diff --git a/gui.py b/gui.py index 06c5cb9..0dfb15b 100644 --- a/gui.py +++ b/gui.py @@ -241,7 +241,7 @@ if __name__ == "__main__": layout = [ [ sg.Frame( - title=i18n("加载模型"), + title=i18n("Load model"), layout=[ [ sg.Input( @@ -249,7 +249,7 @@ if __name__ == "__main__": key="pth_path", ), sg.FileBrowse( - i18n("选择.pth文件"), + i18n("Select the .pth file"), initial_folder=os.path.join( os.getcwd(), "assets/weights" ), @@ -262,7 +262,7 @@ if __name__ == "__main__": key="index_path", ), sg.FileBrowse( - i18n("选择.index文件"), + i18n("Select the .index file"), initial_folder=os.path.join(os.getcwd(), "logs"), file_types=[("Index File", "*.index")], ), @@ -274,7 +274,7 @@ if __name__ == "__main__": sg.Frame( layout=[ [ - sg.Text(i18n("设备类型")), + sg.Text(i18n("Device type")), sg.Combo( self.hostapis, key="sg_hostapi", @@ -283,14 +283,14 @@ if __name__ == "__main__": size=(20, 1), ), sg.Checkbox( - i18n("独占 WASAPI 设备"), + i18n("Takeover WASAPI device"), key="sg_wasapi_exclusive", default=data.get("sg_wasapi_exclusive", False), enable_events=True, ), ], [ - sg.Text(i18n("输入设备")), + sg.Text(i18n("Input device")), sg.Combo( self.input_devices, key="sg_input_device", @@ -300,7 +300,7 @@ if __name__ == "__main__": ), ], [ - sg.Text(i18n("输出设备")), + sg.Text(i18n("Output device")), sg.Combo( self.output_devices, key="sg_output_device", @@ -310,33 +310,35 @@ if __name__ == "__main__": ), ], [ - sg.Button(i18n("重载设备列表"), key="reload_devices"), + sg.Button( + i18n("Reload device list"), key="reload_devices" + ), sg.Radio( - i18n("使用模型采样率"), + i18n("Choose sample rate of the model"), "sr_type", key="sr_model", default=data.get("sr_model", True), enable_events=True, ), sg.Radio( - i18n("使用设备采样率"), + i18n("Choose sample rate of the device"), "sr_type", key="sr_device", default=data.get("sr_device", False), enable_events=True, ), - sg.Text(i18n("采样率")), + sg.Text(i18n("Sampling rate")), sg.Text("", key="sr_stream"), ], ], - title=i18n("音频设备"), + title=i18n("Audio device"), ) ], [ sg.Frame( layout=[ [ - sg.Text(i18n("响应阈值")), + sg.Text(i18n("Response threshold")), sg.Slider( range=(-60, 0), key="threhold", @@ -347,7 +349,7 @@ if __name__ == "__main__": ), ], [ - sg.Text(i18n("音调设置")), + sg.Text(i18n("Pitch settings")), sg.Slider( range=(-24, 24), key="pitch", @@ -358,7 +360,7 @@ if __name__ == "__main__": ), ], [ - sg.Text(i18n("共振偏移")), + sg.Text(i18n("Formant offset")), sg.Slider( range=(-5, 5), key="formant", @@ -369,7 +371,11 @@ if __name__ == "__main__": ), ], [ - sg.Text(i18n("检索特征占比")), + sg.Text( + i18n( + "Search feature ratio (controls accent strength, too high has artifacting)" + ) + ), sg.Slider( range=(0.0, 1.0), key="index_rate", @@ -380,7 +386,7 @@ if __name__ == "__main__": ), ], [ - sg.Text(i18n("响度因子")), + sg.Text(i18n("Loudness factor")), sg.Slider( range=(0.0, 1.0), key="rms_mix_rate", @@ -391,7 +397,7 @@ if __name__ == "__main__": ), ], [ - sg.Text(i18n("音高算法")), + sg.Text(i18n("Pitch detection algorithm")), sg.Radio( "pm", "f0method", @@ -429,12 +435,12 @@ if __name__ == "__main__": ), ], ], - title=i18n("常规设置"), + title=i18n("General settings"), ), sg.Frame( layout=[ [ - sg.Text(i18n("采样长度")), + sg.Text(i18n("Sample length")), sg.Slider( range=(0.02, 1.5), key="block_time", @@ -456,7 +462,11 @@ if __name__ == "__main__": # ), # ], [ - sg.Text(i18n("harvest进程数")), + sg.Text( + i18n( + "Number of CPU processes used for harvest pitch algorithm" + ) + ), sg.Slider( range=(1, n_cpu), key="n_cpu", @@ -469,7 +479,7 @@ if __name__ == "__main__": ), ], [ - sg.Text(i18n("淡入淡出长度")), + sg.Text(i18n("Fade length")), sg.Slider( range=(0.01, 0.15), key="crossfade_length", @@ -480,7 +490,7 @@ if __name__ == "__main__": ), ], [ - sg.Text(i18n("额外推理时长")), + sg.Text(i18n("Extra inference time")), sg.Slider( range=(0.05, 5.00), key="extra_time", @@ -492,17 +502,17 @@ if __name__ == "__main__": ], [ sg.Checkbox( - i18n("输入降噪"), + i18n("Input noise reduction"), key="I_noise_reduce", enable_events=True, ), sg.Checkbox( - i18n("输出降噪"), + i18n("Output noise reduction"), key="O_noise_reduce", enable_events=True, ), sg.Checkbox( - i18n("启用相位声码器"), + i18n("Enable phase vocoder"), key="use_pv", default=data.get("use_pv", False), enable_events=True, @@ -516,29 +526,29 @@ if __name__ == "__main__": ], # [sg.Text("注：首次使用JIT加速时，会出现卡顿，\n 并伴随一些噪音，但这是正常现象！")], ], - title=i18n("性能设置"), + title=i18n("Performance settings"), ), ], [ - sg.Button(i18n("开始音频转换"), key="start_vc"), - sg.Button(i18n("停止音频转换"), key="stop_vc"), + sg.Button(i18n("Start audio conversion"), key="start_vc"), + sg.Button(i18n("Stop audio conversion"), key="stop_vc"), sg.Radio( - i18n("输入监听"), + i18n("Input voice monitor"), "function", key="im", default=False, enable_events=True, ), sg.Radio( - i18n("输出变声"), + i18n("Output converted voice"), "function", key="vc", default=True, enable_events=True, ), - sg.Text(i18n("算法延迟(ms)")), + sg.Text(i18n("Algorithmic delays (ms)")), sg.Text("0", key="delay_time"), - sg.Text(i18n("推理时间(ms)")), + sg.Text(i18n("Inference time (ms)")), sg.Text("0", key="infer_time"), ], ] @@ -669,17 +679,17 @@ if __name__ == "__main__": def set_values(self, values): if len(values["pth_path"].strip()) == 0: - sg.popup(i18n("请选择pth文件")) + sg.popup(i18n("Please choose the .pth file")) return False if len(values["index_path"].strip()) == 0: - sg.popup(i18n("请选择index文件")) + sg.popup(i18n("Please choose the .index file")) return False pattern = re.compile("[^\x00-\x7F]+") if pattern.findall(values["pth_path"]): - sg.popup(i18n("pth文件路径不可包含中文")) + sg.popup(i18n("pth path cannot contain unicode characters")) return False if pattern.findall(values["index_path"]): - sg.popup(i18n("index文件路径不可包含中文")) + sg.popup(i18n("index path cannot contain unicode characters")) return False self.set_devices(values["sg_input_device"], values["sg_output_device"]) self.config.use_jit = False # values["use_jit"] diff --git a/i18n/locale/en_US.json b/i18n/locale/en_US.json index 0c7d256..6cf6a47 100644 --- a/i18n/locale/en_US.json +++ b/i18n/locale/en_US.json @@ -1,160 +1,157 @@ { - "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### Modify 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": "### View model information\n> Only supported for small model files extracted from the 'weights' folder.", - "### 模型提取\n> 输入logs文件夹下大文件模型路径\n\n适用于训一半不想训了模型没有自动提取保存小文件模型, 或者想测试中间模型的情况": "### Model extraction\n> Enter the path of the large file model under the 'logs' folder.\n\nThis is useful if you want to stop training halfway and manually extract and save a small model file, or if you want to test an intermediate model.", - "### 模型比较\n> 模型ID(长)请于下方`查看模型信息`中获得\n\n可用于比较两模型推理相似度": "### Model comparison\n> You can get model ID (long) from `View model information` below.\n\nCalculate a similarity between two models.", - "### 模型融合\n可用于测试音色融合": "### Model fusion\nCan be used to test timbre fusion.", - "### 第一步 填写实验配置\n实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件.": "### Step 1. Fill in the experimental configuration.\nExperimental data is stored in the 'logs' folder, with each experiment having a separate folder. Manually enter the experiment name path, which contains the experimental configuration, logs, and trained model files.", - "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.": "### Step 3. Start training.\nFill in the training settings and start training the model and index.", - "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.": "### Step 2. Audio processing. \n#### 1. Slicing.\nAutomatically traverse all files in the training folder that can be decoded into audio and perform slice normalization. Generates 2 wav folders in the experiment directory. Currently, only single-singer/speaker training is supported.", - "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).": "#### 2. Feature extraction.\nUse CPU to extract pitch (if the model has pitch), use GPU to extract features (select GPU index).", - ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音": "If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness.", - "A模型ID(长)": "ID of model A (long)", - "A模型权重": "Weight (w) for Model A", - "A模型路径": "Path to Model A", - "B模型ID(长)": "ID of model B (long)", - "B模型路径": "Path to Model B", - "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调": "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation", - "ID(短)": "ID(short)", - "ID(长)": "ID(long)", + "### 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.", + "### 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 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.", + "#### 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", + "Algorithmic delays (ms)": "Algorithmic delays (ms)", + "All processes have been completed!": "All processes have been completed!", + "Audio device": "Audio device", + "Auto-detect index path and select from the dropdown": "Auto-detect index path and select from the dropdown", + "Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt').": "Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt').", + "Batch inference": "Batch inference", + "Batch processing for vocal accompaniment separation using the UVR5 model.
Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).
The model is divided into three categories:
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.
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.
3. De-reverb and de-delay models (by FoxJoy):
  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;
 (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.
De-reverb/de-delay notes:
1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.
2. The MDX-Net-Dereverb model is quite slow.
3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive.": "Batch processing for vocal accompaniment separation using the UVR5 model.
Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).
The model is divided into three categories:
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.
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.
3. De-reverb and de-delay models (by FoxJoy):
  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;
 (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.
De-reverb/de-delay notes:
1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.
2. The MDX-Net-Dereverb model is quite slow.
3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive.", + "Batch size per GPU": "Batch size per GPU", + "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": "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", + "Calculate": "Calculate", + "Choose sample rate of the device": "Choose sample rate of the device", + "Choose sample rate of the model": "Choose sample rate of the model", + "Convert": "Convert", + "Device type": "Device type", + "Enable phase vocoder": "Enable phase vocoder", + "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1": "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1", + "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2": "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2", + "Enter the experiment name": "Enter the experiment name", + "Enter the path of the audio folder to be processed": "Enter the path of the audio folder to be processed", + "Enter the path of the audio folder to be processed (copy it from the address bar of the file manager)": "Enter the path of the audio folder to be processed (copy it from the address bar of the file manager)", + "Enter the path of the training folder": "Enter the path of the training folder", + "Exist": "Exist", + "Export Onnx": "Export Onnx", + "Export Onnx Model": "Export Onnx Model", + "Export audio (click on the three dots in the lower right corner to download)": "Export audio (click on the three dots in the lower right corner to download)", + "Export file format": "Export file format", + "Extra inference time": "Extra inference time", + "Extract": "Extract", + "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation": "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation", + "FAQ (Frequently Asked Questions)": "FAQ (Frequently Asked Questions)", + "Fade length": "Fade length", + "Fail": "Fail", + "Feature extraction": "Feature extraction", + "Formant offset": "Formant offset", + "Fusion": "Fusion", + "GPU Information": "GPU Information", + "General settings": "General settings", + "Hidden": "Hidden", + "ID of model A (long)": "ID of model A (long)", + "ID of model B (long)": "ID of model B (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", + "Information": "Information", + "Input device": "Input device", + "Input noise reduction": "Input noise reduction", + "Input voice monitor": "Input voice monitor", + "Link index to outside folder": "Link index to outside folder", + "Load model": "Load model", + "Load pre-trained base model D path": "Load pre-trained base model D path", + "Load pre-trained base model G path": "Load pre-trained base model G path", + "Loudness factor": "Loudness factor", + "Model": "Model", + "Model Author": "Model Author", + "Model Author (Nullable)": "Model Author (Nullable)", + "Model Inference": "Model Inference", + "Model architecture version": "Model architecture version", + "Model info": "Model info", + "Model information to be modified": "Model information to be modified", + "Model information to be placed": "Model information to be placed", + "Model name": "Model name", + "Modify": "Modify", + "Multiple audio files can also be imported. If a folder path exists, this input is ignored.": "Multiple audio files can also be imported. If a folder path exists, this input is ignored.", + "No": "No", "None": "None", - "Onnx导出": "Export Onnx", - "Onnx输出路径": "Onnx Export Path", - "RVC模型路径": "RVC Model Path", + "Not exist": "Not exist", + "Number of CPU processes used for harvest pitch algorithm": "Number of CPU processes used for harvest pitch algorithm", + "Number of CPU processes used for pitch extraction and data processing": "Number of CPU processes used for pitch extraction and data processing", + "One-click training": "One-click training", + "Onnx Export Path": "Onnx Export Path", + "Output converted voice": "Output converted voice", + "Output device": "Output device", + "Output information": "Output information", + "Output noise reduction": "Output noise reduction", + "Path to Model": "Path to Model", + "Path to Model A": "Path to Model A", + "Path to Model B": "Path to Model B", + "Path to the feature index file. Leave blank to use the selected result from the dropdown": "Path to the feature index file. Leave blank to use the selected result from the dropdown", + "Performance settings": "Performance settings", + "Pitch detection algorithm": "Pitch detection algorithm", + "Pitch guidance (f0)": "Pitch guidance (f0)", + "Pitch settings": "Pitch settings", + "Please choose the .index file": "Please choose the .index file", + "Please choose the .pth file": "Please choose the .pth file", + "Please specify the speaker/singer ID": "Please specify the speaker/singer ID", + "Process data": "Process data", + "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": "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", + "RVC Model Path": "RVC Model Path", + "Read from model": "Read from model", + "Refresh voice list and index path": "Refresh voice list and index path", + "Reload device list": "Reload device list", + "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling": "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling", + "Response threshold": "Response threshold", + "Sample length": "Sample length", + "Sampling rate": "Sampling rate", + "Save a small final model to the 'weights' folder at each save point": "Save a small final model to the 'weights' folder at each save point", + "Save file name (default: same as the source file)": "Save file name (default: same as the source file)", + "Save frequency (save_every_epoch)": "Save frequency (save_every_epoch)", + "Save name": "Save name", + "Save only the latest '.ckpt' file to save disk space": "Save only the latest '.ckpt' file to save disk space", + "Saved model name (without extension)": "Saved model name (without extension)", + "Sealing date": "Sealing date", + "Search feature ratio (controls accent strength, too high has artifacting)": "Search feature ratio (controls accent strength, too high has artifacting)", + "Select Speaker/Singer ID": "Select Speaker/Singer ID", + "Select the .index file": "Select the .index file", + "Select the .pth file": "Select the .pth file", + "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": "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", + "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": "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", + "Similarity": "Similarity", + "Similarity (from 0 to 1)": "Similarity (from 0 to 1)", + "Single inference": "Single inference", + "Specify output folder": "Specify output folder", + "Specify the output folder for accompaniment": "Specify the output folder for accompaniment", + "Specify the output folder for vocals": "Specify the output folder for vocals", + "Start audio conversion": "Start audio conversion", + "Step 1: Processing data": "Step 1: Processing data", + "Step 3a: Model training started": "Step 3a: Model training started", + "Stop audio conversion": "Stop audio conversion", + "Successfully built index into": "Successfully built index into", + "Takeover WASAPI device": "Takeover WASAPI device", + "Target sample rate": "Target sample rate", + "The audio file to be processed": "The audio file to be processed", + "This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible.
If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory Agreement-LICENSE.txt for details.": "This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible.
If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory Agreement-LICENSE.txt for details.", + "Total training epochs (total_epoch)": "Total training epochs (total_epoch)", + "Train": "Train", + "Train feature index": "Train feature index", + "Train model": "Train model", + "Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder.": "Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder.", + "Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)": "Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)", + "Unfortunately, there is no compatible GPU available to support your training.": "Unfortunately, there is no compatible GPU available to support your training.", "Unknown": "Unknown", - "ckpt处理": "ckpt Processing", - "harvest进程数": "Number of CPU processes used for harvest pitch algorithm", - "index文件路径不可包含中文": "index path cannot contain unicode characters", - "pth文件路径不可包含中文": "pth path cannot contain unicode characters", - "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程": "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1", - "step1:正在处理数据": "Step 1: Processing data", - "step2:正在提取音高&正在提取特征": "step2:Pitch extraction & feature extraction", - "step3a:正在训练模型": "Step 3a: Model training started", - "一键训练": "One-click training", - "不显示": "Hidden", - "也可批量输入音频文件, 二选一, 优先读文件夹": "Multiple audio files can also be imported. If a folder path exists, this input is ignored.", - "人声伴奏分离批量处理， 使用UVR5模型。
合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
模型分为三类：
1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
3、去混响、去延迟模型（by FoxJoy）：
  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
 (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
去混响/去延迟，附：
1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
2、MDX-Net-Dereverb模型挺慢的；
3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。": "Batch processing for vocal accompaniment separation using the UVR5 model.
Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).
The model is divided into three categories:
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.
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.
3. De-reverb and de-delay models (by FoxJoy):
  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;
 (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.
De-reverb/de-delay notes:
1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.
2. The MDX-Net-Dereverb model is quite slow.
3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive.", - "从模型中读取": "Read from model", - "以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2": "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2", - "伴奏人声分离&去混响&去回声": "Vocals/Accompaniment Separation & Reverberation Removal", - "使用模型采样率": "Choose sample rate of the model", - "使用设备采样率": "Choose sample rate of the device", - "保存名": "Save name", - "保存的文件名, 默认空为和源文件同名": "Save file name (default: same as the source file)", - "保存的模型名不带后缀": "Saved model name (without extension)", - "保存频率save_every_epoch": "Save frequency (save_every_epoch)", - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果": "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", - "信息": "Information", - "修改": "Modify", - "停止音频转换": "Stop audio conversion", - "全流程结束！": "All processes have been completed!", - "共振偏移": "Resonating offset", - "刷新音色列表和索引路径": "Refresh voice list and index path", - "加载模型": "Load model", - "加载预训练底模D路径": "Load pre-trained base model D path", - "加载预训练底模G路径": "Load pre-trained base model G path", - "单次推理": "Single inference", - "卸载音色省显存": "Unload model to save GPU memory", - "变调(整数, 半音数量, 升八度12降八度-12)": "Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)", - "后处理重采样至最终采样率，0为不进行重采样": "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling", - "否": "No", - "启用相位声码器": "Enable phase vocoder", - "响应阈值": "Response threshold", - "响度因子": "loudness factor", - "处理数据": "Process data", - "失败": "Fail", - "实际计算": "Actually calculated", - "导出Onnx模型": "Export Onnx Model", - "导出文件格式": "Export file format", - "封装时间": "Sealing date", - "常见问题解答": "FAQ (Frequently Asked Questions)", - "常规设置": "General settings", - "开始音频转换": "Start audio conversion", - "待处理音频文件": "The audio file to be processed", - "很遗憾您这没有能用的显卡来支持您训练": "Unfortunately, there is no compatible GPU available to support your training.", - "性能设置": "Performance settings", - "总训练轮数total_epoch": "Total training epochs (total_epoch)", - "成功构建索引到": "Successfully built index into", - "批量推理": "Batch inference", - "批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ": "Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt').", - "指定输出主人声文件夹": "Specify the output folder for vocals", - "指定输出文件夹": "Specify output folder", - "指定输出非主人声文件夹": "Specify the output folder for accompaniment", - "推理时间(ms)": "Inference time (ms)", - "推理音色": "Inferencing voice", - "提取": "Extract", - "提取音高和处理数据使用的CPU进程数": "Number of CPU processes used for pitch extraction and data processing", - "无": "Not exist", - "是": "Yes", - "是否仅保存最新的ckpt文件以节省硬盘空间": "Save only the latest '.ckpt' file to save disk space", - "是否在每次保存时间点将最终小模型保存至weights文件夹": "Save a small final model to the 'weights' folder at each save point", - "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速": "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", - "显卡信息": "GPU Information", - "有": "Exist", - "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.": "This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible.
