/i18n/locale/ko_KR.json
+++ b/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"
 }
diff --git a/i18n/locale/pt_BR.json b/i18n/locale/pt_BR.json
index 4fb32c5..01c61a1 100644
--- a/i18n/locale/pt_BR.json
+++ b/i18n/locale/pt_BR.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.": "### Extração do modelo\n> Insira o caminho do modelo de arquivo grande na pasta 'logs'.\n\nIsso é útil se você quiser interromper o treinamento no meio do caminho e extrair e salvar manualmente um arquivo de modelo pequeno, ou se quiser testar um modelo intermediário.",
-    "### 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.": "### Modificar informações do modelo\n> Suportado apenas para arquivos de modelo pequenos extraídos da pasta 'weights'.",
+    "### 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.": "### Exibir informações do modelo\n> Suportado apenas para arquivos de modelo pequenos extraídos da pasta 'weights'.",
+    "### 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": "O envelope de volume da fonte de entrada substitui a taxa de fusão do envelope de volume de saída, quanto mais próximo de 1, mais o envelope de saída é usado:",
@@ -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.": ">=3, use o filtro mediano para o resultado do reconhecimento do tom da heverst, e o valor é o raio do filtro, que pode enfraquecer o mudo.",
     "Inference time (ms)": "Tempo de inferência (ms)",
     "Inferencing voice": "Escolha o seu Modelo:",
@@ -153,5 +155,6 @@
     "ckpt Processing": "processamento ckpt",
     "index path cannot contain unicode characters": "O caminho do arquivo de Index não pode conter caracteres chineses",
     "pth path cannot contain unicode characters": "o caminho do arquivo pth não pode conter caracteres chineses",
-    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征"
+    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征",
+    "模型作者": "模型作者"
 }
diff --git a/i18n/locale/ru_RU.json b/i18n/locale/ru_RU.json
index e9b7f6c..58edfc8 100644
--- a/i18n/locale/ru_RU.json
+++ b/i18n/locale/ru_RU.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.": "### Создание модели из данных\n> Полученных в процессе обучения (введите путь к большому файлу модели в папке 'logs').\n\nМожет пригодиться, если вам нужно завершить обучение и получить маленький файл готовой модели, или если вам нужно проверить недообученную модель.",
-    "### 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.": "### Изменить информацию о модели\n> Работает только с маленькими моделями, взятыми из папки 'weights'.",
+    "### 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.": "### Просмотреть информацию о модели\n> Работает только с маленькими моделями, взятыми из папки 'weights'.",
+    "### 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": "Использовать громкость входного файла для замены или перемешивания с громкостью выходного файла. Чем ближе соотношение к 1, тем больше используется звука из выходного файла:",
@@ -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.": "Если значение больше 3: применить медианную фильтрацию к вытащенным тональностям. Значение контролирует радиус фильтра и может уменьшить излишнее дыхание.",
     "Inference time (ms)": "Время переработки (мс)",
     "Inferencing voice": "Желаемый голос:",
@@ -153,5 +155,6 @@
     "ckpt Processing": "Обработка ckpt",
     "index path cannot contain unicode characters": "Путь к файлу индекса",
     "pth path cannot contain unicode characters": "Путь к файлу pth",
-    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征"
+    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征",
+    "模型作者": "模型作者"
 }
diff --git a/i18n/locale/tr_TR.json b/i18n/locale/tr_TR.json
index 5bf105a..ad894dc 100644
--- a/i18n/locale/tr_TR.json
+++ b/i18n/locale/tr_TR.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.": "### Model çıkartma\n> Büyük dosya modeli yolunu 'logs' klasöründe girin.\n\nBu, eğitimi yarıda bırakmak istediğinizde ve manuel olarak küçük bir model dosyası çıkartmak ve kaydetmek istediğinizde veya bir ara modeli test etmek istediğinizde kullanışlıdır.",
-    "### 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.": "### Model bilgilerini düzenle\n> Sadece 'weights' klasöründen çıkarılan küçük model dosyaları desteklenir.",
+    "### 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.": "### Model bilgilerini görüntüle\n> Sadece 'weights' klasöründen çıkarılan küçük model dosyaları desteklenir.",
+    "### 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": "Sesin hacim zarfını ayarlayın. 0'a yakın değerler, sesin orijinal vokallerin hacmine benzer olmasını sağlar. Düşük bir değerle ses gürültüsünü maskeleyebilir ve hacmi daha doğal bir şekilde duyulabilir hale getirebilirsiniz. 1'e yaklaştıkça sürekli bir yüksek ses seviyesi elde edilir:",
@@ -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.": "Eğer >=3 ise, elde edilen pitch sonuçlarına median filtreleme uygula. Bu değer, filtre yarıçapını temsil eder ve nefesliliği azaltabilir.",
     "Inference time (ms)": "Çıkarsama süresi (ms)",
     "Inferencing voice": "Ses çıkartma (Inference):",
@@ -153,5 +155,6 @@
     "ckpt Processing": "ckpt İşleme",
     "index path cannot contain unicode characters": ".index dosya yolu Çince karakter içeremez",
     "pth path cannot contain unicode characters": ".pth dosya yolu Çince karakter içeremez",
-    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征"
+    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征",
+    "模型作者": "模型作者"
 }
diff --git a/i18n/locale/zh_CN.json b/i18n/locale/zh_CN.json
index 5b85d39..76c1e77 100644
--- a/i18n/locale/zh_CN.json
+++ b/i18n/locale/zh_CN.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.": "### 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.### 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.": "### 模型融合\n可用于测试音色融合",
+    "### 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.": "### 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 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",
+    "### 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": "输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络",
@@ -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.": ">=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",
     "Inference time (ms)": "推理时间(ms)",
     "Inferencing voice": "推理音色",
@@ -153,5 +155,6 @@
     "ckpt Processing": "ckpt处理",
     "index path cannot contain unicode characters": "index文件路径不可包含中文",
     "pth path cannot contain unicode characters": "pth文件路径不可包含中文",
-    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征"
+    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征",
+    "模型作者": "模型作者"
 }
diff --git a/i18n/locale/zh_HK.json b/i18n/locale/zh_HK.json
index bd7aa7b..637a64b 100644
--- a/i18n/locale/zh_HK.json
+++ b/i18n/locale/zh_HK.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.": "### 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.### 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.": "### 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 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",
+    "### 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": "輸入源音量包絡替換輸出音量包絡融合比例，越靠近1越使用輸出包絡",
@@ -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.": ">=3則使用對harvest音高識別的結果使用中值濾波，數值為濾波半徑，使用可以削弱啞音",
     "Inference time (ms)": "推理時間(ms)",
     "Inferencing voice": "推理音色",
@@ -153,5 +155,6 @@
     "ckpt Processing": "ckpt處理",
     "index path cannot contain unicode characters": "index文件路径不可包含中文",
     "pth path cannot contain unicode characters": "pth文件路径不可包含中文",
-    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征"
+    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征",
+    "模型作者": "模型作者"
 }
diff --git a/i18n/locale/zh_SG.json b/i18n/locale/zh_SG.json
index bd7aa7b..637a64b 100644
--- a/i18n/locale/zh_SG.json
+++ b/i18n/locale/zh_SG.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.": "### 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.### 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.": "### 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 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",
+    "### 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": "輸入源音量包絡替換輸出音量包絡融合比例，越靠近1越使用輸出包絡",
@@ -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.": ">=3則使用對harvest音高識別的結果使用中值濾波，數值為濾波半徑，使用可以削弱啞音",
     "Inference time (ms)": "推理時間(ms)",
     "Inferencing voice": "推理音色",
@@ -153,5 +155,6 @@
     "ckpt Processing": "ckpt處理",
     "index path cannot contain unicode characters": "index文件路径不可包含中文",
     "pth path cannot contain unicode characters": "pth文件路径不可包含中文",
-    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征"
+    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征",
+    "模型作者": "模型作者"
 }
diff --git a/i18n/locale/zh_TW.json b/i18n/locale/zh_TW.json
index bd7aa7b..637a64b 100644
--- a/i18n/locale/zh_TW.json
+++ b/i18n/locale/zh_TW.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.": "### 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.### 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.": "### 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 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",
+    "### 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": "輸入源音量包絡替換輸出音量包絡融合比例，越靠近1越使用輸出包絡",
@@ -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.": ">=3則使用對harvest音高識別的結果使用中值濾波，數值為濾波半徑，使用可以削弱啞音",
     "Inference time (ms)": "推理時間(ms)",
     "Inferencing voice": "推理音色",
@@ -153,5 +155,6 @@
     "ckpt Processing": "ckpt處理",
     "index path cannot contain unicode characters": "index文件路径不可包含中文",
     "pth path cannot contain unicode characters": "pth文件路径不可包含中文",
-    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征"
+    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征",
+    "模型作者": "模型作者"
 }

From 4c4492a40e7d8897b64cb4f4c69bb074c39fc033 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=BA=90=E6=96=87=E9=9B=A8?=
 <41315874+fumiama@users.noreply.github.com>
Date: Tue, 11 Jun 2024 16:06:08 +0900
Subject: [PATCH 3/7] fix(i18n): missing translations

---
 i18n/locale/en_US.json   | 2 +-
 i18n/locale/zh_CN.json   | 8 ++++----
 infer/modules/vc/info.py | 2 +-
 web.py                   | 2 +-
 4 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/i18n/locale/en_US.json b/i18n/locale/en_US.json
index 3bd384e..79ac35a 100644
--- a/i18n/locale/en_US.json
+++ b/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",
diff --git a/i18n/locale/zh_CN.json b/i18n/locale/zh_CN.json
index 76c1e77..0f2c6b4 100644
--- a/i18n/locale/zh_CN.json
+++ b/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",
diff --git a/infer/modules/vc/info.py b/infer/modules/vc/info.py
index a396a59..14a7346 100644
--- a/infer/modules/vc/info.py
+++ b/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
diff --git a/web.py b/web.py
index bf61dfe..2265e03 100644
--- a/web.py
+++ b/web.py
@@ -1463,7 +1463,7 @@ with gr.Blocks(title="RVC WebUI") as app:
             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():