If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory Agreement-LICENSE.txt for details.", - "查看": "View", - "检索特征占比": "Search feature ratio (controls accent strength, too high has artifacting)", - "模型": "Model", - "模型作者": "Model Author", - "模型作者(可空)": "Model Author (Nullable)", - "模型信息": "Model info", - "模型名": "Model name", - "模型推理": "Model Inference", - "模型是否带音高指导": "Whether the model has pitch guidance", - "模型是否带音高指导(唱歌一定要, 语音可以不要)": "Whether the model has pitch guidance (required for singing, optional for speech)", - "模型是否带音高指导,1是0否": "Whether the model has pitch guidance (1: yes, 0: no)", - "模型版本型号": "Model architecture version", - "模型路径": "Path to Model", - "每张显卡的batch_size": "Batch size per GPU", - "淡入淡出长度": "Fade length", - "版本": "Version", - "特征提取": "Feature extraction", - "特征检索库文件路径,为空则使用下拉的选择结果": "Path to the feature index file. Leave blank to use the selected result from the dropdown", - "独占 WASAPI 设备": "Takeover WASAPI device", - "男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ": "Recommended +12 key for male to female conversion, and -12 key for female to male conversion. If the sound range goes too far and the voice is distorted, you can also adjust it to the appropriate range by yourself.", - "目标采样率": "Target sample rate", - "相似度": "Similarity", - "相似度(0到1)": "Similarity (from 0 to 1)", - "算法延迟(ms)": "Algorithmic delays (ms)", - "自动检测index路径,下拉式选择(dropdown)": "Auto-detect index path and select from the dropdown", - "融合": "Fusion", - "要改的模型信息": "Model information to be modified", - "要置入的模型信息": "Model information to be placed", - "计算": "Calculate", - "训练": "Train", - "训练模型": "Train model", - "训练特征索引": "Train feature index", - "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": "Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder.", - "设备类型": "Device type", - "请指定说话人id": "Please specify the speaker/singer ID", - "请选择index文件": "Please choose the .index file", - "请选择pth文件": "Please choose the .pth file", - "请选择说话人id": "Select Speaker/Singer ID", - "转换": "Convert", - "输入实验名": "Enter the experiment name", - "输入待处理音频文件夹路径": "Enter the path of the audio folder to be processed", - "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)": "Enter the path of the audio folder to be processed (copy it from the address bar of the file manager)", - "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": "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", - "输入监听": "Input voice monitor", - "输入训练文件夹路径": "Enter the path of the training folder", - "输入设备": "Input device", - "输入降噪": "Input noise reduction", - "输出信息": "Output information", - "输出变声": "Output converted voice", - "输出设备": "Output device", - "输出降噪": "Output noise reduction", - "输出音频(右下角三个点,点了可以下载)": "Export audio (click on the three dots in the lower right corner to download)", - "选择.index文件": "Select the .index file", - "选择.pth文件": "Select the .pth file", - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU": "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", - "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU": "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", - "采样率": "Sampling rate", - "采样长度": "Sample length", - "重载设备列表": "Reload device list", - "链接索引到外部": "Link index to outside folder", - "音调设置": "Pitch settings", - "音频设备": "Audio device", - "音高引导(f0)": "Pitch guidance (f0)", - "音高算法": "Pitch detection algorithm", - "额外推理时长": "Extra inference time" + "Unload model to save GPU memory": "Unload model to save GPU memory", + "Version": "Version", + "View": "View", + "Vocals/Accompaniment Separation & Reverberation Removal": "Vocals/Accompaniment Separation & Reverberation Removal", + "Weight (w) for Model A": "Weight (w) for Model A", + "Whether the model has pitch guidance": "Whether the model has pitch guidance", + "Whether the model has pitch guidance (1: yes, 0: no)": "Whether the model has pitch guidance (1: yes, 0: no)", + "Whether the model has pitch guidance (required for singing, optional for speech)": "Whether the model has pitch guidance (required for singing, optional for speech)", + "Yes": "Yes", + "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" } diff --git a/i18n/locale/es_ES.json b/i18n/locale/es_ES.json index 9d74955..5ab3e23 100644 --- a/i18n/locale/es_ES.json +++ b/i18n/locale/es_ES.json @@ -1,160 +1,157 @@ { - "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### Modificar la información del modelo\n> Solo admite archivos de modelos pequeños extraídos en la carpeta 'weights'.", - "### 查看模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### Ver información del modelo\n> Solo aplicable a archivos de modelos pequeños extraídos de la carpeta 'weights'.", - "### 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": "### 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.", - "### 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": "### Comparación de modelos\n> Obtén el ID del modelo (largo) en la sección de `Ver información del modelo` a continuación\n\nSe puede utilizar para comparar la similitud de la inferencia de dos modelos.", - "### 模型融合\n可用于测试音色融合": "### Fusión de modelos\nSe puede utilizar para fusionar diferentes voces.", - "### 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.": "### Paso 1. Complete la configuración del experimento.\nLos datos del experimento se almacenan en el directorio 'logs', con cada experimento en una carpeta separada. La ruta del nombre del experimento debe ingresarse manualmente y debe contener la configuración del experimento, los registros y los archivos del modelo entrenado.", - "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.": "### Paso tres: Comienza el entrenamiento\nCompleta la configuración de entrenamiento, comienza a entrenar el modelo y el índice.", - "### 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.": "### 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.", - "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).": "#### 2. 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).", - ">=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": "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", - "A模型ID(长)": "ID del modelo A (largo)", - "A模型权重": "Un peso modelo para el modelo A.", - "A模型路径": "Modelo A ruta.", - "B模型ID(长)": "ID del modelo B (largo)", - "B模型路径": "Modelo B ruta.", - "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调": "Archivo de curva F0, opcional, un tono por línea, en lugar de F0 predeterminado y cambio de tono", - "ID(短)": "ID (corto)", - "ID(长)": "ID (largo)", + "### 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.", + "### 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 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'.", + "#### 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", + "Algorithmic delays (ms)": "Retrasos algorítmicos (ms)", + "All processes have been completed!": "¡Todo el proceso ha terminado!", + "Audio device": "Dispositivo de audio", + "Auto-detect index path and select from the dropdown": "Detección automática de la ruta del índice, selección desplegable (dropdown)", + "Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt').": "Conversión por lotes, ingrese la carpeta que contiene los archivos de audio para convertir o cargue varios archivos de audio. El audio convertido se emitirá en la carpeta especificada (opción predeterminada).", + "Batch inference": "Inferencia por lotes", + "Batch processing for vocal accompaniment separation using the UVR5 model.
Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).
The model is divided into three categories:
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.
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.
3. De-reverb and de-delay models (by FoxJoy):
  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;
 (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.
De-reverb/de-delay notes:
1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.
2. The MDX-Net-Dereverb model is quite slow.
3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive.": "Procesamiento por lotes para la separación de acompañamiento vocal utilizando el modelo UVR5.
Ejemplo de formato de ruta de carpeta válido: D:\\ruta\\a\\la\\carpeta\\de\\entrada (copiar desde la barra de direcciones del administrador de archivos).
El modelo se divide en tres categorías:
1. Preservar voces: Elija esta opción para audio sin armonías. Preserva las voces mejor que HP5. Incluye dos modelos incorporados: HP2 y HP3. HP3 puede filtrar ligeramente el acompañamiento pero conserva las voces un poco mejor que HP2.
2. Preservar solo voces principales: Elija esta opción para audio con armonías. Puede debilitar las voces principales. Incluye un modelo incorporado: HP5.
3. Modelos de des-reverberación y des-retardo (por FoxJoy):
  (1) MDX-Net: La mejor opción para la eliminación de reverberación estéreo pero no puede eliminar la reverberación mono;
 (234) DeEcho: Elimina efectos de retardo. El modo Agresivo elimina más a fondo que el modo Normal. DeReverb adicionalmente elimina la reverberación y puede eliminar la reverberación mono, pero no muy efectivamente para contenido de alta frecuencia fuertemente reverberado.
Notas de des-reverberación/des-retardo:
1. El tiempo de procesamiento para el modelo DeEcho-DeReverb es aproximadamente el doble que los otros dos modelos DeEcho.
2. El modelo MDX-Net-Dereverb es bastante lento.
3. La configuración más limpia recomendada es aplicar primero MDX-Net y luego DeEcho-Agresivo.", + "Batch size per GPU": "Tamaño del lote (batch_size) por tarjeta gráfica", + "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": "Si almacenar en caché todos los conjuntos de entrenamiento en la memoria de la GPU. Los conjuntos de datos pequeños (menos de 10 minutos) se pueden almacenar en caché para acelerar el entrenamiento, pero el almacenamiento en caché de conjuntos de datos grandes puede causar errores de memoria en la GPU y no aumenta la velocidad de manera significativa.", + "Calculate": "Calcularlo", + "Choose sample rate of the device": "Elija la frecuencia de muestreo del dispositivo", + "Choose sample rate of the model": "Elija la frecuencia de muestreo del modelo", + "Convert": "Conversión", + "Device type": "Tipo de dispositivo", + "Enable phase vocoder": "Habilitar vocoder de fase", + "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1": "Separe los números de identificación de la GPU con '-' al ingresarlos. Por ejemplo, '0-1-2' significa usar GPU 0, GPU 1 y GPU 2.", + "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2": "Separe los números de identificación de la GPU con '-' al ingresarlos. Por ejemplo, '0-1-2' significa usar GPU 0, GPU 1 y GPU 2.", + "Enter the experiment name": "Ingrese el nombre del modelo", + "Enter the path of the audio folder to be processed": "Ingrese la ruta a la carpeta de audio que se procesará", + "Enter the path of the audio folder to be processed (copy it from the address bar of the file manager)": "Ingrese la ruta a la carpeta de audio que se procesará (simplemente cópiela desde la barra de direcciones del administrador de archivos)", + "Enter the path of the training folder": "Introduzca la ruta de la carpeta de entrenamiento", + "Exist": "Existente", + "Export Onnx": "Exportar Onnx", + "Export Onnx Model": "Exportar modelo Onnx", + "Export audio (click on the three dots in the lower right corner to download)": "Salida de audio (haga clic en los tres puntos en la esquina inferior derecha para descargar)", + "Export file format": "Formato de archivo de exportación", + "Extra inference time": "Tiempo de inferencia adicional", + "Extract": "Extraer", + "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation": "Archivo de curva F0, opcional, un tono por línea, en lugar de F0 predeterminado y cambio de tono", + "FAQ (Frequently Asked Questions)": "Preguntas frecuentes", + "Fade length": "Duración del fundido de entrada/salida", + "Fail": "Falló", + "Feature extraction": "Extracción de características", + "Formant offset": "Compensación resonante", + "Fusion": "Fusión", + "GPU Information": "información de la GPU", + "General settings": "Configuración general", + "Hidden": "Oculto", + "ID of model A (long)": "ID del modelo A (largo)", + "ID of model B (long)": "ID del modelo B (largo)", + "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", + "Information": "Información", + "Input device": "Dispositivo de entrada", + "Input noise reduction": "Reducción de ruido de entrada", + "Input voice monitor": "Monitor de voz de entrada", + "Link index to outside folder": "Vincular índice a carpeta externa", + "Load model": "Cargar modelo", + "Load pre-trained base model D path": "Cargue la ruta del modelo D base pre-entrenada.", + "Load pre-trained base model G path": "Cargue la ruta del modelo G base pre-entrenada.", + "Loudness factor": "factor de sonoridad", + "Model": "Modelo", + "Model Author": "Autor del modelo", + "Model Author (Nullable)": "Autor del modelo (anulable)", + "Model Inference": "inferencia del modelo", + "Model architecture version": "Versión y modelo del modelo", + "Model info": "Información del modelo", + "Model information to be modified": "Información del modelo a modificar", + "Model information to be placed": "Información del modelo a colocar.", + "Model name": "Nombre del modelo", + "Modify": "Modificar", + "Multiple audio files can also be imported. If a folder path exists, this input is ignored.": "También se pueden importar varios archivos de audio. Si existe una ruta de carpeta, esta entrada se ignora.", + "No": "No", "None": "Ninguno", - "Onnx导出": "Exportar Onnx", - "Onnx输出路径": "Ruta de salida Onnx", - "RVC模型路径": "Ruta del modelo RVC", + "Not exist": "Inexistente", + "Number of CPU processes used for harvest pitch algorithm": "Número de procesos", + "Number of CPU processes used for pitch extraction and data processing": "Número de procesos de CPU utilizados para extraer el tono y procesar los datos", + "One-click training": "Entrenamiento con un clic", + "Onnx Export Path": "Ruta de salida Onnx", + "Output converted voice": "Salida de voz convertida", + "Output device": "Dispositivo de salida", + "Output information": "Información de salida", + "Output noise reduction": "Reducción de ruido de salida", + "Path to Model": "Ruta del modelo", + "Path to Model A": "Modelo A ruta.", + "Path to Model B": "Modelo B ruta.", + "Path to the feature index file. Leave blank to use the selected result from the dropdown": "Ruta del archivo de la biblioteca de características, si está vacío, se utilizará el resultado de la selección desplegable", + "Performance settings": "Configuración de rendimiento", + "Pitch detection algorithm": "Algoritmo de tono", + "Pitch guidance (f0)": "Guía de tono (f0)", + "Pitch settings": "Ajuste de tono", + "Please choose the .index file": "Seleccione el archivo .index", + "Please choose the .pth file": "Seleccione el archivo .pth", + "Please specify the speaker/singer ID": "ID del modelo", + "Process data": "Procesar datos", + "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": "Proteger las consonantes claras y la respiración, prevenir artefactos como la distorsión de sonido electrónico, 0.5 no está activado, reducir aumentará la protección pero puede reducir el efecto del índice", + "RVC Model Path": "Ruta del modelo RVC", + "Read from model": "Leer del modelo", + "Refresh voice list and index path": "Actualizar la lista de modelos e índice de rutas", + "Reload device list": "Actualizar lista de dispositivos", + "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling": "Remuestreo posterior al proceso a la tasa de muestreo final, 0 significa no remuestrear", + "Response threshold": "Umbral de respuesta", + "Sample length": "Longitud de muestreo", + "Sampling rate": "Tasa de muestreo", + "Save a small final model to the 'weights' folder at each save point": "Guardar pequeño modelo final en la carpeta 'weights' en cada punto de guardado", + "Save file name (default: same as the source file)": "Nombre del archivo que se guardará, el valor predeterminado es el mismo que el nombre del archivo de origen", + "Save frequency (save_every_epoch)": "Frecuencia de guardado (save_every_epoch)", + "Save name": "Guardar nombre", + "Save only the latest '.ckpt' file to save disk space": "Guardar solo el archivo ckpt más reciente para ahorrar espacio en disco", + "Saved model name (without extension)": "Nombre del modelo guardado sin extensión.", + "Sealing date": "fecha de sellado", + "Search feature ratio (controls accent strength, too high has artifacting)": "Proporción de función de búsqueda", + "Select Speaker/Singer ID": "Seleccione una identificación de altavoz", + "Select the .index file": "Seleccione el archivo .index", + "Select the .pth file": "Seleccione el archivo .pth", + "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": "Seleccione el algoritmo de extracción de tono, use 'pm' para acelerar la entrada de canto, 'harvest' es bueno para los graves pero extremadamente lento, 'crepe' tiene buenos resultados pero consume GPU", + "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": "Seleccione el algoritmo de extracción de tono: la canción de entrada se puede acelerar con pm, la voz de alta calidad pero CPU pobre se puede acelerar con dio, harvest es mejor pero más lento, rmvpe es el mejor y se come ligeramente la CPU/GPU", + "Similarity": "Semejanza", + "Similarity (from 0 to 1)": "Similitud (de 0 a 1)", + "Single inference": "Inferencia única", + "Specify output folder": "Especificar carpeta de salida", + "Specify the output folder for accompaniment": "Especifique la carpeta de salida para las voces no principales", + "Specify the output folder for vocals": "Especifique la carpeta de salida para la voz principal", + "Start audio conversion": "Iniciar conversión de audio", + "Step 1: Processing data": "Paso 1: Procesando datos", + "Step 3a: Model training started": "Paso 3a: Entrenando el modelo", + "Stop audio conversion": "Detener la conversión de audio", + "Successfully built index into": "Índice construido con éxito en", + "Takeover WASAPI device": "Adquisición del dispositivo WASAPI", + "Target sample rate": "Tasa de muestreo objetivo", + "The audio file to be processed": "El archivo de audio a procesar", + "This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible.
If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory Agreement-LICENSE.txt for details.": "Este software es de código abierto bajo la licencia MIT, el autor no tiene ningún control sobre el software, y aquellos que usan el software y difunden los sonidos exportados por el software son los únicos responsables.
Si no está de acuerdo con esta cláusula , no puede utilizar ni citar ningún código ni archivo del paquete de software Consulte el directorio raíz Agreement-LICENSE.txt para obtener más información.", + "Total training epochs (total_epoch)": "Total de épocas de entrenamiento (total_epoch)", + "Train": "Entrenamiento", + "Train feature index": "Índice de características", + "Train model": "Entrenar Modelo", + "Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder.": "Entrenamiento finalizado, puede ver el registro de entrenamiento en la consola o en el archivo train.log en la carpeta del experimento", + "Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)": "Cambio de tono (entero, número de semitonos, subir una octava +12 o bajar una octava -12)", + "Unfortunately, there is no compatible GPU available to support your training.": "Lamentablemente, no tiene una tarjeta gráfica adecuada para soportar su entrenamiento", "Unknown": "Desconocido", - "ckpt处理": "Procesamiento de recibos", - "harvest进程数": "Número de procesos", - "index文件路径不可包含中文": "La ruta del archivo .index no debe contener caracteres chinos.", - "pth文件路径不可包含中文": "La ruta del archivo .pth no debe contener caracteres chinos.", - "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程": "Separe los números de identificación de la GPU con '-' al ingresarlos. Por ejemplo, '0-1-2' significa usar GPU 0, GPU 1 y GPU 2.", - "step1:正在处理数据": "Paso 1: Procesando datos", - "step2:正在提取音高&正在提取特征": "Paso 2: Extracción del tono y extracción de características", - "step3a:正在训练模型": "Paso 3a: Entrenando el modelo", - "一键训练": "Entrenamiento con un clic", - "不显示": "Oculto", - "也可批量输入音频文件, 二选一, 优先读文件夹": "También se pueden importar varios archivos de audio. Si existe una ruta de carpeta, esta entrada se ignora.", - "人声伴奏分离批量处理， 使用UVR5模型。
合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
模型分为三类：
1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
3、去混响、去延迟模型（by FoxJoy）：
  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
 (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
去混响/去延迟，附：
1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
2、MDX-Net-Dereverb模型挺慢的；
3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。": "Procesamiento por lotes para la separación de acompañamiento vocal utilizando el modelo UVR5.
Ejemplo de formato de ruta de carpeta válido: D:\\ruta\\a\\la\\carpeta\\de\\entrada (copiar desde la barra de direcciones del administrador de archivos).
El modelo se divide en tres categorías:
1. Preservar voces: Elija esta opción para audio sin armonías. Preserva las voces mejor que HP5. Incluye dos modelos incorporados: HP2 y HP3. HP3 puede filtrar ligeramente el acompañamiento pero conserva las voces un poco mejor que HP2.
2. Preservar solo voces principales: Elija esta opción para audio con armonías. Puede debilitar las voces principales. Incluye un modelo incorporado: HP5.
3. Modelos de des-reverberación y des-retardo (por FoxJoy):
  (1) MDX-Net: La mejor opción para la eliminación de reverberación estéreo pero no puede eliminar la reverberación mono;
 (234) DeEcho: Elimina efectos de retardo. El modo Agresivo elimina más a fondo que el modo Normal. DeReverb adicionalmente elimina la reverberación y puede eliminar la reverberación mono, pero no muy efectivamente para contenido de alta frecuencia fuertemente reverberado.
Notas de des-reverberación/des-retardo:
1. El tiempo de procesamiento para el modelo DeEcho-DeReverb es aproximadamente el doble que los otros dos modelos DeEcho.
2. El modelo MDX-Net-Dereverb es bastante lento.
3. La configuración más limpia recomendada es aplicar primero MDX-Net y luego DeEcho-Agresivo.", - "从模型中读取": "Leer del modelo", - "以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2": "Separe los números de identificación de la GPU con '-' al ingresarlos. Por ejemplo, '0-1-2' significa usar GPU 0, GPU 1 y GPU 2.", - "伴奏人声分离&去混响&去回声": "Separación de voz acompañante & eliminación de reverberación & eco", - "使用模型采样率": "Elija la frecuencia de muestreo del modelo", - "使用设备采样率": "Elija la frecuencia de muestreo del dispositivo", - "保存名": "Guardar nombre", - "保存的文件名, 默认空为和源文件同名": "Nombre del archivo que se guardará, el valor predeterminado es el mismo que el nombre del archivo de origen", - "保存的模型名不带后缀": "Nombre del modelo guardado sin extensión.", - "保存频率save_every_epoch": "Frecuencia de guardado (save_every_epoch)", - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果": "Proteger las consonantes claras y la respiración, prevenir artefactos como la distorsión de sonido electrónico, 0.5 no está activado, reducir aumentará la protección pero puede reducir el efecto del índice", - "信息": "Información", - "修改": "Modificar", - "停止音频转换": "Detener la conversión de audio", - "全流程结束！": "¡Todo el proceso ha terminado!", - "共振偏移": "Compensación resonante", - "刷新音色列表和索引路径": "Actualizar la lista de modelos e índice de rutas", - "加载模型": "Cargar modelo", - "加载预训练底模D路径": "Cargue la ruta del modelo D base pre-entrenada.", - "加载预训练底模G路径": "Cargue la ruta del modelo G base pre-entrenada.", - "单次推理": "Inferencia única", - "卸载音色省显存": "Descargue la voz para ahorrar memoria GPU", - "变调(整数, 半音数量, 升八度12降八度-12)": "Cambio de tono (entero, número de semitonos, subir una octava +12 o bajar una octava -12)", - "后处理重采样至最终采样率，0为不进行重采样": "Remuestreo posterior al proceso a la tasa de muestreo final, 0 significa no remuestrear", - "否": "No", - "启用相位声码器": "Habilitar vocoder de fase", - "响应阈值": "Umbral de respuesta", - "响度因子": "factor de sonoridad", - "处理数据": "Procesar datos", - "失败": "Falló", - "实际计算": "Valor realmente calculado", - "导出Onnx模型": "Exportar modelo Onnx", - "导出文件格式": "Formato de archivo de exportación", - "封装时间": "fecha de sellado", - "常见问题解答": "Preguntas frecuentes", - "常规设置": "Configuración general", - "开始音频转换": "Iniciar conversión de audio", - "待处理音频文件": "El archivo de audio a procesar", - "很遗憾您这没有能用的显卡来支持您训练": "Lamentablemente, no tiene una tarjeta gráfica adecuada para soportar su entrenamiento", - "性能设置": "Configuración de rendimiento", - "总训练轮数total_epoch": "Total de épocas de entrenamiento (total_epoch)", - "成功构建索引到": "Índice construido con éxito en", - "批量推理": "Inferencia por lotes", - "批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ": "Conversión por lotes, ingrese la carpeta que contiene los archivos de audio para convertir o cargue varios archivos de audio. El audio convertido se emitirá en la carpeta especificada (opción predeterminada).", - "指定输出主人声文件夹": "Especifique la carpeta de salida para la voz principal", - "指定输出文件夹": "Especificar carpeta de salida", - "指定输出非主人声文件夹": "Especifique la carpeta de salida para las voces no principales", - "推理时间(ms)": "Inferir tiempo (ms)", - "推理音色": "inferencia de voz", - "提取": "Extraer", - "提取音高和处理数据使用的CPU进程数": "Número de procesos de CPU utilizados para extraer el tono y procesar los datos", - "无": "Inexistente", - "是": "Sí", - "是否仅保存最新的ckpt文件以节省硬盘空间": "Guardar solo el archivo ckpt más reciente para ahorrar espacio en disco", - "是否在每次保存时间点将最终小模型保存至weights文件夹": "Guardar pequeño modelo final en la carpeta 'weights' en cada punto de guardado", - "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速": "Si almacenar en caché todos los conjuntos de entrenamiento en la memoria de la GPU. Los conjuntos de datos pequeños (menos de 10 minutos) se pueden almacenar en caché para acelerar el entrenamiento, pero el almacenamiento en caché de conjuntos de datos grandes puede causar errores de memoria en la GPU y no aumenta la velocidad de manera significativa.", - "显卡信息": "información de la GPU", - "有": "Existente", - "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.": "Este software es de código abierto bajo la licencia MIT, el autor no tiene ningún control sobre el software, y aquellos que usan el software y difunden los sonidos exportados por el software son los únicos responsables.