From 0dea48e7566f9993d61731206fd3a95c3e133d82 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]"
 <41898282+github-actions[bot]@users.noreply.github.com>
Date: Tue, 11 Jun 2024 16:06:56 +0900
Subject: [PATCH 4/7] chore(i18n): sync locale on dev (#35)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
---
 i18n/locale/en_US.json | 5 ++---
 i18n/locale/es_ES.json | 5 ++---
 i18n/locale/fr_FR.json | 5 ++---
 i18n/locale/it_IT.json | 5 ++---
 i18n/locale/ja_JP.json | 5 ++---
 i18n/locale/ko_KR.json | 5 ++---
 i18n/locale/pt_BR.json | 5 ++---
 i18n/locale/ru_RU.json | 5 ++---
 i18n/locale/tr_TR.json | 5 ++---
 i18n/locale/zh_CN.json | 5 ++---
 i18n/locale/zh_HK.json | 5 ++---
 i18n/locale/zh_SG.json | 5 ++---
 i18n/locale/zh_TW.json | 5 ++---
 13 files changed, 26 insertions(+), 39 deletions(-)

diff --git a/i18n/locale/en_US.json b/i18n/locale/en_US.json
index 79ac35a..1f5f747 100644
--- a/i18n/locale/en_US.json
+++ b/i18n/locale/en_US.json
@@ -1,4 +1,5 @@
 {
+    "### 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.": "### 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.",
     "### 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.": "### Modify model information\n> Only supported for small model files extracted from the 'weights' folder.",
@@ -6,7 +7,6 @@
     "### 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.",
-    "### 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",
@@ -155,6 +155,5 @@
     "ckpt Processing": "ckpt Processing",
     "index path cannot contain unicode characters": "index path cannot contain unicode characters",
     "pth path cannot contain unicode characters": "pth path cannot contain unicode characters",
-    "step2:Pitch extraction & feature extraction": "step2:Pitch extraction & feature extraction",
-    "模型作者": "模型作者"
+    "step2:Pitch extraction & feature extraction": "step2:Pitch extraction & feature extraction"
 }
diff --git a/i18n/locale/es_ES.json b/i18n/locale/es_ES.json
index fdc2e01..b0ecbc7 100644
--- a/i18n/locale/es_ES.json
+++ b/i18n/locale/es_ES.json
@@ -1,4 +1,5 @@
 {
+    "### 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.": "### Extracción de modelo\n> Ingrese la ruta de un archivo de modelo grande en la carpeta 'logs'.\n\nAplicable cuando desea extraer un archivo de modelo pequeño después de entrenar a mitad de camino y no se guardó automáticamente, o cuando desea probar un modelo intermedio.",
     "### 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.": "### Modificar la información del modelo\n> Solo admite archivos de modelos pequeños extraídos en la carpeta 'weights'.",
@@ -6,7 +7,6 @@
     "### 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.": "### Paso dos: Procesamiento de audio\n#### 1. Segmentación de audio\nRecorre automáticamente todos los archivos que se pueden decodificar en audio en la carpeta de entrenamiento y realiza la segmentación y normalización, generando 2 carpetas wav en el directorio del experimento; por ahora solo se admite el entrenamiento individual.",
     "### Step 3. Start training.\nFill in the training settings and start training the model and index.": "### Paso tres: Comienza el entrenamiento\nCompleta la configuración de entrenamiento, comienza a entrenar el modelo y el índice.",
     "### View model information\n> Only supported for small model files extracted from the 'weights' folder.": "### Ver información del modelo\n> Solo aplicable a archivos de modelos pequeños extraídos de la carpeta 'weights'.",
-    "### 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. Extracción de características\nUtiliza la CPU para extraer el tono (si el modelo tiene tono), utiliza la GPU para extraer características (selecciona el número de tarjeta).",
     "Actually calculated": "Valor realmente calculado",
     "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": "Proporción de fusión para reemplazar el sobre de volumen de entrada con el sobre de volumen de salida, cuanto más cerca de 1, más se utiliza el sobre de salida",
@@ -155,6 +155,5 @@
     "ckpt Processing": "Procesamiento de recibos",
     "index path cannot contain unicode characters": "La ruta del archivo .index no debe contener caracteres chinos.",
     "pth path cannot contain unicode characters": "La ruta del archivo .pth no debe contener caracteres chinos.",
-    "step2:Pitch extraction & feature extraction": "Paso 2: Extracción del tono y extracción de características",
-    "模型作者": "模型作者"
+    "step2:Pitch extraction & feature extraction": "Paso 2: Extracción del tono y extracción de características"
 }
diff --git a/i18n/locale/fr_FR.json b/i18n/locale/fr_FR.json
index b07f0ef..cb222d9 100644
--- a/i18n/locale/fr_FR.json
+++ b/i18n/locale/fr_FR.json
@@ -1,4 +1,5 @@
 {
+    "### 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.": "### Extraction du modèle\n> Saisissez le chemin d'accès au modèle du grand fichier dans le dossier \"logs\".\n\nCette fonction est utile si vous souhaitez arrêter l'entrainement à mi-chemin et extraire et enregistrer manuellement un petit fichier de modèle, ou si vous souhaitez tester un modèle intermédiaire.",
     "### 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.": "### Modifier les informations du modèle\n> Uniquement pris en charge pour les petits fichiers de modèle extraits du dossier 'weights'.",
@@ -6,7 +7,6 @@
     "### 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.": "### Deuxième étape : Traitement audio\n#### 1. Découpage de l'audio\nParcourez automatiquement tous les fichiers qui peuvent être décodés en audio dans le dossier d'entraînement et effectuez le découpage et la normalisation. Deux dossiers wav sont générés dans le répertoire de l'expérience. Pour le moment, seul l'entraînement individuel est pris en charge.",
     "### Step 3. Start training.\nFill in the training settings and start training the model and index.": "### Troisième étape : Commencer l'entraînement\nRemplissez les paramètres d'entraînement, commencez à entraîner le modèle et l'index.",
     "### View model information\n> Only supported for small model files extracted from the 'weights' folder.": "### Afficher les informations sur le modèle\n> Uniquement pour les petits fichiers de modèle extraits du dossier 'weights'.",
-    "### 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. Extraction des caractéristiques\nUtilisez le CPU pour extraire la hauteur tonale (si le modèle a une hauteur tonale), utilisez le GPU pour extraire les caractéristiques (sélectionnez le numéro de carte).",
     "Actually calculated": "Effectivement calculé",
     "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": "Ajustez l'échelle de l'enveloppe de volume. Plus il est proche de 0, plus il imite le volume des voix originales. Cela peut aider à masquer les bruits et à rendre le volume plus naturel lorsqu'il est réglé relativement bas. Plus le volume est proche de 1, plus le volume sera fort et constant :",
@@ -155,6 +155,5 @@
     "ckpt Processing": "Traitement des fichiers .ckpt",
     "index path cannot contain unicode characters": "Le chemin du fichier d'index ne doit pas contenir de caractères chinois.",
     "pth path cannot contain unicode characters": "Le chemin du fichier .pth ne doit pas contenir de caractères chinois.",
-    "step2:Pitch extraction & feature extraction": "Étape 2 : Extraction de la hauteur et extraction des caractéristiques en cours.",
-    "模型作者": "模型作者"
+    "step2:Pitch extraction & feature extraction": "Étape 2 : Extraction de la hauteur et extraction des caractéristiques en cours."
 }
diff --git a/i18n/locale/it_IT.json b/i18n/locale/it_IT.json
index 1afd75b..6e859ba 100644
--- a/i18n/locale/it_IT.json
+++ b/i18n/locale/it_IT.json
@@ -1,4 +1,5 @@
 {
+    "### 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.": "### Estrazione del modello\n> Inserire il percorso del modello di file di grandi dimensioni nella cartella \"logs\".",
     "### 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.": "### Modifica le informazioni sul modello\n> Supportato solo per i file di modello di piccole dimensioni estratti dalla cartella 'weights'.",
@@ -6,7 +7,6 @@
     "### 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.": "### Visualizza le informazioni sul modello\n> Supportato solo per file di modello piccoli estratti dalla cartella 'weights'.",
-    "### 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": "Regola il ridimensionamento dell'inviluppo del volume. ",
@@ -155,6 +155,5 @@
     "ckpt Processing": "Elaborazione ckpt",
     "index path cannot contain unicode characters": "index文件路径不可包含中文",
     "pth path cannot contain unicode characters": "pth è un'app per il futuro",
-    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征",
-    "模型作者": "模型作者"
+    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征"
 }
diff --git a/i18n/locale/ja_JP.json b/i18n/locale/ja_JP.json
index 2a055f0..fc2f814 100644
--- a/i18n/locale/ja_JP.json
+++ b/i18n/locale/ja_JP.json
@@ -1,4 +1,5 @@
 {
+    "### 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.": "### モデル抽出\n> ログフォルダー内の大モデルのパスを入力\n\nモデルを半分まで学習し、小モデルを保存しなかった場合、又は中間モデルをテストしたい場合に適用されます。",
     "### 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.": "### モデル情報の修正\n> `weights`フォルダから抽出された小モデルのみ対応",
@@ -6,7 +7,6 @@
     "### 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.": "### 第二歩 音声処理\n#### 1. 音声切分\n学習フォルダー内のすべての音声ファイルを自動的に探し出し、切分と正規化を行い、2つのwavフォルダーを実験ディレクトリに生成します。現在は単人モデルの学習のみを支援しています。",
     "### Step 3. Start training.\nFill in the training settings and start training the model and index.": "### 第三歩 学習開始\n学習設定を入力して、モデルと索引の学習を開始します。",
     "### View model information\n> Only supported for small model files extracted from the 'weights' folder.": "### モデル情報を表示\n> `weights`フォルダから抽出された小さなのみ対応",
-    "### 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. 特徴抽出\nCPUで音高を抽出し(モデルに音高がある場合のみ)、GPUで特徴を抽出する(GPU番号を選択すべし)",
     "Actually calculated": "実際計算",
     "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": "入力ソースの音量エンベロープと出力音量エンベロープの融合率 1に近づくほど、出力音量エンベロープの割合が高くなる",
@@ -155,6 +155,5 @@
     "ckpt Processing": "ckptファイルの処理",
     "index path cannot contain unicode characters": "indexファイルのパスに漢字を含んではいけません",
     "pth path cannot contain unicode characters": "pthファイルのパスに漢字を含んではいけません",
-    "step2:Pitch extraction & feature extraction": "step2:ピッチ抽出と特徴抽出",
-    "模型作者": "模型作者"
+    "step2:Pitch extraction & feature extraction": "step2:ピッチ抽出と特徴抽出"
 }
diff --git a/i18n/locale/ko_KR.json b/i18n/locale/ko_KR.json
index b5032c2..cc975be 100644
--- a/i18n/locale/ko_KR.json
+++ b/i18n/locale/ko_KR.json
@@ -1,4 +1,5 @@
 {
+    "### 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.",
     "### 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",
@@ -6,7 +7,6 @@
     "### 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",
@@ -155,6 +155,5 @@
     "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",
-    "模型作者": "模型作者"
+    "step2:Pitch extraction & feature extraction": "step2: 음높이 추출 & 특징 추출 중"
 }
diff --git a/i18n/locale/pt_BR.json b/i18n/locale/pt_BR.json
index 01c61a1..d8a3f7c 100644
--- a/i18n/locale/pt_BR.json
+++ b/i18n/locale/pt_BR.json
@@ -1,4 +1,5 @@
 {
+    "### 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.": "### Extração do modelo\n> Insira o caminho do modelo de arquivo grande na pasta 'logs'.\n\nIsso é útil se você quiser interromper o treinamento no meio do caminho e extrair e salvar manualmente um arquivo de modelo pequeno, ou se quiser testar um modelo intermediário.",
     "### 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.": "### Modificar informações do modelo\n> Suportado apenas para arquivos de modelo pequenos extraídos da pasta 'weights'.",
@@ -6,7 +7,6 @@
     "### 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.": "### Exibir informações do modelo\n> Suportado apenas para arquivos de modelo pequenos extraídos da pasta 'weights'.",
-    "### 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": "O envelope de volume da fonte de entrada substitui a taxa de fusão do envelope de volume de saída, quanto mais próximo de 1, mais o envelope de saída é usado:",
@@ -155,6 +155,5 @@
     "ckpt Processing": "processamento ckpt",
     "index path cannot contain unicode characters": "O caminho do arquivo de Index não pode conter caracteres chineses",
     "pth path cannot contain unicode characters": "o caminho do arquivo pth não pode conter caracteres chineses",
-    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征",
-    "模型作者": "模型作者"
+    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征"
 }
diff --git a/i18n/locale/ru_RU.json b/i18n/locale/ru_RU.json
index 58edfc8..a141796 100644
--- a/i18n/locale/ru_RU.json
+++ b/i18n/locale/ru_RU.json
@@ -1,4 +1,5 @@
 {
+    "### 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.": "### Создание модели из данных\n> Полученных в процессе обучения (введите путь к большому файлу модели в папке 'logs').\n\nМожет пригодиться, если вам нужно завершить обучение и получить маленький файл готовой модели, или если вам нужно проверить недообученную модель.",
     "### 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.": "### Изменить информацию о модели\n> Работает только с маленькими моделями, взятыми из папки 'weights'.",
@@ -6,7 +7,6 @@
     "### 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.": "### Просмотреть информацию о модели\n> Работает только с маленькими моделями, взятыми из папки 'weights'.",
-    "### 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": "Использовать громкость входного файла для замены или перемешивания с громкостью выходного файла. Чем ближе соотношение к 1, тем больше используется звука из выходного файла:",
@@ -155,6 +155,5 @@
     "ckpt Processing": "Обработка ckpt",
     "index path cannot contain unicode characters": "Путь к файлу индекса",
     "pth path cannot contain unicode characters": "Путь к файлу pth",
-    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征",
-    "模型作者": "模型作者"
+    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征"
 }
diff --git a/i18n/locale/tr_TR.json b/i18n/locale/tr_TR.json
index ad894dc..4a8ad0b 100644
--- a/i18n/locale/tr_TR.json
+++ b/i18n/locale/tr_TR.json
@@ -1,4 +1,5 @@
 {
+    "### 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.": "### Model çıkartma\n> Büyük dosya modeli yolunu 'logs' klasöründe girin.\n\nBu, eğitimi yarıda bırakmak istediğinizde ve manuel olarak küçük bir model dosyası çıkartmak ve kaydetmek istediğinizde veya bir ara modeli test etmek istediğinizde kullanışlıdır.",
     "### 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.": "### Model bilgilerini düzenle\n> Sadece 'weights' klasöründen çıkarılan küçük model dosyaları desteklenir.",
@@ -6,7 +7,6 @@
     "### 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.": "### Model bilgilerini görüntüle\n> Sadece 'weights' klasöründen çıkarılan küçük model dosyaları desteklenir.",
-    "### 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": "Sesin hacim zarfını ayarlayın. 0'a yakın değerler, sesin orijinal vokallerin hacmine benzer olmasını sağlar. Düşük bir değerle ses gürültüsünü maskeleyebilir ve hacmi daha doğal bir şekilde duyulabilir hale getirebilirsiniz. 1'e yaklaştıkça sürekli bir yüksek ses seviyesi elde edilir:",
@@ -155,6 +155,5 @@
     "ckpt Processing": "ckpt İşleme",
     "index path cannot contain unicode characters": ".index dosya yolu Çince karakter içeremez",
     "pth path cannot contain unicode characters": ".pth dosya yolu Çince karakter içeremez",
-    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征",
-    "模型作者": "模型作者"
+    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征"
 }
diff --git a/i18n/locale/zh_CN.json b/i18n/locale/zh_CN.json
index 0f2c6b4..4369214 100644
--- a/i18n/locale/zh_CN.json
+++ b/i18n/locale/zh_CN.json
@@ -1,4 +1,5 @@
 {
+    "### 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.": "### 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.": "### 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",
@@ -6,7 +7,6 @@
     "### 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",
-    "### 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": "输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络",
@@ -155,6 +155,5 @@
     "ckpt Processing": "ckpt处理",
     "index path cannot contain unicode characters": "index文件路径不可包含中文",
     "pth path cannot contain unicode characters": "pth文件路径不可包含中文",
-    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征",
-    "模型作者": "模型作者"
+    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征"
 }
diff --git a/i18n/locale/zh_HK.json b/i18n/locale/zh_HK.json
index 637a64b..92ee1af 100644
--- a/i18n/locale/zh_HK.json
+++ b/i18n/locale/zh_HK.json
@@ -1,4 +1,5 @@
 {
+    "### 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.": "### 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.",
     "### 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",
@@ -6,7 +7,6 @@
     "### 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",
-    "### 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": "輸入源音量包絡替換輸出音量包絡融合比例，越靠近1越使用輸出包絡",
@@ -155,6 +155,5 @@
     "ckpt Processing": "ckpt處理",
     "index path cannot contain unicode characters": "index文件路径不可包含中文",
     "pth path cannot contain unicode characters": "pth文件路径不可包含中文",
-    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征",
-    "模型作者": "模型作者"
+    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征"
 }
diff --git a/i18n/locale/zh_SG.json b/i18n/locale/zh_SG.json
index 637a64b..92ee1af 100644
--- a/i18n/locale/zh_SG.json
+++ b/i18n/locale/zh_SG.json
@@ -1,4 +1,5 @@
 {
+    "### 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.": "### 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.",
     "### 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",
@@ -6,7 +7,6 @@
     "### 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",
-    "### 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": "輸入源音量包絡替換輸出音量包絡融合比例，越靠近1越使用輸出包絡",
@@ -155,6 +155,5 @@
     "ckpt Processing": "ckpt處理",
     "index path cannot contain unicode characters": "index文件路径不可包含中文",
     "pth path cannot contain unicode characters": "pth文件路径不可包含中文",
-    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征",
-    "模型作者": "模型作者"
+    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征"
 }
diff --git a/i18n/locale/zh_TW.json b/i18n/locale/zh_TW.json
index 637a64b..92ee1af 100644
--- a/i18n/locale/zh_TW.json
+++ b/i18n/locale/zh_TW.json
@@ -1,4 +1,5 @@
 {
+    "### 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.": "### 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.",
     "### 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",
@@ -6,7 +7,6 @@
     "### 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",
-    "### 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": "輸入源音量包絡替換輸出音量包絡融合比例，越靠近1越使用輸出包絡",
@@ -155,6 +155,5 @@
     "ckpt Processing": "ckpt處理",
     "index path cannot contain unicode characters": "index文件路径不可包含中文",
     "pth path cannot contain unicode characters": "pth文件路径不可包含中文",
-    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征",
-    "模型作者": "模型作者"
+    "step2:Pitch extraction & feature extraction": "step2:正在提取音高&正在提取特征"
 }

From 18fb9196a2bc7098cb6c529f3d9bcaa38dafd82f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=BA=90=E6=96=87=E9=9B=A8?=
 <41315874+fumiama@users.noreply.github.com>
Date: Tue, 11 Jun 2024 16:18:46 +0900
Subject: [PATCH 5/7] fix(i18n): missing translations in zh and ja