Si no está de acuerdo con esta cláusula , no puede utilizar ni citar ningún código ni archivo del paquete de software Consulte el directorio raíz Agreement-LICENSE.txt para obtener más información.", - "查看": "Ver", - "检索特征占比": "Proporción de función de búsqueda", - "模型": "Modelo", - "模型作者": "Autor del modelo", - "模型作者(可空)": "Autor del modelo (anulable)", - "模型信息": "Información del modelo", - "模型名": "Nombre del modelo", - "模型推理": "inferencia del modelo", - "模型是否带音高指导": "Si el modelo tiene guía de tono.", - "模型是否带音高指导(唱歌一定要, 语音可以不要)": "Si el modelo tiene guía de tono (necesaria para cantar, pero no para hablar)", - "模型是否带音高指导,1是0否": "Si el modelo tiene guía de tono, 1 para sí, 0 para no", - "模型版本型号": "Versión y modelo del modelo", - "模型路径": "Ruta del modelo", - "每张显卡的batch_size": "Tamaño del lote (batch_size) por tarjeta gráfica", - "淡入淡出长度": "Duración del fundido de entrada/salida", - "版本": "Versión", - "特征提取": "Extracción de características", - "特征检索库文件路径,为空则使用下拉的选择结果": "Ruta del archivo de la biblioteca de características, si está vacío, se utilizará el resultado de la selección desplegable", - "独占 WASAPI 设备": "Adquisición del dispositivo WASAPI", - "男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ": "Tecla +12 recomendada para conversión de voz de hombre a mujer, tecla -12 para conversión de voz de mujer a hombre. Si el rango de tono es demasiado amplio y causa distorsión, ajústelo usted mismo a un rango adecuado.", - "目标采样率": "Tasa de muestreo objetivo", - "相似度": "Semejanza", - "相似度(0到1)": "Similitud (de 0 a 1)", - "算法延迟(ms)": "Retrasos algorítmicos (ms)", - "自动检测index路径,下拉式选择(dropdown)": "Detección automática de la ruta del índice, selección desplegable (dropdown)", - "融合": "Fusión", - "要改的模型信息": "Información del modelo a modificar", - "要置入的模型信息": "Información del modelo a colocar.", - "计算": "Calcularlo", - "训练": "Entrenamiento", - "训练模型": "Entrenar Modelo", - "训练特征索引": "Índice de características", - "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": "Entrenamiento finalizado, puede ver el registro de entrenamiento en la consola o en el archivo train.log en la carpeta del experimento", - "设备类型": "Tipo de dispositivo", - "请指定说话人id": "ID del modelo", - "请选择index文件": "Seleccione el archivo .index", - "请选择pth文件": "Seleccione el archivo .pth", - "请选择说话人id": "Seleccione una identificación de altavoz", - "转换": "Conversión", - "输入实验名": "Ingrese el nombre del modelo", - "输入待处理音频文件夹路径": "Ingrese la ruta a la carpeta de audio que se procesará", - "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)": "Ingrese la ruta a la carpeta de audio que se procesará (simplemente cópiela desde la barra de direcciones del administrador de archivos)", - "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": "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", - "输入监听": "Monitor de voz de entrada", - "输入训练文件夹路径": "Introduzca la ruta de la carpeta de entrenamiento", - "输入设备": "Dispositivo de entrada", - "输入降噪": "Reducción de ruido de entrada", - "输出信息": "Información de salida", - "输出变声": "Salida de voz convertida", - "输出设备": "Dispositivo de salida", - "输出降噪": "Reducción de ruido de salida", - "输出音频(右下角三个点,点了可以下载)": "Salida de audio (haga clic en los tres puntos en la esquina inferior derecha para descargar)", - "选择.index文件": "Seleccione el archivo .index", - "选择.pth文件": "Seleccione el archivo .pth", - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU": "Seleccione el algoritmo de extracción de tono, use 'pm' para acelerar la entrada de canto, 'harvest' es bueno para los graves pero extremadamente lento, 'crepe' tiene buenos resultados pero consume GPU", - "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU": "Seleccione el algoritmo de extracción de tono: la canción de entrada se puede acelerar con pm, la voz de alta calidad pero CPU pobre se puede acelerar con dio, harvest es mejor pero más lento, rmvpe es el mejor y se come ligeramente la CPU/GPU", - "采样率": "Tasa de muestreo", - "采样长度": "Longitud de muestreo", - "重载设备列表": "Actualizar lista de dispositivos", - "链接索引到外部": "Vincular índice a carpeta externa", - "音调设置": "Ajuste de tono", - "音频设备": "Dispositivo de audio", - "音高引导(f0)": "Guía de tono (f0)", - "音高算法": "Algoritmo de tono", - "额外推理时长": "Tiempo de inferencia adicional" + "Unload model to save GPU memory": "Descargue la voz para ahorrar memoria GPU", + "Version": "Versión", + "View": "Ver", + "Vocals/Accompaniment Separation & Reverberation Removal": "Separación de voz acompañante & eliminación de reverberación & eco", + "Weight (w) for Model A": "Un peso modelo para el modelo A.", + "Whether the model has pitch guidance": "Si el modelo tiene guía de tono.", + "Whether the model has pitch guidance (1: yes, 0: no)": "Si el modelo tiene guía de tono, 1 para sí, 0 para no", + "Whether the model has pitch guidance (required for singing, optional for speech)": "Si el modelo tiene guía de tono (necesaria para cantar, pero no para hablar)", + "Yes": "Sí", + "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" } diff --git a/i18n/locale/fr_FR.json b/i18n/locale/fr_FR.json index 984f6cb..6f92f19 100644 --- a/i18n/locale/fr_FR.json +++ b/i18n/locale/fr_FR.json @@ -1,160 +1,157 @@ { - "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### Modifier les informations du modèle\n> Uniquement pris en charge pour les petits fichiers de modèle extraits du dossier 'weights'.", - "### 查看模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### Afficher les informations sur le modèle\n> Uniquement pour les petits fichiers de modèle extraits du dossier 'weights'.", - "### 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": "### 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.", - "### 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": "### Comparaison des modèles\n> Pour obtenir l'ID du modèle (long), veuillez consulter la section `Voir les informations du modèle` ci-dessous.\n\nPeut être utilisé pour comparer la similarité des inférences entre deux modèles.", - "### 模型融合\n可用于测试音色融合": "### Fusion de modèles\nPeut être utilisée pour tester la fusion de timbres.", - "### 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.": "### Étape 1. Remplissez la configuration expérimentale.\nLes données expérimentales sont stockées dans le dossier 'logs', avec chaque expérience ayant un dossier distinct. Entrez manuellement le chemin du nom de l'expérience, qui contient la configuration expérimentale, les journaux et les fichiers de modèle entraînés.", - "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.": "### Troisième étape : Commencer l'entraînement\nRemplissez les paramètres d'entraînement, commencez à entraîner le modèle et l'index.", - "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.": "### 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.", - "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).": "#### 2. 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).", - ">=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": "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.", - "A模型ID(长)": "ID du modèle A (long)", - "A模型权重": "Poids (w) pour le modèle A :", - "A模型路径": "Chemin d'accès au modèle A :", - "B模型ID(长)": "ID du modèle B (long)", - "B模型路径": "Chemin d'accès au modèle B :", - "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调": "Fichier de courbe F0 (facultatif). Une hauteur par ligne. Remplace la fréquence fondamentale par défaut et la modulation de la hauteur :", - "ID(短)": "ID (court)", - "ID(长)": "ID (long)", + "### 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.", + "### 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 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'.", + "#### 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 :", + "Algorithmic delays (ms)": "Délais algorithmiques (ms)", + "All processes have been completed!": "Toutes les étapes ont été terminées !", + "Audio device": "Périphérique audio", + "Auto-detect index path and select from the dropdown": "Détecter automatiquement le chemin d'accès à l'index et le sélectionner dans la liste déroulante :", + "Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt').": "Conversion en lot. Entrez le dossier contenant les fichiers audio à convertir ou téléchargez plusieurs fichiers audio. Les fichiers audio convertis seront enregistrés dans le dossier spécifié (par défaut : 'opt').", + "Batch inference": "Inférence par lots", + "Batch processing for vocal accompaniment separation using the UVR5 model.
Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).
The model is divided into three categories:
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.
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.
3. De-reverb and de-delay models (by FoxJoy):
  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;
 (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.
De-reverb/de-delay notes:
1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.
2. The MDX-Net-Dereverb model is quite slow.
3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive.": "Traitement en lot pour la séparation de la voix et de l'accompagnement vocal à l'aide du modèle UVR5.
Exemple d'un format de chemin de dossier valide : D:\\chemin\\vers\\dossier\\d'entrée (copiez-le depuis la barre d'adresse du gestionnaire de fichiers).
Le modèle est divisé en trois catégories :
1. Préserver la voix : Choisissez cette option pour l'audio sans harmonies. Elle préserve la voix mieux que HP5. Il comprend deux modèles intégrés : HP2 et HP3. HP3 peut légèrement laisser passer l'accompagnement mais préserve légèrement mieux la voix que HP2.
2. Préserver uniquement la voix principale : Choisissez cette option pour l'audio avec harmonies. Cela peut affaiblir la voix principale. Il comprend un modèle intégré : HP5.
3. Modèles de suppression de la réverbération et du délai (par FoxJoy) :
  (1) MDX-Net : Le meilleur choix pour la suppression de la réverbération stéréo, mais ne peut pas supprimer la réverbération mono.
  (234) DeEcho : Supprime les effets de délai. Le mode Aggressive supprime plus efficacement que le mode Normal. DeReverb supprime également la réverbération et peut supprimer la réverbération mono, mais pas très efficacement pour les contenus à haute fréquence fortement réverbérés.
Notes sur la suppression de la réverbération et du délai :
1. Le temps de traitement pour le modèle DeEcho-DeReverb est environ deux fois plus long que pour les autres deux modèles DeEcho.
2. Le modèle MDX-Net-Dereverb est assez lent.
3. La configuration la plus propre recommandée est d'appliquer d'abord MDX-Net, puis DeEcho-Aggressive.", + "Batch size per GPU": "Taille du batch par GPU :", + "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": "Mettre en cache tous les ensembles d'entrainement dans la mémoire GPU. Mettre en cache de petits ensembles de données (moins de 10 minutes) peut accélérer l'entrainement, mais mettre en cache de grands ensembles de données consommera beaucoup de mémoire GPU et peut ne pas apporter beaucoup d'amélioration de vitesse :", + "Calculate": "Calculez-le", + "Choose sample rate of the device": "Choisissez la fréquence d'échantillonnage de l'appareil", + "Choose sample rate of the model": "Choisissez la fréquence d'échantillonnage du modèle", + "Convert": "Convertir", + "Device type": "Type d'appareil", + "Enable phase vocoder": "Activer le vocodeur de phase", + "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1": "Configuration des numéros de carte RMVPE : séparez les index GPU par des tirets \"-\", par exemple, 0-0-1 pour utiliser 2 processus sur GPU0 et 1 processus sur GPU1.", + "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2": "Entrez le(s) index GPU séparé(s) par '-', par exemple, 0-1-2 pour utiliser les GPU 0, 1 et 2 :", + "Enter the experiment name": "Saisissez le nom de l'expérience :", + "Enter the path of the audio folder to be processed": "Entrez le chemin du dossier audio à traiter :", + "Enter the path of the audio folder to be processed (copy it from the address bar of the file manager)": "Entrez le chemin du dossier audio à traiter (copiez-le depuis la barre d'adresse du gestionnaire de fichiers) :", + "Enter the path of the training folder": "Indiquez le chemin d'accès au dossier d'entraînement :", + "Exist": "Existe", + "Export Onnx": "Exporter en ONNX", + "Export Onnx Model": "Exporter le modèle au format ONNX.", + "Export audio (click on the three dots in the lower right corner to download)": "Exporter l'audio (cliquer sur les trois points dans le coin inférieur droit pour télécharger)", + "Export file format": "Format de fichier d'exportation", + "Extra inference time": "Temps d'inférence supplémentaire", + "Extract": "Extraire", + "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation": "Fichier de courbe F0 (facultatif). Une hauteur par ligne. Remplace la fréquence fondamentale par défaut et la modulation de la hauteur :", + "FAQ (Frequently Asked Questions)": "FAQ (Foire Aux Questions)", + "Fade length": "Longueur de la transition", + "Fail": "Raté", + "Feature extraction": "Extraction des caractéristiques", + "Formant offset": "Décalage résonant", + "Fusion": "Fusion", + "GPU Information": "Informations sur la carte graphique (GPU)", + "General settings": "Paramètres généraux", + "Hidden": "Caché", + "ID of model A (long)": "ID du modèle A (long)", + "ID of model B (long)": "ID du modèle B (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", + "Information": "Information", + "Input device": "Dispositif d'entrée", + "Input noise reduction": "Réduction du bruit d'entrée", + "Input voice monitor": "Moniteur vocal d'entrée", + "Link index to outside folder": "Lier l'index au dossier extérieur", + "Load model": "Charger le modèle.", + "Load pre-trained base model D path": "Charger le chemin du modèle de base pré-entraîné D :", + "Load pre-trained base model G path": "Charger le chemin du modèle de base pré-entraîné G :", + "Loudness factor": "Facteur de volume sonore", + "Model": "Modèle", + "Model Author": "Auteur du modèle", + "Model Author (Nullable)": "Auteur du modèle (Nullable)", + "Model Inference": "Inférence du modèle", + "Model architecture version": "Version de l'architecture du modèle :", + "Model info": "Informations sur le modèle", + "Model information to be modified": "Informations sur le modèle à modifier :", + "Model information to be placed": "Informations sur le modèle à placer :", + "Model name": "Nom du modèle", + "Modify": "Modifier", + "Multiple audio files can also be imported. If a folder path exists, this input is ignored.": "Il est également possible d'importer plusieurs fichiers audio. Si un chemin de dossier existe, cette entrée est ignorée.", + "No": "Non", "None": "Aucun", - "Onnx导出": "Exporter en ONNX", - "Onnx输出路径": "Chemin d'exportation ONNX :", - "RVC模型路径": "Chemin du modèle RVC :", + "Not exist": "N'existe", + "Number of CPU processes used for harvest pitch algorithm": "Nombre de processus CPU utilisés pour l'algorithme de reconnaissance de la hauteur (pitch) dans le cadre de la récolte (harvest).", + "Number of CPU processes used for pitch extraction and data processing": "Nombre de processus CPU utilisés pour l'extraction de la hauteur et le traitement des données :", + "One-click training": "Entraînement en un clic", + "Onnx Export Path": "Chemin d'exportation ONNX :", + "Output converted voice": "Sortie voix convertie", + "Output device": "Dispositif de sortie", + "Output information": "Informations sur la sortie", + "Output noise reduction": "Réduction du bruit de sortie", + "Path to Model": "Le chemin vers le modèle :", + "Path to Model A": "Chemin d'accès au modèle A :", + "Path to Model B": "Chemin d'accès au modèle B :", + "Path to the feature index file. Leave blank to use the selected result from the dropdown": "Chemin d'accès au fichier d'index des caractéristiques. Laisser vide pour utiliser le résultat sélectionné dans la liste déroulante :", + "Performance settings": "Paramètres de performance", + "Pitch detection algorithm": "Algorithme de détection de la hauteur", + "Pitch guidance (f0)": "Guidage du pas (f0)", + "Pitch settings": "Réglages de la hauteur", + "Please choose the .index file": "Veuillez sélectionner le fichier d'index", + "Please choose the .pth file": "Veuillez sélectionner le fichier pth", + "Please specify the speaker/singer ID": "Veuillez spécifier l'ID de l'orateur ou du chanteur :", + "Process data": "Traitement des données", + "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": "Protéger les consonnes sourdes et les bruits de respiration pour éviter les artefacts tels que le déchirement dans la musique électronique. Réglez à 0,5 pour désactiver. Diminuez la valeur pour renforcer la protection, mais cela peut réduire la précision de l'indexation :", + "RVC Model Path": "Chemin du modèle RVC :", + "Read from model": "Lire à partir du modèle", + "Refresh voice list and index path": "Actualiser la liste des voix et le vers l'index.", + "Reload device list": "Recharger la liste des dispositifs", + "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling": "Rééchantillonner l'audio de sortie en post-traitement à la fréquence d'échantillonnage finale. Réglez sur 0 pour ne pas effectuer de rééchantillonnage :", + "Response threshold": "Seuil de réponse", + "Sample length": "Longueur de l'échantillon", + "Sampling rate": "Taux d'échantillonnage", + "Save a small final model to the 'weights' folder at each save point": "Enregistrer un petit modèle final dans le dossier 'weights' à chaque point de sauvegarde :", + "Save file name (default: same as the source file)": "Nom du fichier de sauvegarde (par défaut : identique au nom du fichier source) :", + "Save frequency (save_every_epoch)": "Fréquence de sauvegarde (save_every_epoch) :", + "Save name": "Nom de sauvegarde :", + "Save only the latest '.ckpt' file to save disk space": "Enregistrer uniquement le dernier fichier '.ckpt' pour économiser de l'espace disque :", + "Saved model name (without extension)": "Nom du modèle enregistré (sans extension) :", + "Sealing date": "Date de scellement", + "Search feature ratio (controls accent strength, too high has artifacting)": "Rapport de recherche de caractéristiques (contrôle l'intensité de l'accent, un rapport trop élevé provoque des artefacts) :", + "Select Speaker/Singer ID": "Sélectionner l'ID de l'orateur ou du chanteur :", + "Select the .index file": "Sélectionner le fichier .index", + "Select the .pth file": "Sélectionner le fichier .pth", + "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": "Sélectionnez l'algorithme d'extraction de la hauteur de ton (\"pm\" : extraction plus rapide mais parole de moindre qualité ; \"harvest\" : meilleure basse mais extrêmement lente ; \"crepe\" : meilleure qualité mais utilisation intensive du GPU), \"rmvpe\" : meilleure qualité et peu d'utilisation du GPU.", + "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": "Sélection de l'algorithme d'extraction de la hauteur : la chanson d'entrée peut être traitée plus rapidement par pm, avec une voix de haute qualité mais un CPU médiocre, par dio, harvest est meilleur mais plus lent, rmvpe est le meilleur, mais consomme légèrement le CPU/GPU.", + "Similarity": "Similarité", + "Similarity (from 0 to 1)": "Similarité (de 0 à 1)", + "Single inference": "Inférence unique", + "Specify output folder": "Spécifiez le dossier de sortie :", + "Specify the output folder for accompaniment": "Spécifiez le dossier de sortie pour l'accompagnement :", + "Specify the output folder for vocals": "Spécifiez le dossier de sortie pour les fichiers de voix :", + "Start audio conversion": "Démarrer la conversion audio.", + "Step 1: Processing data": "Étape 1 : Traitement des données en cours.", + "Step 3a: Model training started": "Étape 3a : L'entraînement du modèle a commencé.", + "Stop audio conversion": "Arrêter la conversion audio", + "Successfully built index into": "Index intégré avec succès dans", + "Takeover WASAPI device": "Reprise du périphérique WASAPI", + "Target sample rate": "Taux d'échantillonnage cible :", + "The audio file to be processed": "Le fichier audio à traiter", + "This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible.
If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory Agreement-LICENSE.txt for details.": "Ce logiciel est open source sous la licence MIT. L'auteur n'a aucun contrôle sur le logiciel. Les utilisateurs qui utilisent le logiciel et distribuent les sons exportés par le logiciel en sont entièrement responsables.
Si vous n'acceptez pas cette clause, vous ne pouvez pas utiliser ou faire référence à aucun code ni fichier contenu dans le package logiciel. Consultez le fichier Agreement-LICENSE.txt dans le répertoire racine pour plus de détails.", + "Total training epochs (total_epoch)": "Nombre total d'époques d'entraînement (total_epoch) :", + "Train": "Entraîner", + "Train feature index": "Entraîner l'index des caractéristiques", + "Train model": "Entraîner le modèle", + "Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder.": "Entraînement terminé. Vous pouvez consulter les rapports d'entraînement dans la console ou dans le fichier 'train.log' situé dans le dossier de l'expérience.", + "Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)": "Transposer (entier, nombre de demi-tons, monter d'une octave : 12, descendre d'une octave : -12) :", + "Unfortunately, there is no compatible GPU available to support your training.": "Malheureusement, il n'y a pas de GPU compatible disponible pour prendre en charge votre entrainement.", "Unknown": "Inconnu", - "ckpt处理": "Traitement des fichiers .ckpt", - "harvest进程数": "Nombre de processus CPU utilisés pour l'algorithme de reconnaissance de la hauteur (pitch) dans le cadre de la récolte (harvest).", - "index文件路径不可包含中文": "Le chemin du fichier d'index ne doit pas contenir de caractères chinois.", - "pth文件路径不可包含中文": "Le chemin du fichier .pth ne doit pas contenir de caractères chinois.", - "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程": "Configuration des numéros de carte RMVPE : séparez les index GPU par des tirets \"-\", par exemple, 0-0-1 pour utiliser 2 processus sur GPU0 et 1 processus sur GPU1.", - "step1:正在处理数据": "Étape 1 : Traitement des données en cours.", - "step2:正在提取音高&正在提取特征": "Étape 2 : Extraction de la hauteur et extraction des caractéristiques en cours.", - "step3a:正在训练模型": "Étape 3a : L'entraînement du modèle a commencé.", - "一键训练": "Entraînement en un clic", - "不显示": "Caché", - "也可批量输入音频文件, 二选一, 优先读文件夹": "Il est également possible d'importer plusieurs fichiers audio. Si un chemin de dossier existe, cette entrée est ignorée.", - "人声伴奏分离批量处理， 使用UVR5模型。
合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
模型分为三类：
1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
3、去混响、去延迟模型（by FoxJoy）：
  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
 (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
去混响/去延迟，附：
1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
2、MDX-Net-Dereverb模型挺慢的；
3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。": "Traitement en lot pour la séparation de la voix et de l'accompagnement vocal à l'aide du modèle UVR5.