---
 i18n/locale/ja_JP.json | 14 +++++++-------
 i18n/locale/zh_CN.json |  2 +-
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/i18n/locale/ja_JP.json b/i18n/locale/ja_JP.json
index fc2f814..8b21cf0 100644
--- a/i18n/locale/ja_JP.json
+++ b/i18n/locale/ja_JP.json
@@ -1,9 +1,9 @@
 {
-    "### 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 comparison\n> You can get model ID (long) from `View model information` below.\n\nCalculate a similarity between two models.": "### モデル比べ\n> モデルID(長)は下の`モデル情報を表示`に得ることが出来ます。\n\n両モデルの推論相似度を比べることが出来ます。",
     "### 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.": "### モデル抽出\n> ログフォルダー内の大モデルのパスを入力\n\nモデルを半分まで学習し、小モデルを保存しなかった場合、又は中間モデルをテストしたい場合に適用されます。",
-    "### 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.": "### モデルマージ\n音源のマージテストに使用できます",
     "### Modify model information\n> Only supported for small model files extracted from the 'weights' folder.": "### モデル情報の修正\n> `weights`フォルダから抽出された小モデルのみ対応",
-    "### 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.": "### 第一歩 実験設定入力\n実験データはlogsフォルダーに、実験名別のフォルダで保存されたため、その実験名をご自分で決定する必要があります。実験設定、ログ、学習されたモデルファイルなどがそのフォルダに含まれています。",
     "### 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.": "### 第二歩 音声処理\n#### 1. 音声切分\n学習フォルダー内のすべての音声ファイルを自動的に探し出し、切分と正規化を行い、2つのwavフォルダーを実験ディレクトリに生成します。現在は単人モデルの学習のみを支援しています。",
     "### Step 3. Start training.\nFill in the training settings and start training the model and index.": "### 第三歩 学習開始\n学習設定を入力して、モデルと索引の学習を開始します。",
     "### View model information\n> Only supported for small model files extracted from the 'weights' folder.": "### モデル情報を表示\n> `weights`フォルダから抽出された小さなのみ対応",
@@ -19,7 +19,7 @@
     "Batch processing for vocal accompaniment separation using the UVR5 model.<br>Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).<br>The model is divided into three categories:<br>1. Preserve vocals: Choose this option for audio without harmonies. It preserves vocals better than HP5. It includes two built-in models: HP2 and HP3. HP3 may slightly leak accompaniment but preserves vocals slightly better than HP2.<br>2. Preserve main vocals only: Choose this option for audio with harmonies. It may weaken the main vocals. It includes one built-in model: HP5.<br>3. De-reverb and de-delay models (by FoxJoy):<br>  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;<br>&emsp;(234) DeEcho: Removes delay effects. Aggressive mode removes more thoroughly than Normal mode. DeReverb additionally removes reverb and can remove mono reverb, but not very effectively for heavily reverberated high-frequency content.<br>De-reverb/de-delay notes:<br>1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.<br>2. The MDX-Net-Dereverb model is quite slow.<br>3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive.": "UVR5モデルを使用したボーカル伴奏の分離バッチ処理。<br>有効なフォルダーパスフォーマットの例: D:\\path\\to\\input\\folder (エクスプローラーのアドレスバーからコピーします)。<br>モデルは三つのカテゴリに分かれています:<br>1. ボーカルを保持: ハーモニーのないオーディオに対してこれを選択します。HP5よりもボーカルをより良く保持します。HP2とHP3の二つの内蔵モデルが含まれています。HP3は伴奏をわずかに漏らす可能性がありますが、HP2よりもわずかにボーカルをより良く保持します。<br>2. 主なボーカルのみを保持: ハーモニーのあるオーディオに対してこれを選択します。主なボーカルを弱める可能性があります。HP5の一つの内蔵モデルが含まれています。<br>3. ディリバーブとディレイモデル (by FoxJoy):<br>  (1) MDX-Net: ステレオリバーブの除去に最適な選択肢ですが、モノリバーブは除去できません；<br>&emsp;(234) DeEcho: ディレイ効果を除去します。AggressiveモードはNormalモードよりも徹底的に除去します。DeReverbはさらにリバーブを除去し、モノリバーブを除去することができますが、高周波のリバーブが強い内容に対しては非常に効果的ではありません。<br>ディリバーブ/ディレイに関する注意点:<br>1. DeEcho-DeReverbモデルの処理時間は、他の二つのDeEchoモデルの約二倍です。<br>2. MDX-Net-Dereverbモデルは非常に遅いです。<br>3. 推奨される最もクリーンな設定は、最初にMDX-Netを適用し、その後にDeEcho-Aggressiveを適用することです。",
     "Batch size per GPU": "GPUごとのバッチサイズ",
     "Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training, but caching large datasets will consume a lot of GPU memory and may not provide much speed improvement": "すべての学習データをメモリにキャッシュするかどうか。10分以下の小さなデータはキャッシュして学習を高速化できますが、大きなデータをキャッシュするとメモリが破裂し、あまり速度が上がりません。",
-    "Calculate": "计算",
+    "Calculate": "計算",
     "Choose sample rate of the device": "デバイスサンプリング率を使用",
     "Choose sample rate of the model": "モデルサンプリング率を使用",
     "Convert": "変換",
@@ -50,7 +50,7 @@
     "Hidden": "無表示",
     "ID of model A (long)": "AモデルID(長)",
     "ID of model B (long)": "BモデルID(長)",
-    "ID(long)": "ID(long)",
+    "ID(long)": "ID(長)",
     "ID(short)": "ID(短)",
     "If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness.": ">=3 次に、harvestピッチの認識結果に対してメディアンフィルタを使用します。値はフィルター半径で、ミュートを減衰させるために使用します。",
     "Inference time (ms)": "推論時間(ms)",
@@ -59,7 +59,7 @@
     "Input device": "入力デバイス",
     "Input noise reduction": "入力騒音低減",
     "Input voice monitor": "入力返聴",
-    "Link index to outside folder": "链接索引到外部",
+    "Link index to outside folder": "索引を外部フォルダへリンク",
     "Load model": "モデルをロード",
     "Load pre-trained base model D path": "事前学習済みのDモデルのパス",
     "Load pre-trained base model G path": "事前学習済みのGモデルのパス",
@@ -121,7 +121,7 @@
     "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": "ピッチ抽出アルゴリズムの選択、歌声はpmで高速化でき、harvestは低音が良いが信じられないほど遅く、crepeは良く動くがGPUを喰います",
     "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": "ピッチ抽出アルゴリズムの選択：歌声はpmで高速化でき、入力した音声が高音質でCPUが貧弱な場合はdioで高速化でき、harvestの方が良いが遅く、rmvpeがベストだがCPU/GPUを若干食います。",
     "Similarity": "相似度",
-    "Similarity (from 0 to 1)": "相似度(0到1)",
+    "Similarity (from 0 to 1)": "相似度(0～1)",
     "Single inference": "一度推論",
     "Specify output folder": "出力フォルダを指定してください",
     "Specify the output folder for accompaniment": "マスター以外の出力音声フォルダーを指定する",
diff --git a/i18n/locale/zh_CN.json b/i18n/locale/zh_CN.json
index 4369214..0bfc69f 100644
--- a/i18n/locale/zh_CN.json
+++ b/i18n/locale/zh_CN.json
@@ -1,5 +1,5 @@
 {
-    "### 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 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",
     "### 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.": "### 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",

From 534128992f755a504e6d2a3211ce98ad172bb0cc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=BA=90=E6=96=87=E9=9B=A8?=
 <41315874+fumiama@users.noreply.github.com>
Date: Tue, 11 Jun 2024 16:30:23 +0900
Subject: [PATCH 6/7] optimize(web): remove unnecessary gt.Groups

---
 web.py | 1090 ++++++++++++++++++++++++++++----------------------------
 1 file changed, 542 insertions(+), 548 deletions(-)