Exemple d'un format de chemin de dossier valide : D:\\chemin\\vers\\dossier\\d'entrée (copiez-le depuis la barre d'adresse du gestionnaire de fichiers).
Le modèle est divisé en trois catégories :
1. Préserver la voix : Choisissez cette option pour l'audio sans harmonies. Elle préserve la voix mieux que HP5. Il comprend deux modèles intégrés : HP2 et HP3. HP3 peut légèrement laisser passer l'accompagnement mais préserve légèrement mieux la voix que HP2.
2. Préserver uniquement la voix principale : Choisissez cette option pour l'audio avec harmonies. Cela peut affaiblir la voix principale. Il comprend un modèle intégré : HP5.
3. Modèles de suppression de la réverbération et du délai (par FoxJoy) :
  (1) MDX-Net : Le meilleur choix pour la suppression de la réverbération stéréo, mais ne peut pas supprimer la réverbération mono.
  (234) DeEcho : Supprime les effets de délai. Le mode Aggressive supprime plus efficacement que le mode Normal. DeReverb supprime également la réverbération et peut supprimer la réverbération mono, mais pas très efficacement pour les contenus à haute fréquence fortement réverbérés.
Notes sur la suppression de la réverbération et du délai :
1. Le temps de traitement pour le modèle DeEcho-DeReverb est environ deux fois plus long que pour les autres deux modèles DeEcho.
2. Le modèle MDX-Net-Dereverb est assez lent.
3. La configuration la plus propre recommandée est d'appliquer d'abord MDX-Net, puis DeEcho-Aggressive.", - "从模型中读取": "Lire à partir du modèle", - "以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2": "Entrez le(s) index GPU séparé(s) par '-', par exemple, 0-1-2 pour utiliser les GPU 0, 1 et 2 :", - "伴奏人声分离&去混响&去回声": "Séparation des voix/accompagnement et suppression de la réverbération", - "使用模型采样率": "Choisissez la fréquence d'échantillonnage du modèle", - "使用设备采样率": "Choisissez la fréquence d'échantillonnage de l'appareil", - "保存名": "Nom de sauvegarde :", - "保存的文件名, 默认空为和源文件同名": "Nom du fichier de sauvegarde (par défaut : identique au nom du fichier source) :", - "保存的模型名不带后缀": "Nom du modèle enregistré (sans extension) :", - "保存频率save_every_epoch": "Fréquence de sauvegarde (save_every_epoch) :", - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果": "Protéger les consonnes sourdes et les bruits de respiration pour éviter les artefacts tels que le déchirement dans la musique électronique. Réglez à 0,5 pour désactiver. Diminuez la valeur pour renforcer la protection, mais cela peut réduire la précision de l'indexation :", - "信息": "Information", - "修改": "Modifier", - "停止音频转换": "Arrêter la conversion audio", - "全流程结束！": "Toutes les étapes ont été terminées !", - "共振偏移": "Décalage résonant", - "刷新音色列表和索引路径": "Actualiser la liste des voix et le vers l'index.", - "加载模型": "Charger le modèle.", - "加载预训练底模D路径": "Charger le chemin du modèle de base pré-entraîné D :", - "加载预训练底模G路径": "Charger le chemin du modèle de base pré-entraîné G :", - "单次推理": "Inférence unique", - "卸载音色省显存": "Décharger la voix pour économiser la mémoire GPU.", - "变调(整数, 半音数量, 升八度12降八度-12)": "Transposer (entier, nombre de demi-tons, monter d'une octave : 12, descendre d'une octave : -12) :", - "后处理重采样至最终采样率，0为不进行重采样": "Rééchantillonner l'audio de sortie en post-traitement à la fréquence d'échantillonnage finale. Réglez sur 0 pour ne pas effectuer de rééchantillonnage :", - "否": "Non", - "启用相位声码器": "Activer le vocodeur de phase", - "响应阈值": "Seuil de réponse", - "响度因子": "Facteur de volume sonore", - "处理数据": "Traitement des données", - "失败": "Raté", - "实际计算": "Effectivement calculé", - "导出Onnx模型": "Exporter le modèle au format ONNX.", - "导出文件格式": "Format de fichier d'exportation", - "封装时间": "Date de scellement", - "常见问题解答": "FAQ (Foire Aux Questions)", - "常规设置": "Paramètres généraux", - "开始音频转换": "Démarrer la conversion audio.", - "待处理音频文件": "Le fichier audio à traiter", - "很遗憾您这没有能用的显卡来支持您训练": "Malheureusement, il n'y a pas de GPU compatible disponible pour prendre en charge votre entrainement.", - "性能设置": "Paramètres de performance", - "总训练轮数total_epoch": "Nombre total d'époques d'entraînement (total_epoch) :", - "成功构建索引到": "Index intégré avec succès dans", - "批量推理": "Inférence par lots", - "批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ": "Conversion en lot. Entrez le dossier contenant les fichiers audio à convertir ou téléchargez plusieurs fichiers audio. Les fichiers audio convertis seront enregistrés dans le dossier spécifié (par défaut : 'opt').", - "指定输出主人声文件夹": "Spécifiez le dossier de sortie pour les fichiers de voix :", - "指定输出文件夹": "Spécifiez le dossier de sortie :", - "指定输出非主人声文件夹": "Spécifiez le dossier de sortie pour l'accompagnement :", - "推理时间(ms)": "Temps d'inférence (ms)", - "推理音色": "Voix pour l'inférence", - "提取": "Extraire", - "提取音高和处理数据使用的CPU进程数": "Nombre de processus CPU utilisés pour l'extraction de la hauteur et le traitement des données :", - "无": "N'existe", - "是": "Oui", - "是否仅保存最新的ckpt文件以节省硬盘空间": "Enregistrer uniquement le dernier fichier '.ckpt' pour économiser de l'espace disque :", - "是否在每次保存时间点将最终小模型保存至weights文件夹": "Enregistrer un petit modèle final dans le dossier 'weights' à chaque point de sauvegarde :", - "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速": "Mettre en cache tous les ensembles d'entrainement dans la mémoire GPU. Mettre en cache de petits ensembles de données (moins de 10 minutes) peut accélérer l'entrainement, mais mettre en cache de grands ensembles de données consommera beaucoup de mémoire GPU et peut ne pas apporter beaucoup d'amélioration de vitesse :", - "显卡信息": "Informations sur la carte graphique (GPU)", - "有": "Existe", - "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.": "Ce logiciel est open source sous la licence MIT. L'auteur n'a aucun contrôle sur le logiciel. Les utilisateurs qui utilisent le logiciel et distribuent les sons exportés par le logiciel en sont entièrement responsables.
Si vous n'acceptez pas cette clause, vous ne pouvez pas utiliser ou faire référence à aucun code ni fichier contenu dans le package logiciel. Consultez le fichier Agreement-LICENSE.txt dans le répertoire racine pour plus de détails.", - "查看": "Voir", - "检索特征占比": "Rapport de recherche de caractéristiques (contrôle l'intensité de l'accent, un rapport trop élevé provoque des artefacts) :", - "模型": "Modèle", - "模型作者": "Auteur du modèle", - "模型作者(可空)": "Auteur du modèle (Nullable)", - "模型信息": "Informations sur le modèle", - "模型名": "Nom du modèle", - "模型推理": "Inférence du modèle", - "模型是否带音高指导": "Indique si le modèle dispose d'un guidage en hauteur :", - "模型是否带音高指导(唱歌一定要, 语音可以不要)": "Indique si le modèle dispose d'un système de guidage de la hauteur (obligatoire pour le chant, facultatif pour la parole) :", - "模型是否带音高指导,1是0否": "Le modèle dispose-t-il d'un guide de hauteur (1 : oui, 0 : non) ?", - "模型版本型号": "Version de l'architecture du modèle :", - "模型路径": "Le chemin vers le modèle :", - "每张显卡的batch_size": "Taille du batch par GPU :", - "淡入淡出长度": "Longueur de la transition", - "版本": "Version", - "特征提取": "Extraction des caractéristiques", - "特征检索库文件路径,为空则使用下拉的选择结果": "Chemin d'accès au fichier d'index des caractéristiques. Laisser vide pour utiliser le résultat sélectionné dans la liste déroulante :", - "独占 WASAPI 设备": "Reprise du périphérique WASAPI", - "男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ": "Il est recommandé d'utiliser la clé +12 pour la conversion homme-femme et la clé -12 pour la conversion femme-homme. Si la plage sonore est trop large et que la voix est déformée, vous pouvez également l'ajuster vous-même à la plage appropriée.", - "目标采样率": "Taux d'échantillonnage cible :", - "相似度": "Similarité", - "相似度(0到1)": "Similarité (de 0 à 1)", - "算法延迟(ms)": "Délais algorithmiques (ms)", - "自动检测index路径,下拉式选择(dropdown)": "Détecter automatiquement le chemin d'accès à l'index et le sélectionner dans la liste déroulante :", - "融合": "Fusion", - "要改的模型信息": "Informations sur le modèle à modifier :", - "要置入的模型信息": "Informations sur le modèle à placer :", - "计算": "Calculez-le", - "训练": "Entraîner", - "训练模型": "Entraîner le modèle", - "训练特征索引": "Entraîner l'index des caractéristiques", - "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": "Entraînement terminé. Vous pouvez consulter les rapports d'entraînement dans la console ou dans le fichier 'train.log' situé dans le dossier de l'expérience.", - "设备类型": "Type d'appareil", - "请指定说话人id": "Veuillez spécifier l'ID de l'orateur ou du chanteur :", - "请选择index文件": "Veuillez sélectionner le fichier d'index", - "请选择pth文件": "Veuillez sélectionner le fichier pth", - "请选择说话人id": "Sélectionner l'ID de l'orateur ou du chanteur :", - "转换": "Convertir", - "输入实验名": "Saisissez le nom de l'expérience :", - "输入待处理音频文件夹路径": "Entrez le chemin du dossier audio à traiter :", - "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)": "Entrez le chemin du dossier audio à traiter (copiez-le depuis la barre d'adresse du gestionnaire de fichiers) :", - "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": "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 :", - "输入监听": "Moniteur vocal d'entrée", - "输入训练文件夹路径": "Indiquez le chemin d'accès au dossier d'entraînement :", - "输入设备": "Dispositif d'entrée", - "输入降噪": "Réduction du bruit d'entrée", - "输出信息": "Informations sur la sortie", - "输出变声": "Sortie voix convertie", - "输出设备": "Dispositif de sortie", - "输出降噪": "Réduction du bruit de sortie", - "输出音频(右下角三个点,点了可以下载)": "Exporter l'audio (cliquer sur les trois points dans le coin inférieur droit pour télécharger)", - "选择.index文件": "Sélectionner le fichier .index", - "选择.pth文件": "Sélectionner le fichier .pth", - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU": "Sélectionnez l'algorithme d'extraction de la hauteur de ton (\"pm\" : extraction plus rapide mais parole de moindre qualité ; \"harvest\" : meilleure basse mais extrêmement lente ; \"crepe\" : meilleure qualité mais utilisation intensive du GPU), \"rmvpe\" : meilleure qualité et peu d'utilisation du GPU.", - "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU": "Sélection de l'algorithme d'extraction de la hauteur : la chanson d'entrée peut être traitée plus rapidement par pm, avec une voix de haute qualité mais un CPU médiocre, par dio, harvest est meilleur mais plus lent, rmvpe est le meilleur, mais consomme légèrement le CPU/GPU.", - "采样率": "Taux d'échantillonnage", - "采样长度": "Longueur de l'échantillon", - "重载设备列表": "Recharger la liste des dispositifs", - "链接索引到外部": "Lier l'index au dossier extérieur", - "音调设置": "Réglages de la hauteur", - "音频设备": "Périphérique audio", - "音高引导(f0)": "Guidage du pas (f0)", - "音高算法": "Algorithme de détection de la hauteur", - "额外推理时长": "Temps d'inférence supplémentaire" + "Unload model to save GPU memory": "Décharger la voix pour économiser la mémoire GPU.", + "Version": "Version", + "View": "Voir", + "Vocals/Accompaniment Separation & Reverberation Removal": "Séparation des voix/accompagnement et suppression de la réverbération", + "Weight (w) for Model A": "Poids (w) pour le modèle A :", + "Whether the model has pitch guidance": "Indique si le modèle dispose d'un guidage en hauteur :", + "Whether the model has pitch guidance (1: yes, 0: no)": "Le modèle dispose-t-il d'un guide de hauteur (1 : oui, 0 : non) ?", + "Whether the model has pitch guidance (required for singing, optional for speech)": "Indique si le modèle dispose d'un système de guidage de la hauteur (obligatoire pour le chant, facultatif pour la parole) :", + "Yes": "Oui", + "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." } diff --git a/i18n/locale/it_IT.json b/i18n/locale/it_IT.json index 70e8070..fb477e6 100644 --- a/i18n/locale/it_IT.json +++ b/i18n/locale/it_IT.json @@ -1,160 +1,157 @@ { - "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### Modifica le informazioni sul modello\n> Supportato solo per i file di modello di piccole dimensioni estratti dalla cartella 'weights'.", - "### 查看模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### Visualizza le informazioni sul modello\n> Supportato solo per file di modello piccoli estratti dalla cartella 'weights'.", - "### 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": "### Estrazione del modello\n> Inserire il percorso del modello di file di grandi dimensioni nella cartella \"logs\".", - "### 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", - "### 模型融合\n可用于测试音色融合": "### Model fusion\nPuò essere utilizzato per testare la fusione timbrica.", - "### 第一步 填写实验配置\n实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件.": "### 第一步 填写实验配置\n实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件.", - "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.": "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.", - "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.": "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.", - "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).": "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).", - ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音": "Se >=3: applica il filtro mediano ai risultati del pitch raccolto. ", - "A模型ID(长)": "A模型ID(长)", - "A模型权重": "Peso (w) per il modello A:", - "A模型路径": "Percorso per il modello A:", - "B模型ID(长)": "B模型ID(长)", - "B模型路径": "Percorso per il modello B:", - "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调": "File curva F0 (opzionale). ", - "ID(短)": "ID(短)", + "### 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.", + "### 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 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'.", + "#### 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. ", + "Algorithmic delays (ms)": "算法延迟(ms)", + "All processes have been completed!": "Tutti i processi sono stati completati!", + "Audio device": "Dispositivo audio", + "Auto-detect index path and select from the dropdown": "Rileva automaticamente il percorso dell'indice e seleziona dal menu a tendina:", + "Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt').": "Conversione massiva. Inserisci il percorso della cartella che contiene i file da convertire o carica più file audio. I file convertiti finiranno nella cartella specificata. (default: opt) ", + "Batch inference": "批量推理", + "Batch processing for vocal accompaniment separation using the UVR5 model.
Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).
The model is divided into three categories:
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.
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.
3. De-reverb and de-delay models (by FoxJoy):
  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;
 (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.
De-reverb/de-delay notes:
1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.
2. The MDX-Net-Dereverb model is quite slow.
3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive.": "Elaborazione batch per la separazione dell'accompagnamento vocale utilizzando il modello UVR5.
Esempio di un formato di percorso di cartella valido: D:\\path\\to\\input\\folder (copialo dalla barra degli indirizzi del file manager).
Il modello è suddiviso in tre categorie:
1. Conserva la voce: scegli questa opzione per l'audio senza armonie.
2. Mantieni solo la voce principale: scegli questa opzione per l'audio con armonie.
3. Modelli di de-riverbero e de-delay (di FoxJoy):
  (1) MDX-Net: la scelta migliore per la rimozione del riverbero stereo ma non può rimuovere il riverbero mono;

Note di de-riverbero/de-delay:
1. Il tempo di elaborazione per il modello DeEcho-DeReverb è circa il doppio rispetto agli altri due modelli DeEcho.
2. Il modello MDX-Net-Dereverb è piuttosto lento.
3. La configurazione più pulita consigliata consiste nell'applicare prima MDX-Net e poi DeEcho-Aggressive.", + "Batch size per GPU": "Dimensione batch per GPU:", + "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": "Memorizza nella cache tutti i set di addestramento nella memoria della GPU. ", + "Calculate": "计算", + "Choose sample rate of the device": "使用设备采样率", + "Choose sample rate of the model": "使用模型采样率", + "Convert": "Convertire", + "Device type": "设备类型", + "Enable phase vocoder": "启用相位声码器", + "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1": "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程", + "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2": "Inserisci gli indici GPU separati da '-', ad esempio 0-1-2 per utilizzare GPU 0, 1 e 2:", + "Enter the experiment name": "Inserisci il nome dell'esperimento:", + "Enter the path of the audio folder to be processed": "Immettere il percorso della cartella audio da elaborare:", + "Enter the path of the audio folder to be processed (copy it from the address bar of the file manager)": "Immettere il percorso della cartella audio da elaborare (copiarlo dalla barra degli indirizzi del file manager):", + "Enter the path of the training folder": "Inserisci il percorso della cartella di addestramento:", + "Exist": "有", + "Export Onnx": "Esporta Onnx", + "Export Onnx Model": "Esporta modello Onnx", + "Export audio (click on the three dots in the lower right corner to download)": "Esporta audio (clicca sui tre puntini in basso a destra per scaricarlo)", + "Export file format": "Formato file di esportazione", + "Extra inference time": "Tempo di inferenza extra", + "Extract": "Estrai", + "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation": "File curva F0 (opzionale). ", + "FAQ (Frequently Asked Questions)": "FAQ (Domande frequenti)", + "Fade length": "Lunghezza dissolvenza", + "Fail": "失败", + "Feature extraction": "Estrazione delle caratteristiche", + "Formant offset": "共振偏移", + "Fusion": "Fusione", + "GPU Information": "Informazioni GPU", + "General settings": "Impostazioni generali", + "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(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:", + "Information": "信息", + "Input device": "Dispositivo di input", + "Input noise reduction": "Riduzione del rumore in ingresso", + "Input voice monitor": "输入监听", + "Link index to outside folder": "链接索引到外部", + "Load model": "Carica modello", + "Load pre-trained base model D path": "Carica il percorso D del modello base pre-addestrato:", + "Load pre-trained base model G path": "Carica il percorso G del modello base pre-addestrato:", + "Loudness factor": "fattore di sonorità", + "Model": "Modello", + "Model Author": "模型作者", + "Model Author (Nullable)": "模型作者(可空)", + "Model Inference": "Inferenza del modello", + "Model architecture version": "Versione dell'architettura del modello:", + "Model info": "模型信息", + "Model information to be modified": "Informazioni sul modello da modificare:", + "Model information to be placed": "Informazioni sul modello da posizionare:", + "Model name": "模型名", + "Modify": "Modificare", + "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": "NO", "None": "None", - "Onnx导出": "Esporta Onnx", - "Onnx输出路径": "Percorso di esportazione Onnx:", - "RVC模型路径": "Percorso modello RVC:", + "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": "Numero di processi CPU utilizzati per l'estrazione del tono e l'elaborazione dei dati:", + "One-click training": "Addestramento con un clic", + "Onnx Export Path": "Percorso di esportazione Onnx:", + "Output converted voice": "输出变声", + "Output device": "Dispositivo di uscita", + "Output information": "Informazioni sull'uscita", + "Output noise reduction": "Riduzione del rumore in uscita", + "Path to Model": "Percorso al modello:", + "Path to Model A": "Percorso per il modello A:", + "Path to Model B": "Percorso per il modello B:", + "Path to the feature index file. Leave blank to use the selected result from the dropdown": "Percorso del file di indice delle caratteristiche. ", + "Performance settings": "Impostazioni delle prestazioni", + "Pitch detection algorithm": "音高算法", + "Pitch guidance (f0)": "音高引导(f0)", + "Pitch settings": "Impostazioni del tono", + "Please choose the .index file": "请选择index文件", + "Please choose the .pth file": "请选择pth 文件", + "Please specify the speaker/singer ID": "Si prega di specificare l'ID del locutore/cantante:", + "Process data": "Processa dati", + "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": "Proteggi le consonanti senza voce e i suoni del respiro per evitare artefatti come il tearing nella musica elettronica. ", + "RVC Model Path": "Percorso modello RVC:", + "Read from model": "从模型中读取", + "Refresh voice list and index path": "Aggiorna l'elenco delle voci e il percorso dell'indice", + "Reload device list": "Ricaricare l'elenco dei dispositivi", + "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling": "Ricampiona l'audio di output in post-elaborazione alla frequenza di campionamento finale. ", + "Response threshold": "Soglia di risposta", + "Sample length": "Lunghezza del campione", + "Sampling rate": "采样率", + "Save a small final model to the 'weights' folder at each save point": "Salva un piccolo modello finale nella cartella \"weights\" in ogni punto di salvataggio:", + "Save file name (default: same as the source file)": "Salva il nome del file (predefinito: uguale al file di origine):", + "Save frequency (save_every_epoch)": "Frequenza di salvataggio (save_every_epoch):", + "Save name": "Salva nome:", + "Save only the latest '.ckpt' file to save disk space": "Salva solo l'ultimo file '.ckpt' per risparmiare spazio su disco:", + "Saved model name (without extension)": "Nome del modello salvato (senza estensione):", + "Sealing date": "封装时间", + "Search feature ratio (controls accent strength, too high has artifacting)": "Rapporto funzionalità di ricerca (controlla la forza dell'accento, troppo alto ha artefatti):", + "Select Speaker/Singer ID": "Seleziona ID locutore/cantante:", + "Select the .index file": "Seleziona il file .index", + "Select the .pth file": "Seleziona il file .pth", + "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": "Seleziona l'algoritmo di estrazione del tono (\"pm\": estrazione più veloce ma risultato di qualità inferiore; \"harvest\": bassi migliori ma estremamente lenti; \"crepe\": qualità migliore ma utilizzo intensivo della GPU):", + "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": "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU", + "Similarity": "相似度", + "Similarity (from 0 to 1)": "相似度(0到1)", + "Single inference": "单次推理", + "Specify output folder": "Specifica la cartella di output:", + "Specify the output folder for accompaniment": "Specificare la cartella di output per l'accompagnamento:", + "Specify the output folder for vocals": "Specifica la cartella di output per le voci:", + "Start audio conversion": "Avvia la conversione audio", + "Step 1: Processing data": "Passaggio 1: elaborazione dei dati", + "Step 3a: Model training started": "Passaggio 3a: è iniziato l'addestramento del modello", + "Stop audio conversion": "Arresta la conversione audio", + "Successfully built index into": "成功构建索引到", + "Takeover WASAPI device": "独占 WASAPI 设备", + "Target sample rate": "Frequenza di campionamento target:", + "The audio file to be processed": "待处理音频文件", + "This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible.
If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory Agreement-LICENSE.txt for details.": "Questo software è open source con licenza MIT.
Se non si accetta questa clausola, non è possibile utilizzare o fare riferimento a codici e file all'interno del pacchetto software. Contratto-LICENZA.txt per dettagli.", + "Total training epochs (total_epoch)": "Epoch totali di addestramento (total_epoch):", + "Train": "Addestramento", + "Train feature index": "Addestra indice delle caratteristiche", + "Train model": "Addestra modello", + "Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder.": "Addestramento completato. ", + "Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)": "Trasposizione (numero intero, numero di semitoni, alza di un'ottava: 12, abbassa di un'ottava: -12):", + "Unfortunately, there is no compatible GPU available to support your training.": "Sfortunatamente, non è disponibile alcuna GPU compatibile per supportare l'addestramento.", "Unknown": "Unknown", - "ckpt处理": "Elaborazione ckpt", - "harvest进程数": "harvest进程数", - "index文件路径不可包含中文": "index文件路径不可包含中文", - "pth文件路径不可包含中文": "pth è un'app per il futuro", - "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程": "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程", - "step1:正在处理数据": "Passaggio 1: elaborazione dei dati", - "step2:正在提取音高&正在提取特征": "step2:正在提取音高&正在提取特征", - "step3a:正在训练模型": "Passaggio 3a: è iniziato l'addestramento del modello", - "一键训练": "Addestramento con un clic", - "不显示": "不显示", - "也可批量输入音频文件, 二选一, 优先读文件夹": "也可批量输入音频文件, 二选一, 优先读文件夹", - "人声伴奏分离批量处理， 使用UVR5模型。
合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
模型分为三类：
1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
3、去混响、去延迟模型（by FoxJoy）：
  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
 (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
去混响/去延迟，附：
1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
2、MDX-Net-Dereverb模型挺慢的；
3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。": "Elaborazione batch per la separazione dell'accompagnamento vocale utilizzando il modello UVR5.
Esempio di un formato di percorso di cartella valido: D:\\path\\to\\input\\folder (copialo dalla barra degli indirizzi del file manager).
Il modello è suddiviso in tre categorie:
1. Conserva la voce: scegli questa opzione per l'audio senza armonie.
2. Mantieni solo la voce principale: scegli questa opzione per l'audio con armonie.
3. Modelli di de-riverbero e de-delay (di FoxJoy):
  (1) MDX-Net: la scelta migliore per la rimozione del riverbero stereo ma non può rimuovere il riverbero mono;

Note di de-riverbero/de-delay:
1. Il tempo di elaborazione per il modello DeEcho-DeReverb è circa il doppio rispetto agli altri due modelli DeEcho.
2. Il modello MDX-Net-Dereverb è piuttosto lento.
3. La configurazione più pulita consigliata consiste nell'applicare prima MDX-Net e poi DeEcho-Aggressive.", - "从模型中读取": "从模型中读取", - "以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2": "Inserisci gli indici GPU separati da '-', ad esempio 0-1-2 per utilizzare GPU 0, 1 e 2:", - "伴奏人声分离&去混响&去回声": "Separazione voce/accompagnamento", - "使用模型采样率": "使用模型采样率", - "使用设备采样率": "使用设备采样率", - "保存名": "Salva nome:", - "保存的文件名, 默认空为和源文件同名": "Salva il nome del file (predefinito: uguale al file di origine):", - "保存的模型名不带后缀": "Nome del modello salvato (senza estensione):", - "保存频率save_every_epoch": "Frequenza di salvataggio (save_every_epoch):", - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果": "Proteggi le consonanti senza voce e i suoni del respiro per evitare artefatti come il tearing nella musica elettronica. ", - "信息": "信息", - "修改": "Modificare", - "停止音频转换": "Arresta la conversione audio", - "全流程结束！": "Tutti i processi sono stati completati!", - "共振偏移": "共振偏移", - "刷新音色列表和索引路径": "Aggiorna l'elenco delle voci e il percorso dell'indice", - "加载模型": "Carica modello", - "加载预训练底模D路径": "Carica il percorso D del modello base pre-addestrato:", - "加载预训练底模G路径": "Carica il percorso G del modello base pre-addestrato:", - "单次推理": "单次推理", - "卸载音色省显存": "Scarica la voce per risparmiare memoria della GPU:", - "变调(整数, 半音数量, 升八度12降八度-12)": "Trasposizione (numero intero, numero di semitoni, alza di un'ottava: 12, abbassa di un'ottava: -12):", - "后处理重采样至最终采样率，0为不进行重采样": "Ricampiona l'audio di output in post-elaborazione alla frequenza di campionamento finale. ", - "否": "NO", - "启用相位声码器": "启用相位声码器", - "响应阈值": "Soglia di risposta", - "响度因子": "fattore di sonorità", - "处理数据": "Processa dati", - "失败": "失败", - "实际计算": "实际计算", - "导出Onnx模型": "Esporta modello Onnx", - "导出文件格式": "Formato file di esportazione", - "封装时间": "封装时间", - "常见问题解答": "FAQ (Domande frequenti)", - "常规设置": "Impostazioni generali", - "开始音频转换": "Avvia la conversione audio", - "待处理音频文件": "待处理音频文件", - "很遗憾您这没有能用的显卡来支持您训练": "Sfortunatamente, non è disponibile alcuna GPU compatibile per supportare l'addestramento.", - "性能设置": "Impostazioni delle prestazioni", - "总训练轮数total_epoch": "Epoch totali di addestramento (total_epoch):", - "成功构建索引到": "成功构建索引到", - "批量推理": "批量推理", - "批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ": "Conversione massiva. Inserisci il percorso della cartella che contiene i file da convertire o carica più file audio. I file convertiti finiranno nella cartella specificata. (default: opt) ", - "指定输出主人声文件夹": "Specifica la cartella di output per le voci:", - "指定输出文件夹": "Specifica la cartella di output:", - "指定输出非主人声文件夹": "Specificare la cartella di output per l'accompagnamento:", - "推理时间(ms)": "Tempo di inferenza (ms)", - "推理音色": "Voce di inferenza:", - "提取": "Estrai", - "提取音高和处理数据使用的CPU进程数": "Numero di processi CPU utilizzati per l'estrazione del tono e l'elaborazione dei dati:", - "无": "无", - "是": "SÌ", - "是否仅保存最新的ckpt文件以节省硬盘空间": "Salva solo l'ultimo file '.ckpt' per risparmiare spazio su disco:", - "是否在每次保存时间点将最终小模型保存至weights文件夹": "Salva un piccolo modello finale nella cartella \"weights\" in ogni punto di salvataggio:", - "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速": "Memorizza nella cache tutti i set di addestramento nella memoria della GPU. ", - "显卡信息": "Informazioni GPU", - "有": "有", - "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.": "Questo software è open source con licenza MIT.
Se non si accetta questa clausola, non è possibile utilizzare o fare riferimento a codici e file all'interno del pacchetto software. Contratto-LICENZA.txt per dettagli.", - "查看": "Visualizzazione", - "检索特征占比": "Rapporto funzionalità di ricerca (controlla la forza dell'accento, troppo alto ha artefatti):", - "模型": "Modello", - "模型作者": "模型作者", - "模型作者(可空)": "模型作者(可空)", - "模型信息": "模型信息", - "模型名": "模型名", - "模型推理": "Inferenza del modello", - "模型是否带音高指导": "Se il modello ha una guida del tono:", - "模型是否带音高指导(唱歌一定要, 语音可以不要)": "Se il modello ha una guida del tono (necessario per il canto, facoltativo per il parlato):", - "模型是否带音高指导,1是0否": "Se il modello ha una guida del tono (1: sì, 0: no):", - "模型版本型号": "Versione dell'architettura del modello:", - "模型路径": "Percorso al modello:", - "每张显卡的batch_size": "Dimensione batch per GPU:", - "淡入淡出长度": "Lunghezza dissolvenza", - "版本": "Versione", - "特征提取": "Estrazione delle caratteristiche", - "特征检索库文件路径,为空则使用下拉的选择结果": "Percorso del file di indice delle caratteristiche. ", - "独占 WASAPI 设备": "独占 WASAPI 设备", - "男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ": "Tonalità +12 consigliata per la conversione da maschio a femmina e tonalità -12 per la conversione da femmina a maschio. ", - "目标采样率": "Frequenza di campionamento target:", - "相似度": "相似度", - "相似度(0到1)": "相似度(0到1)", - "算法延迟(ms)": "算法延迟(ms)", - "自动检测index路径,下拉式选择(dropdown)": "Rileva automaticamente il percorso dell'indice e seleziona dal menu a tendina:", - "融合": "Fusione", - "要改的模型信息": "Informazioni sul modello da modificare:", - "要置入的模型信息": "Informazioni sul modello da posizionare:", - "计算": "计算", - "训练": "Addestramento", - "训练模型": "Addestra modello", - "训练特征索引": "Addestra indice delle caratteristiche", - "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": "Addestramento completato. ", - "设备类型": "设备类型", - "请指定说话人id": "Si prega di specificare l'ID del locutore/cantante:", - "请选择index文件": "请选择index文件", - "请选择pth文件": "请选择pth 文件", - "请选择说话人id": "Seleziona ID locutore/cantante:", - "转换": "Convertire", - "输入实验名": "Inserisci il nome dell'esperimento:", - "输入待处理音频文件夹路径": "Immettere il percorso della cartella audio da elaborare:", - "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)": "Immettere il percorso della cartella audio da elaborare (copiarlo dalla barra degli indirizzi del file manager):", - "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": "Regola il ridimensionamento dell'inviluppo del volume. ", - "输入监听": "输入监听", - "输入训练文件夹路径": "Inserisci il percorso della cartella di addestramento:", - "输入设备": "Dispositivo di input", - "输入降噪": "Riduzione del rumore in ingresso", - "输出信息": "Informazioni sull'uscita", - "输出变声": "输出变声", - "输出设备": "Dispositivo di uscita", - "输出降噪": "Riduzione del rumore in uscita", - "输出音频(右下角三个点,点了可以下载)": "Esporta audio (clicca sui tre puntini in basso a destra per scaricarlo)", - "选择.index文件": "Seleziona il file .index", - "选择.pth文件": "Seleziona il file .pth", - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU": "Seleziona l'algoritmo di estrazione del tono (\"pm\": estrazione più veloce ma risultato di qualità inferiore; \"harvest\": bassi migliori ma estremamente lenti; \"crepe\": qualità migliore ma utilizzo intensivo della GPU):", - "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU": "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU", - "采样率": "采样率", - "采样长度": "Lunghezza del campione", - "重载设备列表": "Ricaricare l'elenco dei dispositivi", - "链接索引到外部": "链接索引到外部", - "音调设置": "Impostazioni del tono", - "音频设备": "Dispositivo audio", - "音高引导(f0)": "音高引导(f0)", - "音高算法": "音高算法", - "额外推理时长": "Tempo di inferenza extra" + "Unload model to save GPU memory": "Scarica la voce per risparmiare memoria della GPU:", + "Version": "Versione", + "View": "Visualizzazione", + "Vocals/Accompaniment Separation & Reverberation Removal": "Separazione voce/accompagnamento", + "Weight (w) for Model A": "Peso (w) per il modello A:", + "Whether the model has pitch guidance": "Se il modello ha una guida del tono:", + "Whether the model has pitch guidance (1: yes, 0: no)": "Se il modello ha una guida del tono (1: sì, 0: no):", + "Whether the model has pitch guidance (required for singing, optional for speech)": "Se il modello ha una guida del tono (necessario per il canto, facoltativo per il parlato):", + "Yes": "SÌ", + "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" } diff --git a/i18n/locale/ja_JP.json b/i18n/locale/ja_JP.json index 7e05417..3eaaee6 100644 --- a/i18n/locale/ja_JP.json +++ b/i18n/locale/ja_JP.json @@ -1,160 +1,157 @@ { - "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### 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", - "### 查看模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### 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": "### 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。", - "### 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": "### 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。", - "### 模型融合\n可用于测试音色融合": "### モデルマージ\n音源のマージテストに使用できます", - "### 第一步 填写实验配置\n实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件.": "### 第一歩 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。", - "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.": "### 第三歩 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。", - "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.": "### 第二歩 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。", - "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).": "#### 2. 特徴抽出\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)", - ">=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": ">=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。", - "A模型ID(长)": "AモデルID(長)", - "A模型权重": "Aモデル占有率", - "A模型路径": "Aモデルのパス", - "B模型ID(长)": "BモデルID(長)", - "B模型路径": "Bモデルのパス", - "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调": "F0(最低共振周波数)カーブファイル(オプション、1行に1ピッチ、デフォルトのF0(最低共振周波数)とエレベーションを置き換えます。)", - "ID(短)": "ID(短)", - "ID(长)": "ID(長)", + "### 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.", + "### 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 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", + "#### 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", + "Algorithmic delays (ms)": "推論遅延(ms)", + "All processes have been completed!": "全工程が完了！", + "Audio device": "音声デバイス", + "Auto-detect index path and select from the dropdown": "索引パスの自動検出 ドロップダウンで選択", + "Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt').": "一括変換、変換する音声フォルダを入力、または複数の音声ファイルをアップロードし、指定したフォルダ(デフォルトのopt)に変換した音声を出力します。", + "Batch inference": "一括推論", + "Batch processing for vocal accompaniment separation using the UVR5 model.
Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).
The model is divided into three categories:
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.
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.
3. De-reverb and de-delay models (by FoxJoy):
  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;
 (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.
De-reverb/de-delay notes:
1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.
2. The MDX-Net-Dereverb model is quite slow.
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。
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)。
モデルは三つのカテゴリに分かれています:
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。
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。
3. ディリバーブとディレイモデル (by FoxJoy):
  (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；
 (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。
ディリバーブ/ディレイに関する注意点:
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。
2. MDX-Net-Dereverbモデルは非常に遅いです。
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", + "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", + "Device type": "デバイス種類", + "Enable phase vocoder": "启用相位声码器", + "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1": "rmvpeカード番号設定：異なるプロセスに使用するカード番号を入力する。例えば、0-0-1でカード0に2つのプロセス、カード1に1つのプロセスを実行する。", + "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2": "ハイフンで区切って使用するGPUの番号を入力します。例えば0-1-2はGPU0、GPU1、GPU2を使用します", + "Enter the experiment name": "モデル名", + "Enter the path of the audio folder to be processed": "処理するオーディオファイルのフォルダパスを入力してください", + "Enter the path of the audio folder to be processed (copy it from the address bar of the file manager)": "処理対象音声フォルダーのパスを入力してください(エクスプローラーのアドレスバーからコピーしてください)", + "Enter the path of the training folder": "学習用フォルダのパスを入力してください", + "Exist": "有", + "Export Onnx": "Onnx抽出", + "Export Onnx Model": "Onnxに変換", + "Export audio (click on the three dots in the lower right corner to download)": "出力音声(右下の三点をクリックしてダウンロードできます)", + "Export file format": "エクスポート形式", + "Extra inference time": "追加推論時間", + "Extract": "抽出", + "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation": "F0(最低共振周波数)カーブファイル(オプション、1行に1ピッチ、デフォルトのF0(最低共振周波数)とエレベーションを置き換えます。)", + "FAQ (Frequently Asked Questions)": "よくある質問", + "Fade length": "フェードイン/フェードアウト長", + "Fail": "失败", + "Feature extraction": "特徴抽出", + "Formant offset": "共振偏移", + "Fusion": "マージ", + "GPU Information": "GPU情報", + "General settings": "一般設定", + "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(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", + "Information": "情報", + "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", + "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", + "Loudness factor": "ラウドネス係数", + "Model": "モデル", + "Model Author": "モデル作者", + "Model Author (Nullable)": "モデル作者(空き可)", + "Model Inference": "モデル推論", + "Model architecture version": "モデルのバージョン", + "Model info": "モデル情報", + "Model information to be modified": "変更するモデル情報", + "Model information to be placed": "挿入するモデル情報", + "Model name": "モデル名", + "Modify": "変更", + "Multiple audio files can also be imported. If a folder path exists, this input is ignored.": "複数のオーディオファイルをインポートすることもできます。フォルダパスが存在する場合、この入力は無視されます。", + "No": "いいえ", "None": "空き", - "Onnx导出": "Onnx抽出", - "Onnx输出路径": "Onnx出力パス", - "RVC模型路径": "RVCモデルパス", + "Not exist": "無", + "Number of CPU processes used for harvest pitch algorithm": "harvestプロセス数", + "Number of CPU processes used for pitch extraction and data processing": "ピッチの抽出やデータ処理に使用するCPUスレッド数", + "One-click training": "ワンクリック学習", + "Onnx Export Path": "Onnx出力パス", + "Output converted voice": "出力音声変換", + "Output device": "出力デバイス", + "Output information": "出力情報", + "Output noise reduction": "出力騒音低減", + "Path to Model": "モデルパス", + "Path to Model A": "Aモデルのパス", + "Path to Model B": "Bモデルのパス", + "Path to the feature index file. Leave blank to use the selected result from the dropdown": "特徴検索ライブラリへのパス 空の場合はドロップダウンで選択", + "Performance settings": "パフォーマンス設定", + "Pitch detection algorithm": "ピッチアルゴリズム", + "Pitch guidance (f0)": "ピッチ導き(f0)", + "Pitch settings": "音程設定", + "Please choose the .index file": "indexファイルを選択してください", + "Please choose the .pth file": "pthファイルを選択してください", + "Please specify the speaker/singer ID": "話者IDを指定してください", + "Process data": "データ処理", + "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": "明確な子音と呼吸音を保護し、電子音の途切れやその他のアーティファクトを防止します。0.5でオフになります。下げると保護が強化されますが、indexの効果が低下する可能性があります。", + "RVC Model Path": "RVCモデルパス", + "Read from model": "モデルから読込", + "Refresh voice list and index path": "音源リストと索引パスの更新", + "Reload device list": "デバイス列の再読込", + "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling": "最終的なサンプリング率へのポストプロセッシングのリサンプリング リサンプリングしない場合は0", + "Response threshold": "反応閾値", + "Sample length": "サンプル長", + "Sampling rate": "サンプル率", + "Save a small final model to the 'weights' folder at each save point": "各保存時点の小モデルを全部weightsフォルダに保存するかどうか", + "Save file name (default: same as the source file)": "保存するファイル名、デフォルトでは空欄で元のファイル名と同じ名前になります", + "Save frequency (save_every_epoch)": "エポックごとの保存頻度", + "Save name": "保存ファイル名", + "Save only the latest '.ckpt' file to save disk space": "ハードディスク容量を節約するため、最新のckptファイルのみを保存しますか？", + "Saved model name (without extension)": "拡張子のない保存するモデル名", + "Sealing date": "締め日付", + "Search feature ratio (controls accent strength, too high has artifacting)": "検索特徴率", + "Select Speaker/Singer ID": "話者IDを選択してください", + "Select the .index file": ".indexファイルを選択", + "Select the .pth file": ".pthファイルを選択", + "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)", + "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", + "Specify the output folder for vocals": "マスターの出力音声フォルダーを指定する", + "Start audio conversion": "音声変換を開始", + "Step 1: Processing data": "step1:処理中のデータ", + "Step 3a: Model training started": "step3a:学習中のモデル", + "Stop audio conversion": "音声変換を停止", + "Successfully built index into": "成功构建索引到", + "Takeover WASAPI device": "WASAPIデバイスを独占", + "Target sample rate": "目標サンプリング率", + "The audio file to be processed": "処理待ち音声", + "This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible.