diff --git a/web.py b/web.py
index 2265e03..bf0fc6a 100644
--- a/web.py
+++ b/web.py
@@ -831,134 +831,133 @@ with gr.Blocks(title="RVC WebUI") as app:
                 )
             modelinfo = gr.Textbox(label=i18n("Model info"), max_lines=8)
             with gr.TabItem(i18n("Single inference")):
-                with gr.Group():
-                    with gr.Row():
-                        with gr.Column():
-                            vc_transform0 = gr.Number(
-                                label=i18n(
-                                    "Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)"
-                                ),
-                                value=0,
-                            )
-                            input_audio0 = gr.Audio(
-                                label=i18n("The audio file to be processed"),
-                                type="filepath",
-                            )
-                            file_index2 = gr.Dropdown(
-                                label=i18n(
-                                    "Auto-detect index path and select from the dropdown"
-                                ),
-                                choices=sorted(index_paths),
-                                interactive=True,
-                            )
-                            file_index1 = gr.File(
-                                label=i18n(
-                                    "Path to the feature index file. Leave blank to use the selected result from the dropdown"
-                                ),
-                            )
-                        with gr.Column():
-                            f0method0 = gr.Radio(
-                                label=i18n(
-                                    "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"
-                                ),
-                                choices=(
-                                    ["pm", "harvest", "crepe", "rmvpe"]
-                                    if config.dml == False
-                                    else ["pm", "harvest", "rmvpe"]
-                                ),
-                                value="rmvpe",
-                                interactive=True,
-                            )
-                            resample_sr0 = gr.Slider(
-                                minimum=0,
-                                maximum=48000,
-                                label=i18n(
-                                    "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling"
-                                ),
-                                value=0,
-                                step=1,
-                                interactive=True,
-                            )
-                            rms_mix_rate0 = gr.Slider(
-                                minimum=0,
-                                maximum=1,
-                                label=i18n(
-                                    "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"
-                                ),
-                                value=0.25,
-                                interactive=True,
-                            )
-                            protect0 = gr.Slider(
-                                minimum=0,
-                                maximum=0.5,
-                                label=i18n(
-                                    "Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy"
-                                ),
-                                value=0.33,
-                                step=0.01,
-                                interactive=True,
-                            )
-                            filter_radius0 = gr.Slider(
-                                minimum=0,
-                                maximum=7,
-                                label=i18n(
-                                    "If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness."
-                                ),
-                                value=3,
-                                step=1,
-                                interactive=True,
-                            )
-                            index_rate1 = gr.Slider(
-                                minimum=0,
-                                maximum=1,
-                                label=i18n(
-                                    "Search feature ratio (controls accent strength, too high has artifacting)"
-                                ),
-                                value=0.75,
-                                interactive=True,
-                            )
-                            f0_file = gr.File(
-                                label=i18n(
-                                    "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation"
-                                ),
-                                visible=False,
-                            )
-                            but0 = gr.Button(i18n("Convert"), variant="primary")
-                            vc_output2 = gr.Audio(
-                                label=i18n(
-                                    "Export audio (click on the three dots in the lower right corner to download)"
-                                )
+                with gr.Row():
+                    with gr.Column():
+                        vc_transform0 = gr.Number(
+                            label=i18n(
+                                "Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)"
+                            ),
+                            value=0,
+                        )
+                        input_audio0 = gr.Audio(
+                            label=i18n("The audio file to be processed"),
+                            type="filepath",
+                        )
+                        file_index2 = gr.Dropdown(
+                            label=i18n(
+                                "Auto-detect index path and select from the dropdown"
+                            ),
+                            choices=sorted(index_paths),
+                            interactive=True,
+                        )
+                        file_index1 = gr.File(
+                            label=i18n(
+                                "Path to the feature index file. Leave blank to use the selected result from the dropdown"
+                            ),
+                        )
+                    with gr.Column():
+                        f0method0 = gr.Radio(
+                            label=i18n(
+                                "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"
+                            ),
+                            choices=(
+                                ["pm", "harvest", "crepe", "rmvpe"]
+                                if config.dml == False
+                                else ["pm", "harvest", "rmvpe"]
+                            ),
+                            value="rmvpe",
+                            interactive=True,
+                        )
+                        resample_sr0 = gr.Slider(
+                            minimum=0,
+                            maximum=48000,
+                            label=i18n(
+                                "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling"
+                            ),
+                            value=0,
+                            step=1,
+                            interactive=True,
+                        )
+                        rms_mix_rate0 = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            label=i18n(
+                                "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"
+                            ),
+                            value=0.25,
+                            interactive=True,
+                        )
+                        protect0 = gr.Slider(
+                            minimum=0,
+                            maximum=0.5,
+                            label=i18n(
+                                "Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy"
+                            ),
+                            value=0.33,
+                            step=0.01,
+                            interactive=True,
+                        )
+                        filter_radius0 = gr.Slider(
+                            minimum=0,
+                            maximum=7,
+                            label=i18n(
+                                "If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness."
+                            ),
+                            value=3,
+                            step=1,
+                            interactive=True,
+                        )
+                        index_rate1 = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            label=i18n(
+                                "Search feature ratio (controls accent strength, too high has artifacting)"
+                            ),
+                            value=0.75,
+                            interactive=True,
+                        )
+                        f0_file = gr.File(
+                            label=i18n(
+                                "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation"
+                            ),
+                            visible=False,
+                        )
+                        but0 = gr.Button(i18n("Convert"), variant="primary")
+                        vc_output2 = gr.Audio(
+                            label=i18n(
+                                "Export audio (click on the three dots in the lower right corner to download)"
                             )
+                        )
 
-                            refresh_button.click(
-                                fn=change_choices,
-                                inputs=[],
-                                outputs=[sid0, file_index2],
-                                api_name="infer_refresh",
-                            )
-                with gr.Group():
-                    vc_output1 = gr.Textbox(label=i18n("Output information"))
+                        refresh_button.click(
+                            fn=change_choices,
+                            inputs=[],
+                            outputs=[sid0, file_index2],
+                            api_name="infer_refresh",
+                        )
 
-                    but0.click(
-                        vc.vc_single,
-                        [
-                            spk_item,
-                            input_audio0,
-                            vc_transform0,
-                            f0_file,
-                            f0method0,
-                            file_index1,
-                            file_index2,
-                            # file_big_npy1,
-                            index_rate1,
-                            filter_radius0,
-                            resample_sr0,
-                            rms_mix_rate0,
-                            protect0,
-                        ],
-                        [vc_output1, vc_output2],
-                        api_name="infer_convert",
-                    )
+                vc_output1 = gr.Textbox(label=i18n("Output information"))
+
+                but0.click(
+                    vc.vc_single,
+                    [
+                        spk_item,
+                        input_audio0,
+                        vc_transform0,
+                        f0_file,
+                        f0method0,
+                        file_index1,
+                        file_index2,
+                        # file_big_npy1,
+                        index_rate1,
+                        filter_radius0,
+                        resample_sr0,
+                        rms_mix_rate0,
+                        protect0,
+                    ],
+                    [vc_output1, vc_output2],
+                    api_name="infer_convert",
+                )
             with gr.TabItem(i18n("Batch inference")):
                 gr.Markdown(
                     value=i18n(
@@ -1121,13 +1120,12 @@ with gr.Blocks(title="RVC WebUI") as app:
         with gr.TabItem(
             i18n("Vocals/Accompaniment Separation & Reverberation Removal")
         ):
-            with gr.Group():
-                gr.Markdown(
-                    value=i18n(
-                        "Batch processing for vocal accompaniment separation using the UVR5 model.<br>Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).<br>The model is divided into three categories:<br>1. Preserve vocals: Choose this option for audio without harmonies. It preserves vocals better than HP5. It includes two built-in models: HP2 and HP3. HP3 may slightly leak accompaniment but preserves vocals slightly better than HP2.<br>2. Preserve main vocals only: Choose this option for audio with harmonies. It may weaken the main vocals. It includes one built-in model: HP5.<br>3. De-reverb and de-delay models (by FoxJoy):<br>  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;<br>&emsp;(234) DeEcho: Removes delay effects. Aggressive mode removes more thoroughly than Normal mode. DeReverb additionally removes reverb and can remove mono reverb, but not very effectively for heavily reverberated high-frequency content.<br>De-reverb/de-delay notes:<br>1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.<br>2. The MDX-Net-Dereverb model is quite slow.<br>3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive."
-                    )
+            gr.Markdown(
+                value=i18n(
+                    "Batch processing for vocal accompaniment separation using the UVR5 model.<br>Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).<br>The model is divided into three categories:<br>1. Preserve vocals: Choose this option for audio without harmonies. It preserves vocals better than HP5. It includes two built-in models: HP2 and HP3. HP3 may slightly leak accompaniment but preserves vocals slightly better than HP2.<br>2. Preserve main vocals only: Choose this option for audio with harmonies. It may weaken the main vocals. It includes one built-in model: HP5.<br>3. De-reverb and de-delay models (by FoxJoy):<br>  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;<br>&emsp;(234) DeEcho: Removes delay effects. Aggressive mode removes more thoroughly than Normal mode. DeReverb additionally removes reverb and can remove mono reverb, but not very effectively for heavily reverberated high-frequency content.<br>De-reverb/de-delay notes:<br>1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.<br>2. The MDX-Net-Dereverb model is quite slow.<br>3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive."
                 )
-                with gr.Row():
+            )
+            with gr.Row():
                     with gr.Column():
                         dir_wav_input = gr.Textbox(
                             label=i18n(
@@ -1227,450 +1225,446 @@ with gr.Blocks(title="RVC WebUI") as app:
                     interactive=True,
                     visible=True,
                 )
-            with gr.Group():  # 暂时单人的, 后面支持最多4人的#数据处理
-                gr.Markdown(
-                    value=i18n(
-                        "### 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."
-                    )
+            gr.Markdown(
+                value=i18n(
+                    "### 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."
                 )
-                with gr.Row():
-                    with gr.Column():
-                        trainset_dir4 = gr.Textbox(
-                            label=i18n("Enter the path of the training folder"),
-                        )
-                        spk_id5 = gr.Slider(
-                            minimum=0,
-                            maximum=4,
-                            step=1,
-                            label=i18n("Please specify the speaker/singer ID"),
-                            value=0,
-                            interactive=True,
-                        )
-                        but1 = gr.Button(i18n("Process data"), variant="primary")
-                    with gr.Column():
-                        info1 = gr.Textbox(label=i18n("Output information"), value="")
-                        but1.click(
-                            preprocess_dataset,
-                            [trainset_dir4, exp_dir1, sr2, np7],
-                            [info1],
-                            api_name="train_preprocess",
-                        )
-            with gr.Group():
-                gr.Markdown(
-                    value=i18n(
-                        "#### 2. Feature extraction.\nUse CPU to extract pitch (if the model has pitch), use GPU to extract features (select GPU index)."
+            )
+            with gr.Row():
+                with gr.Column():
+                    trainset_dir4 = gr.Textbox(
+                        label=i18n("Enter the path of the training folder"),
                     )
+                    spk_id5 = gr.Slider(
+                        minimum=0,
+                        maximum=4,
+                        step=1,
+                        label=i18n("Please specify the speaker/singer ID"),
+                        value=0,
+                        interactive=True,
+                    )
+                    but1 = gr.Button(i18n("Process data"), variant="primary")
+                with gr.Column():
+                    info1 = gr.Textbox(label=i18n("Output information"), value="")
+                    but1.click(
+                        preprocess_dataset,
+                        [trainset_dir4, exp_dir1, sr2, np7],
+                        [info1],
+                        api_name="train_preprocess",
+                    )
+            gr.Markdown(
+                value=i18n(
+                    "#### 2. Feature extraction.\nUse CPU to extract pitch (if the model has pitch), use GPU to extract features (select GPU index)."
                 )
-                with gr.Row():
-                    with gr.Column():
-                        gpu_info9 = gr.Textbox(
-                            label=i18n("GPU Information"),
-                            value=gpu_info,
-                            visible=F0GPUVisible,
-                        )
-                        gpus6 = gr.Textbox(
-                            label=i18n(
-                                "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2"
-                            ),
-                            value=gpus,
-                            interactive=True,
-                            visible=F0GPUVisible,
-                        )
-                        gpus_rmvpe = gr.Textbox(
-                            label=i18n(
-                                "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1"
-                            ),
-                            value="%s-%s" % (gpus, gpus),
-                            interactive=True,
-                            visible=F0GPUVisible,
-                        )
-                        f0method8 = gr.Radio(
-                            label=i18n(
-                                "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"
-                            ),
-                            choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"],
-                            value="rmvpe_gpu",
-                            interactive=True,
-                        )
-                    with gr.Column():
-                        but2 = gr.Button(i18n("Feature extraction"), variant="primary")
-                        info2 = gr.Textbox(label=i18n("Output information"), value="")
-                    f0method8.change(
-                        fn=change_f0_method,
-                        inputs=[f0method8],
-                        outputs=[gpus_rmvpe],
+            )
+            with gr.Row():
+                with gr.Column():
+                    gpu_info9 = gr.Textbox(
+                        label=i18n("GPU Information"),
+                        value=gpu_info,
+                        visible=F0GPUVisible,
                     )
-                    but2.click(
-                        extract_f0_feature,
-                        [
-                            gpus6,
-                            np7,
-                            f0method8,
-                            if_f0_3,
-                            exp_dir1,
-                            version19,
-                            gpus_rmvpe,
-                        ],
-                        [info2],
-                        api_name="train_extract_f0_feature",
+                    gpus6 = gr.Textbox(
+                        label=i18n(
+                            "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2"
+                        ),
+                        value=gpus,
+                        interactive=True,
+                        visible=F0GPUVisible,
                     )
-            with gr.Group():
-                gr.Markdown(
-                    value=i18n(
-                        "### Step 3. Start training.\nFill in the training settings and start training the model and index."
+                    gpus_rmvpe = gr.Textbox(
+                        label=i18n(
+                            "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1"
+                        ),
+                        value="%s-%s" % (gpus, gpus),
+                        interactive=True,
+                        visible=F0GPUVisible,
                     )
+                    f0method8 = gr.Radio(
+                        label=i18n(
+                            "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"
+                        ),
+                        choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"],
+                        value="rmvpe_gpu",
+                        interactive=True,
+                    )
+                with gr.Column():
+                    but2 = gr.Button(i18n("Feature extraction"), variant="primary")
+                    info2 = gr.Textbox(label=i18n("Output information"), value="")
+                f0method8.change(
+                    fn=change_f0_method,
+                    inputs=[f0method8],
+                    outputs=[gpus_rmvpe],
                 )
-                with gr.Row():
-                    with gr.Column():
-                        save_epoch10 = gr.Slider(
-                            minimum=1,
-                            maximum=50,
-                            step=1,
-                            label=i18n("Save frequency (save_every_epoch)"),
-                            value=5,
-                            interactive=True,
-                        )
-                        total_epoch11 = gr.Slider(
-                            minimum=2,
-                            maximum=1000,
-                            step=1,
-                            label=i18n("Total training epochs (total_epoch)"),
-                            value=20,
-                            interactive=True,
-                        )
-                        batch_size12 = gr.Slider(
-                            minimum=1,
-                            maximum=40,
-                            step=1,
-                            label=i18n("Batch size per GPU"),
-                            value=default_batch_size,
-                            interactive=True,
-                        )
-                        if_save_latest13 = gr.Radio(
-                            label=i18n(
-                                "Save only the latest '.ckpt' file to save disk space"
-                            ),
-                            choices=[i18n("Yes"), i18n("No")],
-                            value=i18n("No"),
-                            interactive=True,
-                        )
-                        if_cache_gpu17 = gr.Radio(
-                            label=i18n(
-                                "Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training, but caching large datasets will consume a lot of GPU memory and may not provide much speed improvement"
-                            ),
-                            choices=[i18n("Yes"), i18n("No")],
-                            value=i18n("No"),
-                            interactive=True,
-                        )
-                        if_save_every_weights18 = gr.Radio(
-                            label=i18n(
-                                "Save a small final model to the 'weights' folder at each save point"
-                            ),
-                            choices=[i18n("Yes"), i18n("No")],
-                            value=i18n("No"),
-                            interactive=True,
-                        )
-                    with gr.Column():
-                        pretrained_G14 = gr.Textbox(
-                            label=i18n("Load pre-trained base model G path"),
-                            value="assets/pretrained_v2/f0G40k.pth",
-                            interactive=True,
-                        )
-                        pretrained_D15 = gr.Textbox(
-                            label=i18n("Load pre-trained base model D path"),
-                            value="assets/pretrained_v2/f0D40k.pth",
-                            interactive=True,
-                        )
-                        gpus16 = gr.Textbox(
-                            label=i18n(
-                                "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2"
-                            ),
-                            value=gpus,
-                            interactive=True,
-                        )
-                        sr2.change(
-                            change_sr2,
-                            [sr2, if_f0_3, version19],
-                            [pretrained_G14, pretrained_D15],
-                        )
-                        version19.change(
-                            change_version19,
-                            [sr2, if_f0_3, version19],
-                            [pretrained_G14, pretrained_D15, sr2],
-                        )
-                        if_f0_3.change(
-                            change_f0,
-                            [if_f0_3, sr2, version19],
-                            [f0method8, gpus_rmvpe, pretrained_G14, pretrained_D15],
-                        )
+                but2.click(
+                    extract_f0_feature,
+                    [
+                        gpus6,
+                        np7,
+                        f0method8,
+                        if_f0_3,
+                        exp_dir1,
+                        version19,
+                        gpus_rmvpe,
+                    ],
+                    [info2],
+                    api_name="train_extract_f0_feature",
+                )
+            gr.Markdown(
+                value=i18n(
+                    "### Step 3. Start training.\nFill in the training settings and start training the model and index."
+                )
+            )
+            with gr.Row():
+                with gr.Column():
+                    save_epoch10 = gr.Slider(
+                        minimum=1,
+                        maximum=50,
+                        step=1,
+                        label=i18n("Save frequency (save_every_epoch)"),
+                        value=5,
+                        interactive=True,
+                    )
+                    total_epoch11 = gr.Slider(
+                        minimum=2,
+                        maximum=1000,
+                        step=1,
+                        label=i18n("Total training epochs (total_epoch)"),
+                        value=20,
+                        interactive=True,
+                    )
+                    batch_size12 = gr.Slider(
+                        minimum=1,
+                        maximum=40,
+                        step=1,
+                        label=i18n("Batch size per GPU"),
+                        value=default_batch_size,
+                        interactive=True,
+                    )
+                    if_save_latest13 = gr.Radio(
+                        label=i18n(
+                            "Save only the latest '.ckpt' file to save disk space"
+                        ),
+                        choices=[i18n("Yes"), i18n("No")],
+                        value=i18n("No"),
+                        interactive=True,
+                    )
+                    if_cache_gpu17 = gr.Radio(
+                        label=i18n(
+                            "Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training, but caching large datasets will consume a lot of GPU memory and may not provide much speed improvement"
+                        ),
+                        choices=[i18n("Yes"), i18n("No")],
+                        value=i18n("No"),
+                        interactive=True,
+                    )
+                    if_save_every_weights18 = gr.Radio(
+                        label=i18n(
+                            "Save a small final model to the 'weights' folder at each save point"
+                        ),
+                        choices=[i18n("Yes"), i18n("No")],
+                        value=i18n("No"),
+                        interactive=True,
+                    )
+                with gr.Column():
+                    pretrained_G14 = gr.Textbox(
+                        label=i18n("Load pre-trained base model G path"),
+                        value="assets/pretrained_v2/f0G40k.pth",
+                        interactive=True,
+                    )
+                    pretrained_D15 = gr.Textbox(
+                        label=i18n("Load pre-trained base model D path"),
+                        value="assets/pretrained_v2/f0D40k.pth",
+                        interactive=True,
+                    )
+                    gpus16 = gr.Textbox(
+                        label=i18n(
+                            "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2"
+                        ),
+                        value=gpus,
+                        interactive=True,
+                    )
+                    sr2.change(
+                        change_sr2,
+                        [sr2, if_f0_3, version19],
+                        [pretrained_G14, pretrained_D15],
+                    )
+                    version19.change(
+                        change_version19,
+                        [sr2, if_f0_3, version19],
+                        [pretrained_G14, pretrained_D15, sr2],
+                    )
+                    if_f0_3.change(
+                        change_f0,
+                        [if_f0_3, sr2, version19],
+                        [f0method8, gpus_rmvpe, pretrained_G14, pretrained_D15],
+                    )
 