If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory Agreement-LICENSE.txt for details.": "本ソフトウェアはMITライセンスに基づくオープンソースであり、製作者は本ソフトウェアに対していかなる責任を持ちません。本ソフトウェアの利用者および本ソフトウェアから派生した音源(成果物)を配布する者は、本ソフトウェアに対して自身で責任を負うものとします。
この条項に同意しない場合、パッケージ内のコードやファイルを使用や参照を禁じます。詳しくはLICENSEをご覧ください。", + "Total training epochs (total_epoch)": "総エポック数", + "Train": "学習", + "Train feature index": "特徴索引の学習", + "Train model": "モデルの学習", + "Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder.": "学習終了時に、学習ログやフォルダ内のtrain.logを確認することができます", + "Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)": "ピッチ変更(整数、半音数、上下オクターブ12-12)", + "Unfortunately, there is no compatible GPU available to support your training.": "学習に対応したGPUが動作しないのは残念です。", "Unknown": "未知", - "ckpt处理": "ckptファイルの処理", - "harvest进程数": "harvestプロセス数", - "index文件路径不可包含中文": "indexファイルのパスに漢字を含んではいけません", - "pth文件路径不可包含中文": "pthファイルのパスに漢字を含んではいけません", - "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程": "rmvpeカード番号設定：異なるプロセスに使用するカード番号を入力する。例えば、0-0-1でカード0に2つのプロセス、カード1に1つのプロセスを実行する。", - "step1:正在处理数据": "step1:処理中のデータ", - "step2:正在提取音高&正在提取特征": "step2:ピッチ抽出と特徴抽出", - "step3a:正在训练模型": "step3a:学習中のモデル", - "一键训练": "ワンクリック学習", - "不显示": "無表示", - "也可批量输入音频文件, 二选一, 优先读文件夹": "複数のオーディオファイルをインポートすることもできます。フォルダパスが存在する場合、この入力は無視されます。", - "人声伴奏分离批量处理， 使用UVR5模型。
合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
模型分为三类：
1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
3、去混响、去延迟模型（by FoxJoy）：
  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
 (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
去混响/去延迟，附：
1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
2、MDX-Net-Dereverb模型挺慢的；
3、个人推荐的最干净的配置是先MDX-Net再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。
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)。
モデルは三つのカテゴリに分かれています:
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。
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。
3. ディリバーブとディレイモデル (by FoxJoy):
  (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；
 (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。
ディリバーブ/ディレイに関する注意点:
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。
2. MDX-Net-Dereverbモデルは非常に遅いです。
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。", - "从模型中读取": "モデルから読込", - "以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2": "ハイフンで区切って使用するGPUの番号を入力します。例えば0-1-2はGPU0、GPU1、GPU2を使用します", - "伴奏人声分离&去混响&去回声": "伴奏ボーカル分離＆残響除去＆エコー除去", - "使用模型采样率": "モデルサンプリング率を使用", - "使用设备采样率": "デバイスサンプリング率を使用", - "保存名": "保存ファイル名", - "保存的文件名, 默认空为和源文件同名": "保存するファイル名、デフォルトでは空欄で元のファイル名と同じ名前になります", - "保存的模型名不带后缀": "拡張子のない保存するモデル名", - "保存频率save_every_epoch": "エポックごとの保存頻度", - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果": "明確な子音と呼吸音を保護し、電子音の途切れやその他のアーティファクトを防止します。0.5でオフになります。下げると保護が強化されますが、indexの効果が低下する可能性があります。", - "信息": "情報", - "修改": "変更", - "停止音频转换": "音声変換を停止", - "全流程结束！": "全工程が完了！", - "共振偏移": "共振偏移", - "刷新音色列表和索引路径": "音源リストと索引パスの更新", - "加载模型": "モデルをロード", - "加载预训练底模D路径": "事前学習済みのDモデルのパス", - "加载预训练底模G路径": "事前学習済みのGモデルのパス", - "单次推理": "一度推論", - "卸载音色省显存": "音源を削除してメモリを節約", - "变调(整数, 半音数量, 升八度12降八度-12)": "ピッチ変更(整数、半音数、上下オクターブ12-12)", - "后处理重采样至最终采样率，0为不进行重采样": "最終的なサンプリング率へのポストプロセッシングのリサンプリング リサンプリングしない場合は0", - "否": "いいえ", - "启用相位声码器": "启用相位声码器", - "响应阈值": "反応閾値", - "响度因子": "ラウドネス係数", - "处理数据": "データ処理", - "失败": "失败", - "实际计算": "実際計算", - "导出Onnx模型": "Onnxに変換", - "导出文件格式": "エクスポート形式", - "封装时间": "締め日付", - "常见问题解答": "よくある質問", - "常规设置": "一般設定", - "开始音频转换": "音声変換を開始", - "待处理音频文件": "処理待ち音声", - "很遗憾您这没有能用的显卡来支持您训练": "学習に対応したGPUが動作しないのは残念です。", - "性能设置": "パフォーマンス設定", - "总训练轮数total_epoch": "総エポック数", - "成功构建索引到": "成功构建索引到", - "批量推理": "一括推論", - "批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ": "一括変換、変換する音声フォルダを入力、または複数の音声ファイルをアップロードし、指定したフォルダ(デフォルトのopt)に変換した音声を出力します。", - "指定输出主人声文件夹": "マスターの出力音声フォルダーを指定する", - "指定输出文件夹": "出力フォルダを指定してください", - "指定输出非主人声文件夹": "マスター以外の出力音声フォルダーを指定する", - "推理时间(ms)": "推論時間(ms)", - "推理音色": "音源推論", - "提取": "抽出", - "提取音高和处理数据使用的CPU进程数": "ピッチの抽出やデータ処理に使用するCPUスレッド数", - "无": "無", - "是": "はい", - "是否仅保存最新的ckpt文件以节省硬盘空间": "ハードディスク容量を節約するため、最新のckptファイルのみを保存しますか？", - "是否在每次保存时间点将最终小模型保存至weights文件夹": "各保存時点の小モデルを全部weightsフォルダに保存するかどうか", - "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速": "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。", - "显卡信息": "GPU情報", - "有": "有", - "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.": "本ソフトウェアはMITライセンスに基づくオープンソースであり、製作者は本ソフトウェアに対していかなる責任を持ちません。本ソフトウェアの利用者および本ソフトウェアから派生した音源(成果物)を配布する者は、本ソフトウェアに対して自身で責任を負うものとします。
この条項に同意しない場合、パッケージ内のコードやファイルを使用や参照を禁じます。詳しくはLICENSEをご覧ください。", - "查看": "表示", - "检索特征占比": "検索特徴率", - "模型": "モデル", - "模型作者": "モデル作者", - "模型作者(可空)": "モデル作者(空き可)", - "模型信息": "モデル情報", - "模型名": "モデル名", - "模型推理": "モデル推論", - "模型是否带音高指导": "モデルに音高ガイドを付けるかどうか", - "模型是否带音高指导(唱歌一定要, 语音可以不要)": "モデルに音高ガイドがあるかどうか(歌唱には必要ですが、音声には必要ありません)", - "模型是否带音高指导,1是0否": "モデルに音高ガイドを付けるかどうか、1は付ける、0は付けない", - "模型版本型号": "モデルのバージョン", - "模型路径": "モデルパス", - "每张显卡的batch_size": "GPUごとのバッチサイズ", - "淡入淡出长度": "フェードイン/フェードアウト長", - "版本": "バージョン", - "特征提取": "特徴抽出", - "特征检索库文件路径,为空则使用下拉的选择结果": "特徴検索ライブラリへのパス 空の場合はドロップダウンで選択", - "独占 WASAPI 设备": "WASAPIデバイスを独占", - "男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ": "男性から女性へは+12キーをお勧めします。女性から男性へは-12キーをお勧めします。音域が広すぎて音質が劣化した場合は、適切な音域に自分で調整してください。", - "目标采样率": "目標サンプリング率", - "相似度": "相似度", - "相似度(0到1)": "相似度(0到1)", - "算法延迟(ms)": "推論遅延(ms)", - "自动检测index路径,下拉式选择(dropdown)": "索引パスの自動検出 ドロップダウンで選択", - "融合": "マージ", - "要改的模型信息": "変更するモデル情報", - "要置入的模型信息": "挿入するモデル情報", - "计算": "计算", - "训练": "学習", - "训练模型": "モデルの学習", - "训练特征索引": "特徴索引の学習", - "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": "学習終了時に、学習ログやフォルダ内のtrain.logを確認することができます", - "设备类型": "デバイス種類", - "请指定说话人id": "話者IDを指定してください", - "请选择index文件": "indexファイルを選択してください", - "请选择pth文件": "pthファイルを選択してください", - "请选择说话人id": "話者IDを選択してください", - "转换": "変換", - "输入实验名": "モデル名", - "输入待处理音频文件夹路径": "処理するオーディオファイルのフォルダパスを入力してください", - "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)": "処理対象音声フォルダーのパスを入力してください(エクスプローラーのアドレスバーからコピーしてください)", - "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": "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", - "输入监听": "入力返聴", - "输入训练文件夹路径": "学習用フォルダのパスを入力してください", - "输入设备": "入力デバイス", - "输入降噪": "入力騒音低減", - "输出信息": "出力情報", - "输出变声": "出力音声変換", - "输出设备": "出力デバイス", - "输出降噪": "出力騒音低減", - "输出音频(右下角三个点,点了可以下载)": "出力音声(右下の三点をクリックしてダウンロードできます)", - "选择.index文件": ".indexファイルを選択", - "选择.pth文件": ".pthファイルを選択", - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃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、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", - "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃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。", - "采样率": "サンプル率", - "采样长度": "サンプル長", - "重载设备列表": "デバイス列の再読込", - "链接索引到外部": "链接索引到外部", - "音调设置": "音程設定", - "音频设备": "音声デバイス", - "音高引导(f0)": "ピッチ導き(f0)", - "音高算法": "ピッチアルゴリズム", - "额外推理时长": "追加推論時間" + "Unload model to save GPU memory": "音源を削除してメモリを節約", + "Version": "バージョン", + "View": "表示", + "Vocals/Accompaniment Separation & Reverberation Removal": "伴奏ボーカル分離＆残響除去＆エコー除去", + "Weight (w) for Model A": "Aモデル占有率", + "Whether the model has pitch guidance": "モデルに音高ガイドを付けるかどうか", + "Whether the model has pitch guidance (1: yes, 0: no)": "モデルに音高ガイドを付けるかどうか、1は付ける、0は付けない", + "Whether the model has pitch guidance (required for singing, optional for speech)": "モデルに音高ガイドがあるかどうか(歌唱には必要ですが、音声には必要ありません)", + "Yes": "はい", + "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" } diff --git a/i18n/locale/ko_KR.json b/i18n/locale/ko_KR.json index b38faf5..96ee976 100644 --- a/i18n/locale/ko_KR.json +++ b/i18n/locale/ko_KR.json @@ -1,160 +1,157 @@ { - "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### 모델 정보 수정\n> 오직 weights 폴더 아래에서 추출된 작은 모델 파일만 지원", - "### 查看模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### 모델 정보 보기\n> 오직 weights 폴더에서 추출된 소형 모델 파일만 지원", - "### 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": "### 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", - "### 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", - "### 模型融合\n可用于测试音色融合": "### 모델 융합\n음색 융합 테스트에 사용 가능", - "### 第一步 填写实验配置\n实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件.": "### Step 1. 실험 구성 작성\n실험 데이터는 logs에 저장, 각 실험은 하나의 폴더, 수동으로 실험 이름 경로 입력 필요, 실험 구성, 로그, 훈련된 모델 파일 포함.", - "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.": "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.", - "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.": "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.", - "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).": "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).", - ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音": ">=3인 경우 harvest 피치 인식 결과에 중간값 필터 적용, 필터 반경은 값으로 지정, 사용 시 무성음 감소 가능", - "A模型ID(长)": "A模型ID(长)", - "A模型权重": "A 모델 가중치", - "A模型路径": "A 모델 경로", - "B模型ID(长)": "B模型ID(长)", - "B模型路径": "B 모델 경로", - "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调": "F0 곡선 파일, 선택적, 한 줄에 하나의 피치, 기본 F0 및 음높이 조절 대체", - "ID(短)": "ID(短)", + "### 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.", + "### 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 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", + "#### 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", + "Algorithmic delays (ms)": "알고리즘 지연(ms)", + "All processes have been completed!": "전체 과정 완료!", + "Audio device": "오디오 장치", + "Auto-detect index path and select from the dropdown": "자동으로 index 경로 감지, 드롭다운 선택(dropdown)", + "Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt').": "일괄 변환, 변환할 오디오 파일 폴더 입력 또는 여러 오디오 파일 업로드, 지정된 폴더(기본값 opt)에 변환된 오디오 출력.", + "Batch inference": "일괄 추론", + "Batch processing for vocal accompaniment separation using the UVR5 model.
Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).
The model is divided into three categories:
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.
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.
3. De-reverb and de-delay models (by FoxJoy):
  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;
 (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.
De-reverb/de-delay notes:
1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.
2. The MDX-Net-Dereverb model is quite slow.
3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive.": "인간 목소리와 반주 분리 배치 처리, UVR5 모델 사용.
적절한 폴더 경로 예시: E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(파일 관리자 주소 표시줄에서 복사하면 됨).
모델은 세 가지 유형으로 나뉨:
1. 인간 목소리 보존: 화음이 없는 오디오에 이것을 선택, HP5보다 주된 인간 목소리 보존에 더 좋음. 내장된 HP2와 HP3 두 모델, HP3는 약간의 반주 누락 가능성이 있지만 HP2보다 주된 인간 목소리 보존이 약간 더 좋음;
2. 주된 인간 목소리만 보존: 화음이 있는 오디오에 이것을 선택, 주된 인간 목소리에 약간의 약화 가능성 있음. 내장된 HP5 모델 하나;
3. 혼효음 제거, 지연 제거 모델(by FoxJoy):
  (1)MDX-Net(onnx_dereverb): 이중 채널 혼효음에는 최선의 선택, 단일 채널 혼효음은 제거할 수 없음;
 (234)DeEcho: 지연 제거 효과. Aggressive는 Normal보다 더 철저하게 제거, DeReverb는 추가로 혼효음을 제거, 단일 채널 혼효음은 제거 가능하지만 고주파 중심의 판 혼효음은 완전히 제거하기 어려움.
혼효음/지연 제거, 부록:
1. DeEcho-DeReverb 모델의 처리 시간은 다른 두 개의 DeEcho 모델의 거의 2배임;
2. MDX-Net-Dereverb 모델은 상당히 느림;
3. 개인적으로 추천하는 가장 깨끗한 구성은 MDX-Net 다음에 DeEcho-Aggressive 사용.", + "Batch size per GPU": "각 그래픽 카드의 batch_size", + "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": "모든 훈련 세트를 VRAM에 캐시할지 여부. 10분 미만의 소량 데이터는 캐시하여 훈련 속도를 높일 수 있지만, 대량 데이터 캐시는 VRAM을 과부하시키고 속도를 크게 향상시키지 못함", + "Calculate": "计算", + "Choose sample rate of the device": "장치 샘플링 레이트 사용", + "Choose sample rate of the model": "모델 샘플링 레이트 사용", + "Convert": "변환", + "Device type": "设备类型", + "Enable phase vocoder": "위상 보코더 활성화", + "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1": "rmvpe 카드 번호 설정: -로 구분된 입력 사용 카드 번호, 예: 0-0-1은 카드 0에서 2개 프로세스, 카드 1에서 1개 프로세스 실행", + "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2": "-로 구분하여 입력하는 카드 번호, 예: 0-1-2는 카드 0, 카드 1, 카드 2 사용", + "Enter the experiment name": "실험명 입력", + "Enter the path of the audio folder to be processed": "처리할 오디오 파일 폴더 경로 입력", + "Enter the path of the audio folder to be processed (copy it from the address bar of the file manager)": "처리할 오디오 파일 폴더 경로 입력(파일 탐색기 주소 표시줄에서 복사)", + "Enter the path of the training folder": "훈련 파일 폴더 경로 입력", + "Exist": "有", + "Export Onnx": "Onnx 내보내기", + "Export Onnx Model": "Onnx 모델 내보내기", + "Export audio (click on the three dots in the lower right corner to download)": "출력 오디오(오른쪽 하단 세 개의 점, 클릭하면 다운로드 가능)", + "Export file format": "내보낼 파일 형식", + "Extra inference time": "추가 추론 시간", + "Extract": "추출", + "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation": "F0 곡선 파일, 선택적, 한 줄에 하나의 피치, 기본 F0 및 음높이 조절 대체", + "FAQ (Frequently Asked Questions)": "자주 묻는 질문", + "Fade length": "페이드 인/아웃 길이", + "Fail": "失败", + "Feature extraction": "특징 추출", + "Formant offset": "共振偏移", + "Fusion": "융합", + "GPU Information": "그래픽 카드 정보", + "General settings": "일반 설정", + "Hidden": "不显示", + "ID of model A (long)": "A模型ID(长)", + "ID of model B (long)": "B模型ID(长)", + "ID(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", + "Information": "정보", + "Input device": "입력 장치", + "Input noise reduction": "입력 노이즈 감소", + "Input voice monitor": "입력 모니터링", + "Link index to outside folder": "链接索引到外部", + "Load model": "모델 로드", + "Load pre-trained base model D path": "미리 훈련된 베이스 모델 D 경로 로드", + "Load pre-trained base model G path": "미리 훈련된 베이스 모델 G 경로 로드", + "Loudness factor": "음량 인자", + "Model": "모델", + "Model Author": "模型作者", + "Model Author (Nullable)": "模型作者(可空)", + "Model Inference": "모델 추론", + "Model architecture version": "모델 버전 및 모델", + "Model info": "모델 정보", + "Model information to be modified": "변경할 모델 정보", + "Model information to be placed": "삽입할 모델 정보", + "Model name": "模型名", + "Modify": "수정", + "Multiple audio files can also be imported. If a folder path exists, this input is ignored.": "여러 오디오 파일을 일괄 입력할 수도 있음, 둘 중 하나 선택, 폴더 우선 읽기", + "No": "아니오", "None": "None", - "Onnx导出": "Onnx 내보내기", - "Onnx输出路径": "Onnx 출력 경로", - "RVC模型路径": "RVC 모델 경로", + "Not exist": "无", + "Number of CPU processes used for harvest pitch algorithm": "harvest 프로세스 수", + "Number of CPU processes used for pitch extraction and data processing": "음높이 추출 및 데이터 처리에 사용되는 CPU 프로세스 수", + "One-click training": "원클릭 훈련", + "Onnx Export Path": "Onnx 출력 경로", + "Output converted voice": "출력 음성 변조", + "Output device": "출력 장치", + "Output information": "출력 정보", + "Output noise reduction": "출력 노이즈 감소", + "Path to Model": "모델 경로", + "Path to Model A": "A 모델 경로", + "Path to Model B": "B 모델 경로", + "Path to the feature index file. Leave blank to use the selected result from the dropdown": "특징 검색 라이브러리 파일 경로, 비어 있으면 드롭다운 선택 결과 사용", + "Performance settings": "성능 설정", + "Pitch detection algorithm": "음높이 알고리즘", + "Pitch guidance (f0)": "音高引导(f0)", + "Pitch settings": "음조 설정", + "Please choose the .index file": "index 파일 선택", + "Please choose the .pth file": "pth 파일 선택", + "Please specify the speaker/singer ID": "화자 ID 지정 필요", + "Process data": "데이터 처리", + "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": "청자음과 호흡 소리를 보호, 전자음 찢김 등의 아티팩트 방지, 0.5까지 올려서 비활성화, 낮추면 보호 강도 증가하지만 인덱스 효과 감소 가능성 있음", + "RVC Model Path": "RVC 모델 경로", + "Read from model": "从模型中读取", + "Refresh voice list and index path": "음색 목록 및 인덱스 경로 새로고침", + "Reload device list": "장치 목록 재로드", + "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling": "후처리 재샘플링을 최종 샘플링 레이트로, 0은 재샘플링하지 않음", + "Response threshold": "응답 임계값", + "Sample length": "샘플링 길이", + "Sampling rate": "샘플링률", + "Save a small final model to the 'weights' folder at each save point": "저장 시마다 최종 소형 모델을 weights 폴더에 저장할지 여부", + "Save file name (default: same as the source file)": "저장될 파일명, 기본적으로 빈 공간은 원본 파일과 동일한 이름으로", + "Save frequency (save_every_epoch)": "저장 빈도 save_every_epoch", + "Save name": "저장 이름", + "Save only the latest '.ckpt' file to save disk space": "디스크 공간을 절약하기 위해 최신 ckpt 파일만 저장할지 여부", + "Saved model name (without extension)": "저장된 모델명은 접미사 없음", + "Sealing date": "封装时间", + "Search feature ratio (controls accent strength, too high has artifacting)": "검색 특징 비율", + "Select Speaker/Singer ID": "화자 ID 선택", + "Select the .index file": ".index 파일 선택", + "Select the .pth file": ".pth 파일 선택", + "Select the pitch extraction algorithm ('pm': faster extraction but lower-quality speech; 'harvest': better bass but extremely slow; 'crepe': better quality but GPU intensive), 'rmvpe': best quality, and little GPU requirement": "음높이 추출 알고리즘 선택, 노래 입력 시 pm으로 속도 향상, harvest는 저음이 좋지만 매우 느림, crepe는 효과가 좋지만 GPU 사용, rmvpe는 효과가 가장 좋으며 GPU를 적게 사용", + "Select the pitch extraction algorithm: when extracting singing, you can use 'pm' to speed up. For high-quality speech with fast performance, but worse CPU usage, you can use 'dio'. 'harvest' results in better quality but is slower. 'rmvpe' has the best results and consumes less CPU/GPU": "음높이 추출 알고리즘 선택: 노래 입력 시 pm으로 속도 향상, 고품질 음성에는 CPU가 부족할 때 dio 사용, harvest는 품질이 더 좋지만 느림, rmvpe는 효과가 가장 좋으며 CPU/GPU를 적게 사용", + "Similarity": "相似度", + "Similarity (from 0 to 1)": "相似度(0到1)", + "Single inference": "단일 추론", + "Specify output folder": "출력 파일 폴더 지정", + "Specify the output folder for accompaniment": "주된 목소리가 아닌 출력 폴더 지정", + "Specify the output folder for vocals": "주된 목소리 출력 폴더 지정", + "Start audio conversion": "오디오 변환 시작", + "Step 1: Processing data": "step1: 데이터 처리 중", + "Step 3a: Model training started": "step3a: 모델 훈련 중", + "Stop audio conversion": "오디오 변환 중지", + "Successfully built index into": "成功构建索引到", + "Takeover WASAPI device": "独占 WASAPI 设备", + "Target sample rate": "목표 샘플링률", + "The audio file to be processed": "待处理音频文件", + "This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible.
If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory Agreement-LICENSE.txt for details.": "이 소프트웨어는 MIT 라이선스로 공개되며, 저자는 소프트웨어에 대해 어떠한 통제권도 가지지 않습니다. 모든 귀책사유는 소프트웨어 사용자 및 소프트웨어에서 생성된 결과물을 사용하는 당사자에게 있습니다.
해당 조항을 인정하지 않는 경우, 소프트웨어 패키지의 어떠한 코드나 파일도 사용하거나 인용할 수 없습니다. 자세한 내용은 루트 디렉토리의 LICENSE를 참조하세요.", + "Total training epochs (total_epoch)": "총 훈련 라운드 수 total_epoch", + "Train": "훈련", + "Train feature index": "특징 인덱스 훈련", + "Train model": "모델 훈련", + "Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder.": "훈련 완료, 콘솔 훈련 로그 또는 실험 폴더 내의 train.log 확인 가능", + "Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)": "키 변경(정수, 반음 수, 옥타브 상승 12, 옥타브 하강 -12)", + "Unfortunately, there is no compatible GPU available to support your training.": "사용 가능한 그래픽 카드가 없어 훈련을 지원할 수 없습니다", "Unknown": "Unknown", - "ckpt处理": "ckpt 처리", - "harvest进程数": "harvest 프로세스 수", - "index文件路径不可包含中文": "index 파일 경로는 중국어를 포함할 수 없음", - "pth文件路径不可包含中文": "pth 파일 경로는 중국어를 포함할 수 없음", - "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程": "rmvpe 카드 번호 설정: -로 구분된 입력 사용 카드 번호, 예: 0-0-1은 카드 0에서 2개 프로세스, 카드 1에서 1개 프로세스 실행", - "step1:正在处理数据": "step1: 데이터 처리 중", - "step2:正在提取音高&正在提取特征": "step2: 음높이 추출 & 특징 추출 중", - "step3a:正在训练模型": "step3a: 모델 훈련 중", - "一键训练": "원클릭 훈련", - "不显示": "不显示", - "也可批量输入音频文件, 二选一, 优先读文件夹": "여러 오디오 파일을 일괄 입력할 수도 있음, 둘 중 하나 선택, 폴더 우선 읽기", - "人声伴奏分离批量处理， 使用UVR5模型。
合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
模型分为三类：
1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
3、去混响、去延迟模型（by FoxJoy）：
  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
 (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
去混响/去延迟，附：
1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
2、MDX-Net-Dereverb模型挺慢的；
3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。": "인간 목소리와 반주 분리 배치 처리, UVR5 모델 사용.