-                        but3 = gr.Button(i18n("Train model"), variant="primary")
-                        but4 = gr.Button(i18n("Train feature index"), variant="primary")
-                        but5 = gr.Button(i18n("One-click training"), variant="primary")
-                with gr.Row():
-                    info3 = gr.Textbox(label=i18n("Output information"), value="")
-                    but3.click(
-                        click_train,
-                        [
-                            exp_dir1,
-                            sr2,
-                            if_f0_3,
-                            spk_id5,
-                            save_epoch10,
-                            total_epoch11,
-                            batch_size12,
-                            if_save_latest13,
-                            pretrained_G14,
-                            pretrained_D15,
-                            gpus16,
-                            if_cache_gpu17,
-                            if_save_every_weights18,
-                            version19,
-                            author,
-                        ],
-                        info3,
-                        api_name="train_start",
-                    )
-                    but4.click(train_index, [exp_dir1, version19], info3)
-                    but5.click(
-                        train1key,
-                        [
-                            exp_dir1,
-                            sr2,
-                            if_f0_3,
-                            trainset_dir4,
-                            spk_id5,
-                            np7,
-                            f0method8,
-                            save_epoch10,
-                            total_epoch11,
-                            batch_size12,
-                            if_save_latest13,
-                            pretrained_G14,
-                            pretrained_D15,
-                            gpus16,
-                            if_cache_gpu17,
-                            if_save_every_weights18,
-                            version19,
-                            gpus_rmvpe,
-                            author,
-                        ],
-                        info3,
-                        api_name="train_start_all",
-                    )
+                    but3 = gr.Button(i18n("Train model"), variant="primary")
+                    but4 = gr.Button(i18n("Train feature index"), variant="primary")
+                    but5 = gr.Button(i18n("One-click training"), variant="primary")
+            with gr.Row():
+                info3 = gr.Textbox(label=i18n("Output information"), value="")
+                but3.click(
+                    click_train,
+                    [
+                        exp_dir1,
+                        sr2,
+                        if_f0_3,
+                        spk_id5,
+                        save_epoch10,
+                        total_epoch11,
+                        batch_size12,
+                        if_save_latest13,
+                        pretrained_G14,
+                        pretrained_D15,
+                        gpus16,
+                        if_cache_gpu17,
+                        if_save_every_weights18,
+                        version19,
+                        author,
+                    ],
+                    info3,
+                    api_name="train_start",
+                )
+                but4.click(train_index, [exp_dir1, version19], info3)
+                but5.click(
+                    train1key,
+                    [
+                        exp_dir1,
+                        sr2,
+                        if_f0_3,
+                        trainset_dir4,
+                        spk_id5,
+                        np7,
+                        f0method8,
+                        save_epoch10,
+                        total_epoch11,
+                        batch_size12,
+                        if_save_latest13,
+                        pretrained_G14,
+                        pretrained_D15,
+                        gpus16,
+                        if_cache_gpu17,
+                        if_save_every_weights18,
+                        version19,
+                        gpus_rmvpe,
+                        author,
+                    ],
+                    info3,
+                    api_name="train_start_all",
+                )
 
         with gr.TabItem(i18n("ckpt Processing")):
-            with gr.Group():
-                gr.Markdown(
-                    value=i18n(
-                        "### Model comparison\n> You can get model ID (long) from `View model information` below.\n\nCalculate a similarity between two models."
+            gr.Markdown(
+                value=i18n(
+                    "### Model comparison\n> You can get model ID (long) from `View model information` below.\n\nCalculate a similarity between two models."
+                )
+            )
+            with gr.Row():
+                with gr.Column():
+                    id_a = gr.Textbox(label=i18n("ID of model A (long)"), value="")
+                    id_b = gr.Textbox(label=i18n("ID of model B (long)"), value="")
+                with gr.Column():
+                    butmodelcmp = gr.Button(i18n("Calculate"), variant="primary")
+                    infomodelcmp = gr.Textbox(
+                        label=i18n("Similarity (from 0 to 1)"),
+                        value="",
+                        max_lines=1,
                     )
+            butmodelcmp.click(
+                hash_similarity,
+                [
+                    id_a,
+                    id_b,
+                ],
+                infomodelcmp,
+                api_name="ckpt_merge",
+            )
+
+            gr.Markdown(
+                value=i18n("### Model fusion\nCan be used to test timbre fusion.")
+            )
+            with gr.Row():
+                with gr.Column():
+                    ckpt_a = gr.Textbox(
+                        label=i18n("Path to Model A"), value="", interactive=True
+                    )
+                    ckpt_b = gr.Textbox(
+                        label=i18n("Path to Model B"), value="", interactive=True
+                    )
+                    alpha_a = gr.Slider(
+                        minimum=0,
+                        maximum=1,
+                        label=i18n("Weight (w) for Model A"),
+                        value=0.5,
+                        interactive=True,
+                    )
+                with gr.Column():
+                    sr_ = gr.Radio(
+                        label=i18n("Target sample rate"),
+                        choices=["40k", "48k"],
+                        value="40k",
+                        interactive=True,
+                    )
+                    if_f0_ = gr.Radio(
+                        label=i18n("Whether the model has pitch guidance"),
+                        choices=[i18n("Yes"), i18n("No")],
+                        value=i18n("Yes"),
+                        interactive=True,
+                    )
+                    info__ = gr.Textbox(
+                        label=i18n("Model information to be placed"),
+                        value="",
+                        max_lines=8,
+                        interactive=True,
+                    )
+                with gr.Column():
+                    name_to_save0 = gr.Textbox(
+                        label=i18n("Saved model name (without extension)"),
+                        value="",
+                        max_lines=1,
+                        interactive=True,
+                    )
+                    version_2 = gr.Radio(
+                        label=i18n("Model architecture version"),
+                        choices=["v1", "v2"],
+                        value="v1",
+                        interactive=True,
+                    )
+                    but6 = gr.Button(i18n("Fusion"), variant="primary")
+            with gr.Row():
+                info4 = gr.Textbox(label=i18n("Output information"), value="")
+            but6.click(
+                merge,
+                [
+                    ckpt_a,
+                    ckpt_b,
+                    alpha_a,
+                    sr_,
+                    if_f0_,
+                    info__,
+                    name_to_save0,
+                    version_2,
+                ],
+                info4,
+                api_name="ckpt_merge",
+            )  # def merge(path1,path2,alpha1,sr,f0,info):
+
+            gr.Markdown(
+                value=i18n(
+                    "### Modify model information\n> Only supported for small model files extracted from the 'weights' folder."
                 )
-                with gr.Row():
-                    with gr.Column():
-                        id_a = gr.Textbox(label=i18n("ID of model A (long)"), value="")
-                        id_b = gr.Textbox(label=i18n("ID of model B (long)"), value="")
-                    with gr.Column():
-                        butmodelcmp = gr.Button(i18n("Calculate"), variant="primary")
-                        infomodelcmp = gr.Textbox(
-                            label=i18n("Similarity (from 0 to 1)"),
-                            value="",
-                            max_lines=1,
-                        )
-                butmodelcmp.click(
-                    hash_similarity,
-                    [
-                        id_a,
-                        id_b,
-                    ],
-                    infomodelcmp,
-                    api_name="ckpt_merge",
+            )
+            with gr.Row():
+                with gr.Column():
+                    ckpt_path0 = gr.Textbox(
+                        label=i18n("Path to Model"), value="", interactive=True
+                    )
+                    info_ = gr.Textbox(
+                        label=i18n("Model information to be modified"),
+                        value="",
+                        max_lines=8,
+                        interactive=True,
+                    )
+                    name_to_save1 = gr.Textbox(
+                        label=i18n(
+                            "Save file name (default: same as the source file)"
+                        ),
+                        value="",
+                        max_lines=1,
+                        interactive=True,
+                    )
+                with gr.Column():
+                    but7 = gr.Button(i18n("Modify"), variant="primary")
+                    info5 = gr.Textbox(label=i18n("Output information"), value="")
+            but7.click(
+                change_info,
+                [ckpt_path0, info_, name_to_save1],
+                info5,
+                api_name="ckpt_modify",
+            )
+
+            gr.Markdown(
+                value=i18n(
+                    "### View model information\n> Only supported for small model files extracted from the 'weights' folder."
                 )
-            with gr.Group():
-                gr.Markdown(
-                    value=i18n("### Model fusion\nCan be used to test timbre fusion.")
+            )
+            with gr.Row():
+                with gr.Column():
+                    ckpt_path1 = gr.File(label=i18n("Path to Model"))
+                    but8 = gr.Button(i18n("View"), variant="primary")
+                with gr.Column():
+                    info6 = gr.Textbox(label=i18n("Output information"), value="")
+            but8.click(show_info, [ckpt_path1], info6, api_name="ckpt_show")
+
+            gr.Markdown(
+                value=i18n(
+                    "### 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."
                 )
-                with gr.Row():
-                    with gr.Column():
-                        ckpt_a = gr.Textbox(
-                            label=i18n("Path to Model A"), value="", interactive=True
-                        )
-                        ckpt_b = gr.Textbox(
-                            label=i18n("Path to Model B"), value="", interactive=True
-                        )
-                        alpha_a = gr.Slider(
-                            minimum=0,
-                            maximum=1,
-                            label=i18n("Weight (w) for Model A"),
-                            value=0.5,
-                            interactive=True,
-                        )
-                    with gr.Column():
-                        sr_ = gr.Radio(
+            )
+            with gr.Row():
+                with gr.Column():
+                    ckpt_path2 = gr.Textbox(
+                        label=i18n("Path to Model"),
+                        value="E:\\codes\\py39\\logs\\mi-test_f0_48k\\G_23333.pth",
+                        interactive=True,
+                    )
+                    save_name = gr.Textbox(
+                        label=i18n("Save name"), value="", interactive=True
+                    )
+                    with gr.Row():
+                        sr__ = gr.Radio(
                             label=i18n("Target sample rate"),
-                            choices=["40k", "48k"],
+                            choices=["32k", "40k", "48k"],
                             value="40k",
                             interactive=True,
                         )
-                        if_f0_ = gr.Radio(
-                            label=i18n("Whether the model has pitch guidance"),
-                            choices=[i18n("Yes"), i18n("No")],
-                            value=i18n("Yes"),
+                        if_f0__ = gr.Radio(
+                            label=i18n(
+                                "Whether the model has pitch guidance (1: yes, 0: no)"
+                            ),
+                            choices=["1", "0"],
+                            value="1",
                             interactive=True,
                         )
-                        info__ = gr.Textbox(
-                            label=i18n("Model information to be placed"),
-                            value="",
-                            max_lines=8,
-                            interactive=True,
-                        )
-                    with gr.Column():
-                        name_to_save0 = gr.Textbox(
-                            label=i18n("Saved model name (without extension)"),
-                            value="",
-                            max_lines=1,
-                            interactive=True,
-                        )
-                        version_2 = gr.Radio(
+                        version_1 = gr.Radio(
                             label=i18n("Model architecture version"),
                             choices=["v1", "v2"],
-                            value="v1",
+                            value="v2",
                             interactive=True,
                         )
-                        but6 = gr.Button(i18n("Fusion"), variant="primary")
-                with gr.Row():
-                    info4 = gr.Textbox(label=i18n("Output information"), value="")
-                but6.click(
-                    merge,
-                    [
-                        ckpt_a,
-                        ckpt_b,
-                        alpha_a,
-                        sr_,
-                        if_f0_,
-                        info__,
-                        name_to_save0,
-                        version_2,
-                    ],
-                    info4,
-                    api_name="ckpt_merge",
-                )  # def merge(path1,path2,alpha1,sr,f0,info):
-            with gr.Group():
-                gr.Markdown(
-                    value=i18n(
-                        "### Modify model information\n> Only supported for small model files extracted from the 'weights' folder."
+                    info___ = gr.Textbox(
+                        label=i18n("Model information to be placed"),
+                        value="",
+                        max_lines=8,
+                        interactive=True,
                     )
-                )
-                with gr.Row():
-                    with gr.Column():
-                        ckpt_path0 = gr.Textbox(
-                            label=i18n("Path to Model"), value="", interactive=True
-                        )
-                        info_ = gr.Textbox(
-                            label=i18n("Model information to be modified"),
-                            value="",
-                            max_lines=8,
-                            interactive=True,
-                        )
-                        name_to_save1 = gr.Textbox(
-                            label=i18n(
-                                "Save file name (default: same as the source file)"
-                            ),
-                            value="",
-                            max_lines=1,
-                            interactive=True,
-                        )
-                    with gr.Column():
-                        but7 = gr.Button(i18n("Modify"), variant="primary")
-                        info5 = gr.Textbox(label=i18n("Output information"), value="")
-                but7.click(
-                    change_info,
-                    [ckpt_path0, info_, name_to_save1],
-                    info5,
-                    api_name="ckpt_modify",
-                )
-            with gr.Group():
-                gr.Markdown(
-                    value=i18n(
-                        "### View model information\n> Only supported for small model files extracted from the 'weights' folder."
+                    extauthor = gr.Textbox(
+                        label=i18n("Model Author"),
+                        value="",
+                        max_lines=1,
+                        interactive=True,
                     )
-                )
-                with gr.Row():
-                    with gr.Column():
-                        ckpt_path1 = gr.File(label=i18n("Path to Model"))
-                        but8 = gr.Button(i18n("View"), variant="primary")
-                    with gr.Column():
-                        info6 = gr.Textbox(label=i18n("Output information"), value="")
-                but8.click(show_info, [ckpt_path1], info6, api_name="ckpt_show")
-            with gr.Group():
-                gr.Markdown(
-                    value=i18n(
-                        "### 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."
+                with gr.Column():
+                    but9 = gr.Button(i18n("Extract"), variant="primary")
+                    info7 = gr.Textbox(label=i18n("Output information"), value="")
+                    ckpt_path2.change(
+                        change_info_, [ckpt_path2], [sr__, if_f0__, version_1]
                     )
-                )
-                with gr.Row():
-                    with gr.Column():
-                        ckpt_path2 = gr.Textbox(
-                            label=i18n("Path to Model"),
-                            value="E:\\codes\\py39\\logs\\mi-test_f0_48k\\G_23333.pth",
-                            interactive=True,
-                        )
-                        save_name = gr.Textbox(
-                            label=i18n("Save name"), value="", interactive=True
-                        )
-                        with gr.Row():
-                            sr__ = gr.Radio(
-                                label=i18n("Target sample rate"),
-                                choices=["32k", "40k", "48k"],
-                                value="40k",
-                                interactive=True,
-                            )
-                            if_f0__ = gr.Radio(
-                                label=i18n(
-                                    "Whether the model has pitch guidance (1: yes, 0: no)"
-                                ),
-                                choices=["1", "0"],
-                                value="1",
-                                interactive=True,
-                            )
-                            version_1 = gr.Radio(
-                                label=i18n("Model architecture version"),
-                                choices=["v1", "v2"],
-                                value="v2",
-                                interactive=True,
-                            )
-                        info___ = gr.Textbox(
-                            label=i18n("Model information to be placed"),
-                            value="",
-                            max_lines=8,
-                            interactive=True,
-                        )
-                        extauthor = gr.Textbox(
-                            label=i18n("Model Author"),
-                            value="",
-                            max_lines=1,
-                            interactive=True,
-                        )
-                    with gr.Column():
-                        but9 = gr.Button(i18n("Extract"), variant="primary")
-                        info7 = gr.Textbox(label=i18n("Output information"), value="")
-                        ckpt_path2.change(
-                            change_info_, [ckpt_path2], [sr__, if_f0__, version_1]
-                        )
-                but9.click(
-                    extract_small_model,
-                    [
-                        ckpt_path2,
-                        save_name,
-                        extauthor,
-                        sr__,
-                        if_f0__,
-                        info___,
-                        version_1,
-                    ],
-                    info7,
-                    api_name="ckpt_extract",
-                )
+            but9.click(
+                extract_small_model,
+                [
+                    ckpt_path2,
+                    save_name,
+                    extauthor,
+                    sr__,
+                    if_f0__,
+                    info___,
+                    version_1,
+                ],
+                info7,
+                api_name="ckpt_extract",
+            )
 
         with gr.TabItem(i18n("Export Onnx")):
             with gr.Row():

From e81b7c52c0846f9f3d8a52e262e52cdafebc9e0b Mon Sep 17 00:00:00 2001
From: "github-actions[bot]"
 <41898282+github-actions[bot]@users.noreply.github.com>
Date: Tue, 11 Jun 2024 16:32:14 +0900
Subject: [PATCH 7/7] chore(format): run black on dev (#36)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
---
 web.py | 112 ++++++++++++++++++++++++++++-----------------------------
 1 file changed, 54 insertions(+), 58 deletions(-)

diff --git a/web.py b/web.py
index bf0fc6a..4b8f483 100644
--- a/web.py
+++ b/web.py
@@ -1126,62 +1126,60 @@ with gr.Blocks(title="RVC WebUI") as app:
                 )
             )
             with gr.Row():
-                    with gr.Column():
-                        dir_wav_input = gr.Textbox(
-                            label=i18n(
-                                "Enter the path of the audio folder to be processed"
-                            ),
-                            placeholder="C:\\Users\\Desktop\\todo-songs",
-                        )
-                        wav_inputs = gr.File(
-                            file_count="multiple",
-                            label=i18n(
-                                "Multiple audio files can also be imported. If a folder path exists, this input is ignored."
-                            ),
-                        )
-                    with gr.Column():
-                        model_choose = gr.Dropdown(
-                            label=i18n("Model"), choices=uvr5_names
-                        )
-                        agg = gr.Slider(
-                            minimum=0,
-                            maximum=20,
-                            step=1,
-                            label="人声提取激进程度",
-                            value=10,
-                            interactive=True,
-                            visible=False,  # 先不开放调整
-                        )
-                        opt_vocal_root = gr.Textbox(
-                            label=i18n("Specify the output folder for vocals"),
-                            value="opt",
-                        )
-                        opt_ins_root = gr.Textbox(
-                            label=i18n("Specify the output folder for accompaniment"),
-                            value="opt",
-                        )
-                        format0 = gr.Radio(
-                            label=i18n("Export file format"),
-                            choices=["wav", "flac", "mp3", "m4a"],
-                            value="flac",
-                            interactive=True,
-                        )
-                    but2 = gr.Button(i18n("Convert"), variant="primary")
-                    vc_output4 = gr.Textbox(label=i18n("Output information"))
-                    but2.click(
-                        uvr,
-                        [
-                            model_choose,
-                            dir_wav_input,
-                            opt_vocal_root,
-                            wav_inputs,
-                            opt_ins_root,
-                            agg,
-                            format0,
-                        ],
-                        [vc_output4],
-                        api_name="uvr_convert",
+                with gr.Column():
+                    dir_wav_input = gr.Textbox(
+                        label=i18n(
+                            "Enter the path of the audio folder to be processed"
+                        ),
+                        placeholder="C:\\Users\\Desktop\\todo-songs",
                     )
+                    wav_inputs = gr.File(
+                        file_count="multiple",
+                        label=i18n(
+                            "Multiple audio files can also be imported. If a folder path exists, this input is ignored."
+                        ),
+                    )
+                with gr.Column():
+                    model_choose = gr.Dropdown(label=i18n("Model"), choices=uvr5_names)
+                    agg = gr.Slider(
+                        minimum=0,
+                        maximum=20,
+                        step=1,
+                        label="人声提取激进程度",
+                        value=10,
+                        interactive=True,
+                        visible=False,  # 先不开放调整
+                    )
+                    opt_vocal_root = gr.Textbox(
+                        label=i18n("Specify the output folder for vocals"),
+                        value="opt",
+                    )
+                    opt_ins_root = gr.Textbox(
+                        label=i18n("Specify the output folder for accompaniment"),
+                        value="opt",
+                    )
+                    format0 = gr.Radio(
+                        label=i18n("Export file format"),
+                        choices=["wav", "flac", "mp3", "m4a"],
+                        value="flac",
+                        interactive=True,
+                    )
+                but2 = gr.Button(i18n("Convert"), variant="primary")
+                vc_output4 = gr.Textbox(label=i18n("Output information"))
+                but2.click(
+                    uvr,
+                    [
+                        model_choose,
+                        dir_wav_input,
+                        opt_vocal_root,
+                        wav_inputs,
+                        opt_ins_root,
+                        agg,
+                        format0,
+                    ],
+                    [vc_output4],
+                    api_name="uvr_convert",
+                )
         with gr.TabItem(i18n("Train")):
             gr.Markdown(
                 value=i18n(
@@ -1567,9 +1565,7 @@ with gr.Blocks(title="RVC WebUI") as app:
                         interactive=True,
                     )
                     name_to_save1 = gr.Textbox(
-                        label=i18n(
-                            "Save file name (default: same as the source file)"
-                        ),
+                        label=i18n("Save file name (default: same as the source file)"),
                         value="",
                         max_lines=1,
                         interactive=True,