적절한 폴더 경로 예시: E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(파일 관리자 주소 표시줄에서 복사하면 됨).
모델은 세 가지 유형으로 나뉨:
1. 인간 목소리 보존: 화음이 없는 오디오에 이것을 선택, HP5보다 주된 인간 목소리 보존에 더 좋음. 내장된 HP2와 HP3 두 모델, HP3는 약간의 반주 누락 가능성이 있지만 HP2보다 주된 인간 목소리 보존이 약간 더 좋음;
2. 주된 인간 목소리만 보존: 화음이 있는 오디오에 이것을 선택, 주된 인간 목소리에 약간의 약화 가능성 있음. 내장된 HP5 모델 하나;
3. 혼효음 제거, 지연 제거 모델(by FoxJoy):
  (1)MDX-Net(onnx_dereverb): 이중 채널 혼효음에는 최선의 선택, 단일 채널 혼효음은 제거할 수 없음;
 (234)DeEcho: 지연 제거 효과. Aggressive는 Normal보다 더 철저하게 제거, DeReverb는 추가로 혼효음을 제거, 단일 채널 혼효음은 제거 가능하지만 고주파 중심의 판 혼효음은 완전히 제거하기 어려움.
혼효음/지연 제거, 부록:
1. DeEcho-DeReverb 모델의 처리 시간은 다른 두 개의 DeEcho 모델의 거의 2배임;
2. MDX-Net-Dereverb 모델은 상당히 느림;
3. 개인적으로 추천하는 가장 깨끗한 구성은 MDX-Net 다음에 DeEcho-Aggressive 사용.", - "从模型中读取": "从模型中读取", - "以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2": "-로 구분하여 입력하는 카드 번호, 예: 0-1-2는 카드 0, 카드 1, 카드 2 사용", - "伴奏人声分离&去混响&去回声": "반주 인간 목소리 분리 & 혼효음 제거 & 에코 제거", - "使用模型采样率": "모델 샘플링 레이트 사용", - "使用设备采样率": "장치 샘플링 레이트 사용", - "保存名": "저장 이름", - "保存的文件名, 默认空为和源文件同名": "저장될 파일명, 기본적으로 빈 공간은 원본 파일과 동일한 이름으로", - "保存的模型名不带后缀": "저장된 모델명은 접미사 없음", - "保存频率save_every_epoch": "저장 빈도 save_every_epoch", - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果": "청자음과 호흡 소리를 보호, 전자음 찢김 등의 아티팩트 방지, 0.5까지 올려서 비활성화, 낮추면 보호 강도 증가하지만 인덱스 효과 감소 가능성 있음", - "信息": "정보", - "修改": "수정", - "停止音频转换": "오디오 변환 중지", - "全流程结束！": "전체 과정 완료!", - "共振偏移": "共振偏移", - "刷新音色列表和索引路径": "음색 목록 및 인덱스 경로 새로고침", - "加载模型": "모델 로드", - "加载预训练底模D路径": "미리 훈련된 베이스 모델 D 경로 로드", - "加载预训练底模G路径": "미리 훈련된 베이스 모델 G 경로 로드", - "单次推理": "단일 추론", - "卸载音色省显存": "음색 언로드로 디스플레이 메모리 절약", - "变调(整数, 半音数量, 升八度12降八度-12)": "키 변경(정수, 반음 수, 옥타브 상승 12, 옥타브 하강 -12)", - "后处理重采样至最终采样率，0为不进行重采样": "후처리 재샘플링을 최종 샘플링 레이트로, 0은 재샘플링하지 않음", - "否": "아니오", - "启用相位声码器": "위상 보코더 활성화", - "响应阈值": "응답 임계값", - "响度因子": "음량 인자", - "处理数据": "데이터 처리", - "失败": "失败", - "实际计算": "实际计算", - "导出Onnx模型": "Onnx 모델 내보내기", - "导出文件格式": "내보낼 파일 형식", - "封装时间": "封装时间", - "常见问题解答": "자주 묻는 질문", - "常规设置": "일반 설정", - "开始音频转换": "오디오 변환 시작", - "待处理音频文件": "待处理音频文件", - "很遗憾您这没有能用的显卡来支持您训练": "사용 가능한 그래픽 카드가 없어 훈련을 지원할 수 없습니다", - "性能设置": "성능 설정", - "总训练轮数total_epoch": "총 훈련 라운드 수 total_epoch", - "成功构建索引到": "成功构建索引到", - "批量推理": "일괄 추론", - "批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ": "일괄 변환, 변환할 오디오 파일 폴더 입력 또는 여러 오디오 파일 업로드, 지정된 폴더(기본값 opt)에 변환된 오디오 출력.", - "指定输出主人声文件夹": "주된 목소리 출력 폴더 지정", - "指定输出文件夹": "출력 파일 폴더 지정", - "指定输出非主人声文件夹": "주된 목소리가 아닌 출력 폴더 지정", - "推理时间(ms)": "추론 시간 (ms)", - "推理音色": "추론 음색", - "提取": "추출", - "提取音高和处理数据使用的CPU进程数": "음높이 추출 및 데이터 처리에 사용되는 CPU 프로세스 수", - "无": "无", - "是": "예", - "是否仅保存最新的ckpt文件以节省硬盘空间": "디스크 공간을 절약하기 위해 최신 ckpt 파일만 저장할지 여부", - "是否在每次保存时间点将最终小模型保存至weights文件夹": "저장 시마다 최종 소형 모델을 weights 폴더에 저장할지 여부", - "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速": "모든 훈련 세트를 VRAM에 캐시할지 여부. 10분 미만의 소량 데이터는 캐시하여 훈련 속도를 높일 수 있지만, 대량 데이터 캐시는 VRAM을 과부하시키고 속도를 크게 향상시키지 못함", - "显卡信息": "그래픽 카드 정보", - "有": "有", - "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.": "이 소프트웨어는 MIT 라이선스로 공개되며, 저자는 소프트웨어에 대해 어떠한 통제권도 가지지 않습니다. 모든 귀책사유는 소프트웨어 사용자 및 소프트웨어에서 생성된 결과물을 사용하는 당사자에게 있습니다.
해당 조항을 인정하지 않는 경우, 소프트웨어 패키지의 어떠한 코드나 파일도 사용하거나 인용할 수 없습니다. 자세한 내용은 루트 디렉토리의 LICENSE를 참조하세요.", - "查看": "보기", - "检索特征占比": "검색 특징 비율", - "模型": "모델", - "模型作者": "模型作者", - "模型作者(可空)": "模型作者(可空)", - "模型信息": "모델 정보", - "模型名": "模型名", - "模型推理": "모델 추론", - "模型是否带音高指导": "모델이 음높이 지도를 포함하는지 여부", - "模型是否带音高指导(唱歌一定要, 语音可以不要)": "모델이 음높이 지도를 포함하는지 여부(노래에는 반드시 필요, 음성에는 필요 없음)", - "模型是否带音高指导,1是0否": "모델이 음높이 지도를 포함하는지 여부, 1은 예, 0은 아니오", - "模型版本型号": "모델 버전 및 모델", - "模型路径": "모델 경로", - "每张显卡的batch_size": "각 그래픽 카드의 batch_size", - "淡入淡出长度": "페이드 인/아웃 길이", - "版本": "버전", - "特征提取": "특징 추출", - "特征检索库文件路径,为空则使用下拉的选择结果": "특징 검색 라이브러리 파일 경로, 비어 있으면 드롭다운 선택 결과 사용", - "独占 WASAPI 设备": "独占 WASAPI 设备", - "男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ": "남성에서 여성으로 변경 시 +12 키 권장, 여성에서 남성으로 변경 시 -12 키 권장, 음역대 폭발로 음색이 왜곡되면 적절한 음역대로 조정 가능.", - "目标采样率": "목표 샘플링률", - "相似度": "相似度", - "相似度(0到1)": "相似度(0到1)", - "算法延迟(ms)": "알고리즘 지연(ms)", - "自动检测index路径,下拉式选择(dropdown)": "자동으로 index 경로 감지, 드롭다운 선택(dropdown)", - "融合": "융합", - "要改的模型信息": "변경할 모델 정보", - "要置入的模型信息": "삽입할 모델 정보", - "计算": "计算", - "训练": "훈련", - "训练模型": "모델 훈련", - "训练特征索引": "특징 인덱스 훈련", - "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": "훈련 완료, 콘솔 훈련 로그 또는 실험 폴더 내의 train.log 확인 가능", - "设备类型": "设备类型", - "请指定说话人id": "화자 ID 지정 필요", - "请选择index文件": "index 파일 선택", - "请选择pth文件": "pth 파일 선택", - "请选择说话人id": "화자 ID 선택", - "转换": "변환", - "输入实验名": "실험명 입력", - "输入待处理音频文件夹路径": "처리할 오디오 파일 폴더 경로 입력", - "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)": "처리할 오디오 파일 폴더 경로 입력(파일 탐색기 주소 표시줄에서 복사)", - "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": "입력 소스 볼륨 엔벨로프와 출력 볼륨 엔벨로프의 결합 비율 입력, 1에 가까울수록 출력 엔벨로프 사용", - "输入监听": "입력 모니터링", - "输入训练文件夹路径": "훈련 파일 폴더 경로 입력", - "输入设备": "입력 장치", - "输入降噪": "입력 노이즈 감소", - "输出信息": "출력 정보", - "输出变声": "출력 음성 변조", - "输出设备": "출력 장치", - "输出降噪": "출력 노이즈 감소", - "输出音频(右下角三个点,点了可以下载)": "출력 오디오(오른쪽 하단 세 개의 점, 클릭하면 다운로드 가능)", - "选择.index文件": ".index 파일 선택", - "选择.pth文件": ".pth 파일 선택", - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU": "음높이 추출 알고리즘 선택, 노래 입력 시 pm으로 속도 향상, harvest는 저음이 좋지만 매우 느림, crepe는 효과가 좋지만 GPU 사용, rmvpe는 효과가 가장 좋으며 GPU를 적게 사용", - "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU": "음높이 추출 알고리즘 선택: 노래 입력 시 pm으로 속도 향상, 고품질 음성에는 CPU가 부족할 때 dio 사용, harvest는 품질이 더 좋지만 느림, rmvpe는 효과가 가장 좋으며 CPU/GPU를 적게 사용", - "采样率": "샘플링률", - "采样长度": "샘플링 길이", - "重载设备列表": "장치 목록 재로드", - "链接索引到外部": "链接索引到外部", - "音调设置": "음조 설정", - "音频设备": "오디오 장치", - "音高引导(f0)": "音高引导(f0)", - "音高算法": "음높이 알고리즘", - "额外推理时长": "추가 추론 시간" + "Unload model to save GPU memory": "음색 언로드로 디스플레이 메모리 절약", + "Version": "버전", + "View": "보기", + "Vocals/Accompaniment Separation & Reverberation Removal": "반주 인간 목소리 분리 & 혼효음 제거 & 에코 제거", + "Weight (w) for Model A": "A 모델 가중치", + "Whether the model has pitch guidance": "모델이 음높이 지도를 포함하는지 여부", + "Whether the model has pitch guidance (1: yes, 0: no)": "모델이 음높이 지도를 포함하는지 여부, 1은 예, 0은 아니오", + "Whether the model has pitch guidance (required for singing, optional for speech)": "모델이 음높이 지도를 포함하는지 여부(노래에는 반드시 필요, 음성에는 필요 없음)", + "Yes": "예", + "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" } diff --git a/i18n/locale/pt_BR.json b/i18n/locale/pt_BR.json index 608c04d..4fb32c5 100644 --- a/i18n/locale/pt_BR.json +++ b/i18n/locale/pt_BR.json @@ -1,160 +1,157 @@ { - "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### Modificar informações do modelo\n> Suportado apenas para arquivos de modelo pequenos extraídos da pasta 'weights'.", - "### 查看模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### Exibir informações do modelo\n> Suportado apenas para arquivos de modelo pequenos extraídos da pasta 'weights'.", - "### 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": "### 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.", - "### 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", - "### 模型融合\n可用于测试音色融合": "### A fusão modelo\nPode ser usada para testar a fusão do timbre.", - "### 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.": "### Etapa 1. Preencha a configuração experimental.\nOs dados experimentais são armazenados na pasta 'logs', com cada experimento tendo uma pasta separada. Digite manualmente o caminho do nome do experimento, que contém a configuração experimental, os logs e os arquivos de modelo treinados.", - "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.": "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.", - "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.": "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.", - "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).": "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).", - ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音": ">=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.", - "A模型ID(长)": "A模型ID(长)", - "A模型权重": "Peso (w) para o modelo A:", - "A模型路径": "Caminho para o Modelo A:", - "B模型ID(长)": "B模型ID(长)", - "B模型路径": "Caminho para o Modelo B:", - "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调": "Arquivo de curva F0 (opcional). Um arremesso por linha. Substitui a modulação padrão F0 e tom:", - "ID(短)": "ID(短)", + "### 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.", + "### 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 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'.", + "#### 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:", + "Algorithmic delays (ms)": "Atrasos algorítmicos (ms)", + "All processes have been completed!": "Todos os processos foram concluídos!", + "Audio device": "音频设备", + "Auto-detect index path and select from the dropdown": "Detecte automaticamente o caminho do Index e selecione no menu suspenso:", + "Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt').": "Conversão em Massa.", + "Batch inference": "Conversão em Lote", + "Batch processing for vocal accompaniment separation using the UVR5 model.
Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).
The model is divided into three categories:
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.
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.
3. De-reverb and de-delay models (by FoxJoy):
  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;
 (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.
De-reverb/de-delay notes:
1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.
2. The MDX-Net-Dereverb model is quite slow.
3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive.": "Processamento em lote para separação de acompanhamento vocal usando o modelo UVR5.
Exemplo de um formato de caminho de pasta válido: D:\\caminho\\para a pasta\\entrada\\ (copie-o da barra de endereços do gerenciador de arquivos).
O modelo é dividido em três categorias:
1. Preservar vocais: Escolha esta opção para áudio sem harmonias. Ele preserva os vocais melhor do que o HP5. Inclui dois modelos integrados: HP2 e HP3. O HP3 pode vazar ligeiramente o acompanhamento, mas preserva os vocais um pouco melhor do que o HP2.
2 Preservar apenas os vocais principais: Escolha esta opção para áudio com harmonias. Isso pode enfraquecer os vocais principais. Ele inclui um modelo embutido: HP5.
3. Modelos de de-reverb e de-delay (por FoxJoy):
  (1) MDX-Net: A melhor escolha para remoção de reverb estéreo, mas não pode remover reverb mono;
 (234) DeEcho: Remove efeitos de atraso. O modo agressivo remove mais completamente do que o modo normal. O DeReverb também remove reverb e pode remover reverb mono, mas não de forma muito eficaz para conteúdo de alta frequência fortemente reverberado.
Notas de de-reverb/de-delay:
1. O tempo de processamento para o modelo DeEcho-DeReverb é aproximadamente duas vezes maior que os outros dois modelos DeEcho.
2 O modelo MDX-Net-Dereverb é bastante lento.
3. A configuração mais limpa recomendada é aplicar MDX-Net primeiro e depois DeEcho-Aggressive.", + "Batch size per GPU": "Batch Size (DEIXE COMO ESTÁ a menos que saiba o que está fazendo, no Colab pode deixar até 20!):", + "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": "Se deve armazenar em cache todos os conjuntos de treinamento na memória de vídeo. Pequenos dados com menos de 10 minutos podem ser armazenados em cache para acelerar o treinamento, e um cache de dados grande irá explodir a memória de vídeo e não aumentar muito a velocidade:", + "Calculate": "计算", + "Choose sample rate of the device": "使用设备采样率", + "Choose sample rate of the model": "使用模型采样率", + "Convert": "Converter", + "Device type": "设备类型", + "Enable phase vocoder": "启用相位声码器", + "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1": "Configuração do número do cartão rmvpe: Use - para separar os números dos cartões de entrada de diferentes processos. Por exemplo, 0-0-1 é usado para executar 2 processos no cartão 0 e 1 processo no cartão 1.", + "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2": "Digite o (s) índice(s) da GPU separados por '-', por exemplo, 0-1-2 para usar a GPU 0, 1 e 2:", + "Enter the experiment name": "Nome da voz:", + "Enter the path of the audio folder to be processed": "Caminho da pasta de áudio a ser processada:", + "Enter the path of the audio folder to be processed (copy it from the address bar of the file manager)": "Caminho da pasta de áudio a ser processada (copie-o da barra de endereços do gerenciador de arquivos):", + "Enter the path of the training folder": "Caminho da pasta de treinamento:", + "Exist": "有", + "Export Onnx": "Exportar Onnx", + "Export Onnx Model": "Exportar Modelo Onnx", + "Export audio (click on the three dots in the lower right corner to download)": "Exportar áudio (clique nos três pontos no canto inferior direito para baixar)", + "Export file format": "Qual formato de arquivo você prefere?", + "Extra inference time": "Tempo extra de inferência", + "Extract": "Extrato", + "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation": "Arquivo de curva F0 (opcional). Um arremesso por linha. Substitui a modulação padrão F0 e tom:", + "FAQ (Frequently Asked Questions)": "FAQ (Perguntas frequentes)", + "Fade length": "Comprimento de desvanecimento", + "Fail": "失败", + "Feature extraction": "Extrair Tom", + "Formant offset": "共振偏移", + "Fusion": "Fusão", + "GPU Information": "Informações da GPU", + "General settings": "Configurações gerais", + "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(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:", + "Information": "信息", + "Input device": "Dispositivo de entrada", + "Input noise reduction": "Redução de ruído de entrada", + "Input voice monitor": "Monitoramento de entrada", + "Link index to outside folder": "链接索引到外部", + "Load model": "Modelo", + "Load pre-trained base model D path": "Carregue o caminho D do modelo base pré-treinado:", + "Load pre-trained base model G path": "Carregue o caminho G do modelo base pré-treinado:", + "Loudness factor": "Fator de volume", + "Model": "Modelo", + "Model Author": "模型作者", + "Model Author (Nullable)": "模型作者(可空)", + "Model Inference": "Inference", + "Model architecture version": "Versão:", + "Model info": "模型信息", + "Model information to be modified": "Informações do modelo a ser modificado:", + "Model information to be placed": "Informações do modelo a ser colocado:", + "Model name": "模型名", + "Modify": "Editar", + "Multiple audio files can also be imported. If a folder path exists, this input is ignored.": "Você também pode inserir arquivos de áudio em lotes. Escolha uma das duas opções. É dada prioridade à leitura da pasta.", + "No": "Não", "None": "None", - "Onnx导出": "Exportar Onnx", - "Onnx输出路径": "Caminho de exportação ONNX:", - "RVC模型路径": "Caminho do Modelo RVC:", + "Not exist": "无", + "Number of CPU processes used for harvest pitch algorithm": "Número de processos harvest", + "Number of CPU processes used for pitch extraction and data processing": "Número de processos de CPU usados para extração de tom e processamento de dados:", + "One-click training": "Treinamento com um clique", + "Onnx Export Path": "Caminho de exportação ONNX:", + "Output converted voice": "Mudança de voz de saída", + "Output device": "Dispositivo de saída", + "Output information": "Informação de saída", + "Output noise reduction": "Redução de ruído de saída", + "Path to Model": "Caminho para o Modelo:", + "Path to Model A": "Caminho para o Modelo A:", + "Path to Model B": "Caminho para o Modelo B:", + "Path to the feature index file. Leave blank to use the selected result from the dropdown": "Caminho para o arquivo de Index. Deixe em branco para usar o resultado selecionado no menu debaixo:", + "Performance settings": "Configurações de desempenho.", + "Pitch detection algorithm": "Algoritmo de detecção de pitch", + "Pitch guidance (f0)": "音高引导(f0)", + "Pitch settings": "Configurações de tom", + "Please choose the .index file": "Selecione o arquivo de Index", + "Please choose the .pth file": "Selecione o arquivo pth", + "Please specify the speaker/singer ID": "Especifique o ID do locutor/cantor:", + "Process data": "Processar o Conjunto de Dados", + "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": "Proteja consoantes sem voz e sons respiratórios, evite artefatos como quebra de som eletrônico e desligue-o quando estiver cheio de 0,5. Diminua-o para aumentar a proteção, mas pode reduzir o efeito de indexação:", + "RVC Model Path": "Caminho do Modelo RVC:", + "Read from model": "从模型中读取", + "Refresh voice list and index path": "Atualizar lista de voz e caminho do Index", + "Reload device list": "Recarregar lista de dispositivos", + "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling": "Reamostragem pós-processamento para a taxa de amostragem final, 0 significa sem reamostragem:", + "Response threshold": "Limiar de resposta", + "Sample length": "Comprimento da Amostra", + "Sampling rate": "采样率", + "Save a small final model to the 'weights' folder at each save point": "Salve um pequeno modelo final na pasta 'weights' em cada ponto de salvamento:", + "Save file name (default: same as the source file)": "Salvar nome do arquivo (padrão: igual ao arquivo de origem):", + "Save frequency (save_every_epoch)": "Faça backup a cada # de Epoch:", + "Save name": "Salvar nome", + "Save only the latest '.ckpt' file to save disk space": "Só deve salvar apenas o arquivo ckpt mais recente para economizar espaço em disco:", + "Saved model name (without extension)": "Nome do modelo salvo (sem extensão):", + "Sealing date": "封装时间", + "Search feature ratio (controls accent strength, too high has artifacting)": "Taxa de recurso de recuperação:", + "Select Speaker/Singer ID": "Selecione Palestrantes/Cantores ID:", + "Select the .index file": "Selecione o Index", + "Select the .pth file": "Selecione o Arquivo", + "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": "Selecione o algoritmo de extração de tom \n'pm': extração mais rápida, mas discurso de qualidade inferior; \n'harvest': graves melhores, mas extremamente lentos; \n'harvest': melhor qualidade, mas extração mais lenta); 'crepe': melhor qualidade, mas intensivo em GPU; 'magio-crepe': melhor opção; 'RMVPE': um modelo robusto para estimativa de afinação vocal em música polifônica;", + "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": "Selecione o algoritmo de extração de tom \n'pm': extração mais rápida, mas discurso de qualidade inferior; \n'harvest': graves melhores, mas extremamente lentos; \n'crepe': melhor qualidade (mas intensivo em GPU);\n rmvpe tem o melhor efeito e consome menos CPU/GPU.", + "Similarity": "相似度", + "Similarity (from 0 to 1)": "相似度(0到1)", + "Single inference": "Único", + "Specify output folder": "Especifique a pasta de saída:", + "Specify the output folder for accompaniment": "Informar a pasta de saída para acompanhamento:", + "Specify the output folder for vocals": "Especifique a pasta de saída para vocais:", + "Start audio conversion": "Iniciar conversão de áudio", + "Step 1: Processing data": "Etapa 1: Processamento de dados", + "Step 3a: Model training started": "Etapa 3a: Treinamento do modelo iniciado", + "Stop audio conversion": "Conversão de áudio", + "Successfully built index into": "成功构建索引到", + "Takeover WASAPI device": "独占 WASAPI 设备", + "Target sample rate": "Taxa de amostragem:", + "The audio file to be processed": "待处理音频文件", + "This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible.
If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory Agreement-LICENSE.txt for details.": "
The Mangio-RVC 💻 | Tradução por Krisp e Rafael Godoy Ebert | AI HUB BRASIL
Este software é de código aberto sob a licença MIT. O autor não tem qualquer controle sobre o software. Aqueles que usam o software e divulgam os sons exportados pelo software são totalmente responsáveis.
Se você não concorda com este termo, você não pode usar ou citar nenhum código e arquivo no pacote de software. Para obter detalhes, consulte o diretório raiz O acordo a ser seguido para uso LICENSE
", + "Total training epochs (total_epoch)": "Número total de ciclos(epoch) de treino (se escolher um valor alto demais, o seu modelo parecerá terrivelmente sobretreinado):", + "Train": "Treinar", + "Train feature index": "Treinar Index", + "Train model": "Treinar Modelo", + "Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder.": "Após o término do treinamento, você pode verificar o log de treinamento do console ou train.log na pasta de experimentos", + "Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)": "Mude o tom aqui. Se a voz for do mesmo sexo, não é necessario alterar (12 caso seja Masculino para feminino, -12 caso seja ao contrário).", + "Unfortunately, there is no compatible GPU available to support your training.": "Infelizmente, não há GPU compatível disponível para apoiar o seu treinamento.", "Unknown": "Unknown", - "ckpt处理": "processamento ckpt", - "harvest进程数": "Número de processos harvest", - "index文件路径不可包含中文": "O caminho do arquivo de Index não pode conter caracteres chineses", - "pth文件路径不可包含中文": "o caminho do arquivo pth não pode conter caracteres chineses", - "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程": "Configuração do número do cartão rmvpe: Use - para separar os números dos cartões de entrada de diferentes processos. Por exemplo, 0-0-1 é usado para executar 2 processos no cartão 0 e 1 processo no cartão 1.", - "step1:正在处理数据": "Etapa 1: Processamento de dados", - "step2:正在提取音高&正在提取特征": "step2:正在提取音高&正在提取特征", - "step3a:正在训练模型": "Etapa 3a: Treinamento do modelo iniciado", - "一键训练": "Treinamento com um clique", - "不显示": "不显示", - "也可批量输入音频文件, 二选一, 优先读文件夹": "Você também pode inserir arquivos de áudio em lotes. Escolha uma das duas opções. É dada prioridade à leitura da pasta.", - "人声伴奏分离批量处理， 使用UVR5模型。
合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
模型分为三类：
1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
3、去混响、去延迟模型（by FoxJoy）：
  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
 (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
去混响/去延迟，附：
1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
2、MDX-Net-Dereverb模型挺慢的；
3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。": "Processamento em lote para separação de acompanhamento vocal usando o modelo UVR5.
Exemplo de um formato de caminho de pasta válido: D:\\caminho\\para a pasta\\entrada\\ (copie-o da barra de endereços do gerenciador de arquivos).
O modelo é dividido em três categorias:
1. Preservar vocais: Escolha esta opção para áudio sem harmonias. Ele preserva os vocais melhor do que o HP5. Inclui dois modelos integrados: HP2 e HP3. O HP3 pode vazar ligeiramente o acompanhamento, mas preserva os vocais um pouco melhor do que o HP2.
2 Preservar apenas os vocais principais: Escolha esta opção para áudio com harmonias. Isso pode enfraquecer os vocais principais. Ele inclui um modelo embutido: HP5.
3. Modelos de de-reverb e de-delay (por FoxJoy):
  (1) MDX-Net: A melhor escolha para remoção de reverb estéreo, mas não pode remover reverb mono;
 (234) DeEcho: Remove efeitos de atraso. O modo agressivo remove mais completamente do que o modo normal. O DeReverb também remove reverb e pode remover reverb mono, mas não de forma muito eficaz para conteúdo de alta frequência fortemente reverberado.
Notas de de-reverb/de-delay:
1. O tempo de processamento para o modelo DeEcho-DeReverb é aproximadamente duas vezes maior que os outros dois modelos DeEcho.
2 O modelo MDX-Net-Dereverb é bastante lento.
3. A configuração mais limpa recomendada é aplicar MDX-Net primeiro e depois DeEcho-Aggressive.", - "从模型中读取": "从模型中读取", - "以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2": "Digite o (s) índice(s) da GPU separados por '-', por exemplo, 0-1-2 para usar a GPU 0, 1 e 2:", - "伴奏人声分离&去混响&去回声": "UVR5", - "使用模型采样率": "使用模型采样率", - "使用设备采样率": "使用设备采样率", - "保存名": "Salvar nome", - "保存的文件名, 默认空为和源文件同名": "Salvar nome do arquivo (padrão: igual ao arquivo de origem):", - "保存的模型名不带后缀": "Nome do modelo salvo (sem extensão):", - "保存频率save_every_epoch": "Faça backup a cada # de Epoch:", - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果": "Proteja consoantes sem voz e sons respiratórios, evite artefatos como quebra de som eletrônico e desligue-o quando estiver cheio de 0,5. Diminua-o para aumentar a proteção, mas pode reduzir o efeito de indexação:", - "信息": "信息", - "修改": "Editar", - "停止音频转换": "Conversão de áudio", - "全流程结束！": "Todos os processos foram concluídos!", - "共振偏移": "共振偏移", - "刷新音色列表和索引路径": "Atualizar lista de voz e caminho do Index", - "加载模型": "Modelo", - "加载预训练底模D路径": "Carregue o caminho D do modelo base pré-treinado:", - "加载预训练底模G路径": "Carregue o caminho G do modelo base pré-treinado:", - "单次推理": "Único", - "卸载音色省显存": "Descarregue a voz para liberar a memória da GPU:", - "变调(整数, 半音数量, 升八度12降八度-12)": "Mude o tom aqui. Se a voz for do mesmo sexo, não é necessario alterar (12 caso seja Masculino para feminino, -12 caso seja ao contrário).", - "后处理重采样至最终采样率，0为不进行重采样": "Reamostragem pós-processamento para a taxa de amostragem final, 0 significa sem reamostragem:", - "否": "Não", - "启用相位声码器": "启用相位声码器", - "响应阈值": "Limiar de resposta", - "响度因子": "Fator de volume", - "处理数据": "Processar o Conjunto de Dados", - "失败": "失败", - "实际计算": "实际计算", - "导出Onnx模型": "Exportar Modelo Onnx", - "导出文件格式": "Qual formato de arquivo você prefere?", - "封装时间": "封装时间", - "常见问题解答": "FAQ (Perguntas frequentes)", - "常规设置": "Configurações gerais", - "开始音频转换": "Iniciar conversão de áudio", - "待处理音频文件": "待处理音频文件", - "很遗憾您这没有能用的显卡来支持您训练": "Infelizmente, não há GPU compatível disponível para apoiar o seu treinamento.", - "性能设置": "Configurações de desempenho.", - "总训练轮数total_epoch": "Número total de ciclos(epoch) de treino (se escolher um valor alto demais, o seu modelo parecerá terrivelmente sobretreinado):", - "成功构建索引到": "成功构建索引到", - "批量推理": "Conversão em Lote", - "批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ": "Conversão em Massa.", - "指定输出主人声文件夹": "Especifique a pasta de saída para vocais:", - "指定输出文件夹": "Especifique a pasta de saída:", - "指定输出非主人声文件夹": "Informar a pasta de saída para acompanhamento:", - "推理时间(ms)": "Tempo de inferência (ms)", - "推理音色": "Escolha o seu Modelo:", - "提取": "Extrato", - "提取音高和处理数据使用的CPU进程数": "Número de processos de CPU usados para extração de tom e processamento de dados:", - "无": "无", - "是": "Sim", - "是否仅保存最新的ckpt文件以节省硬盘空间": "Só deve salvar apenas o arquivo ckpt mais recente para economizar espaço em disco:", - "是否在每次保存时间点将最终小模型保存至weights文件夹": "Salve um pequeno modelo final na pasta 'weights' em cada ponto de salvamento:", - "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速": "Se deve armazenar em cache todos os conjuntos de treinamento na memória de vídeo. Pequenos dados com menos de 10 minutos podem ser armazenados em cache para acelerar o treinamento, e um cache de dados grande irá explodir a memória de vídeo e não aumentar muito a velocidade:", - "显卡信息": "Informações da GPU", - "有": "有", - "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.": "
The Mangio-RVC 💻 | Tradução por Krisp e Rafael Godoy Ebert | AI HUB BRASIL
Este software é de código aberto sob a licença MIT. O autor não tem qualquer controle sobre o software. Aqueles que usam o software e divulgam os sons exportados pelo software são totalmente responsáveis.
Se você não concorda com este termo, você não pode usar ou citar nenhum código e arquivo no pacote de software. Para obter detalhes, consulte o diretório raiz O acordo a ser seguido para uso LICENSE
", - "查看": "Visualizar", - "检索特征占比": "Taxa de recurso de recuperação:", - "模型": "Modelo", - "模型作者": "模型作者", - "模型作者(可空)": "模型作者(可空)", - "模型信息": "模型信息", - "模型名": "模型名", - "模型推理": "Inference", - "模型是否带音高指导": "Se o modelo tem orientação de tom:", - "模型是否带音高指导(唱歌一定要, 语音可以不要)": "Se o modelo tem orientação de tom (necessário para cantar, opcional para fala):", - "模型是否带音高指导,1是0否": "Se o modelo tem orientação de passo (1: sim, 0: não):", - "模型版本型号": "Versão:", - "模型路径": "Caminho para o Modelo:", - "每张显卡的batch_size": "Batch Size (DEIXE COMO ESTÁ a menos que saiba o que está fazendo, no Colab pode deixar até 20!):", - "淡入淡出长度": "Comprimento de desvanecimento", - "版本": "Versão", - "特征提取": "Extrair Tom", - "特征检索库文件路径,为空则使用下拉的选择结果": "Caminho para o arquivo de Index. Deixe em branco para usar o resultado selecionado no menu debaixo:", - "独占 WASAPI 设备": "独占 WASAPI 设备", - "男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ": "Recomendado +12 chave para conversão de homem para mulher e -12 chave para conversão de mulher para homem. Se a faixa de som for muito longe e a voz estiver distorcida, você também pode ajustá-la à faixa apropriada por conta própria.", - "目标采样率": "Taxa de amostragem:", - "相似度": "相似度", - "相似度(0到1)": "相似度(0到1)", - "算法延迟(ms)": "Atrasos algorítmicos (ms)", - "自动检测index路径,下拉式选择(dropdown)": "Detecte automaticamente o caminho do Index e selecione no menu suspenso:", - "融合": "Fusão", - "要改的模型信息": "Informações do modelo a ser modificado:", - "要置入的模型信息": "Informações do modelo a ser colocado:", - "计算": "计算", - "训练": "Treinar", - "训练模型": "Treinar Modelo", - "训练特征索引": "Treinar Index", - "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": "Após o término do treinamento, você pode verificar o log de treinamento do console ou train.log na pasta de experimentos", - "设备类型": "设备类型", - "请指定说话人id": "Especifique o ID do locutor/cantor:", - "请选择index文件": "Selecione o arquivo de Index", - "请选择pth文件": "Selecione o arquivo pth", - "请选择说话人id": "Selecione Palestrantes/Cantores ID:", - "转换": "Converter", - "输入实验名": "Nome da voz:", - "输入待处理音频文件夹路径": "Caminho da pasta de áudio a ser processada:", - "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)": "Caminho da pasta de áudio a ser processada (copie-o da barra de endereços do gerenciador de arquivos):", - "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": "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:", - "输入监听": "Monitoramento de entrada", - "输入训练文件夹路径": "Caminho da pasta de treinamento:", - "输入设备": "Dispositivo de entrada", - "输入降噪": "Redução de ruído de entrada", - "输出信息": "Informação de saída", - "输出变声": "Mudança de voz de saída", - "输出设备": "Dispositivo de saída", - "输出降噪": "Redução de ruído de saída", - "输出音频(右下角三个点,点了可以下载)": "Exportar áudio (clique nos três pontos no canto inferior direito para baixar)", - "选择.index文件": "Selecione o Index", - "选择.pth文件": "Selecione o Arquivo", - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU": "Selecione o algoritmo de extração de tom \n'pm': extração mais rápida, mas discurso de qualidade inferior; \n'harvest': graves melhores, mas extremamente lentos; \n'harvest': melhor qualidade, mas extração mais lenta); 'crepe': melhor qualidade, mas intensivo em GPU; 'magio-crepe': melhor opção; 'RMVPE': um modelo robusto para estimativa de afinação vocal em música polifônica;", - "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU": "Selecione o algoritmo de extração de tom \n'pm': extração mais rápida, mas discurso de qualidade inferior; \n'harvest': graves melhores, mas extremamente lentos; \n'crepe': melhor qualidade (mas intensivo em GPU);\n rmvpe tem o melhor efeito e consome menos CPU/GPU.", - "采样率": "采样率", - "采样长度": "Comprimento da Amostra", - "重载设备列表": "Recarregar lista de dispositivos", - "链接索引到外部": "链接索引到外部", - "音调设置": "Configurações de tom", - "音频设备": "音频设备", - "音高引导(f0)": "音高引导(f0)", - "音高算法": "Algoritmo de detecção de pitch", - "额外推理时长": "Tempo extra de inferência" + "Unload model to save GPU memory": "Descarregue a voz para liberar a memória da GPU:", + "Version": "Versão", + "View": "Visualizar", + "Vocals/Accompaniment Separation & Reverberation Removal": "UVR5", + "Weight (w) for Model A": "Peso (w) para o modelo A:", + "Whether the model has pitch guidance": "Se o modelo tem orientação de tom:", + "Whether the model has pitch guidance (1: yes, 0: no)": "Se o modelo tem orientação de passo (1: sim, 0: não):", + "Whether the model has pitch guidance (required for singing, optional for speech)": "Se o modelo tem orientação de tom (necessário para cantar, opcional para fala):", + "Yes": "Sim", + "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" } diff --git a/i18n/locale/ru_RU.json b/i18n/locale/ru_RU.json index 7215f02..e9b7f6c 100644 --- a/i18n/locale/ru_RU.json +++ b/i18n/locale/ru_RU.json @@ -1,160 +1,157 @@ { - "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### Изменить информацию о модели\n> Работает только с маленькими моделями, взятыми из папки 'weights'.", - "### 查看模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### Просмотреть информацию о модели\n> Работает только с маленькими моделями, взятыми из папки 'weights'.", - "### 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": "### 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.", - "### 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", - "### 模型融合\n可用于测试音色融合": "### Слияние моделей> Может быть использовано для проверки слияния тембра.", - "### 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.": "### Шаг 1. Конфигурирование модели.\nДанные обучения модели сохраняются в папку 'logs', и для каждой модели создаётся отдельная папка. Введите вручную путь к настройкам для модели, в которой находятся логи и тренировочные файлы.", - "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.": "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.", - "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.": "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.", - "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).": "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).", - ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音": "Если значение больше 3: применить медианную фильтрацию к вытащенным тональностям. Значение контролирует радиус фильтра и может уменьшить излишнее дыхание.", - "A模型ID(长)": "A模型ID(长)", - "A模型权重": "Весы (w) модели А:", - "A模型路径": "Путь к модели А:", - "B模型ID(长)": "B模型ID(长)", - "B模型路径": "Путь к модели Б:", - "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调": "Файл дуги F0 (не обязательно). Одна тональность на каждую строчку. Заменяет обычный F0 и модуляцию тональности:", - "ID(短)": "ID(短)", + "### 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.", + "### 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 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'.", + "#### 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:", + "Algorithmic delays (ms)": "算法延迟(ms)", + "All processes have been completed!": "Все процессы завершены!", + "Audio device": "Аудиоустройство", + "Auto-detect index path and select from the dropdown": "Автоматически найденные файлы индексов черт (выберите вариант из списка):", + "Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt').": "Массовое преобразование. Введите путь к папке, в которой находятся файлы для преобразования голоса или выгрузите несколько аудиофайлов. Сконвертированные файлы будут сохранены в указанной папке (по умолчанию: 'opt').", + "Batch inference": "批量推理", + "Batch processing for vocal accompaniment separation using the UVR5 model.
Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).
The model is divided into three categories:
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.
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.
3. De-reverb and de-delay models (by FoxJoy):
  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;
 (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.
De-reverb/de-delay notes:
1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.
2. The MDX-Net-Dereverb model is quite slow.
3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive.": "Пакетная обработка для разделения вокального сопровождения с использованием модели UVR5.
Пример допустимого формата пути к папке: D:\\path\\to\\input\\folder
Модель разделена на три категории:
1. Сохранить вокал: выберите этот вариант для звука без гармоний. Он сохраняет вокал лучше, чем HP5. Он включает в себя две встроенные модели: HP2 и HP3. HP3 может немного пропускать инструментал, но сохраняет вокал немного лучше, чем HP2.
2. Сохранить только основной вокал: выберите этот вариант для звука с гармониями. Это может ослабить основной вокал. Он включает одну встроенную модель: HP5.
3. Модели удаления реверберации и задержки (от FoxJoy):
  (1) MDX-Net: лучший выбор для удаления стереореверберации, но он не может удалить монореверберацию;
 (234) DeEcho: удаляет эффекты задержки. Агрессивный режим удаляет более тщательно, чем Нормальный режим. DeReverb дополнительно удаляет реверберацию и может удалять монореверберацию, но не очень эффективно для сильно реверберированного высокочастотного контента.
Примечания по удалению реверберации/задержки:
1. Время обработки для модели DeEcho-DeReverb примерно в два раза больше, чем для двух других моделей DeEcho.
2. Модель MDX-Net-Dereverb довольно медленная.
3. Рекомендуемая самая чистая конфигурация — сначала применить MDX-Net, а затем DeEcho-Aggressive.", + "Batch size per GPU": "Размер пачки для GPU:", + "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": "Кэшировать все тренировочные сеты в видеопамять. Кэширование маленький датасетов (меньше 10 минут) может ускорить тренировку, но кэширование больших, наоборот, займёт много видеопамяти и не сильно ускорит тренировку:", + "Calculate": "计算", + "Choose sample rate of the device": "使用设备采样率", + "Choose sample rate of the model": "使用模型采样率", + "Convert": "Преобразовать", + "Device type": "设备类型", + "Enable phase vocoder": "启用相位声码器", + "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1": "Введите номера графических процессоров, разделенные символом «-», например, 0-0-1, чтобы запустить два процесса на GPU 0 и один процесс на GPU 1:", + "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2": "Введите, какие(-ую) GPU(-у) хотите использовать через '-', например 0-1-2, чтобы использовать GPU с номерами 0, 1 и 2:", + "Enter the experiment name": "Название модели:", + "Enter the path of the audio folder to be processed": "Путь к папке с аудиофайлами для обработки:", + "Enter the path of the audio folder to be processed (copy it from the address bar of the file manager)": "Путь к папке с аудиофайлами для переработки (можно скопировать путь из адресной строки файлового менеджера):", + "Enter the path of the training folder": "Путь к папке с аудиозаписями, на которых будет обучаться модель:", + "Exist": "有", + "Export Onnx": "Экспорт ONNX", + "Export Onnx Model": "Экспортировать модель", + "Export audio (click on the three dots in the lower right corner to download)": "Аудиофайл (чтобы скачать, нажмите на три точки справа в плеере)", + "Export file format": "Формат выходных файлов", + "Extra inference time": "Доп. время переработки", + "Extract": "Создать модель", + "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation": "Файл дуги F0 (не обязательно). Одна тональность на каждую строчку. Заменяет обычный F0 и модуляцию тональности:", + "FAQ (Frequently Asked Questions)": "ЧаВо (часто задаваемые вопросы)", + "Fade length": "Длина затухания", + "Fail": "失败", + "Feature extraction": "Извлечь черты", + "Formant offset": "共振偏移", + "Fusion": "Запустить слияние", + "GPU Information": "Информация о графических процессорах (GPUs):", + "General settings": "Основные настройки", + "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(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:", + "Information": "信息", + "Input device": "Входное устройство", + "Input noise reduction": "Уменьшение входного шума", + "Input voice monitor": "输入监听", + "Link index to outside folder": "链接索引到外部", + "Load model": "Загрузить модель", + "Load pre-trained base model D path": "Путь к предварительно обученной базовой модели D:", + "Load pre-trained base model G path": "Путь к предварительно обученной базовой модели G:", + "Loudness factor": "коэффициент громкости", + "Model": "Модели", + "Model Author": "模型作者", + "Model Author (Nullable)": "模型作者(可空)", + "Model Inference": "Изменение голоса", + "Model architecture version": "Версия архитектуры модели:", + "Model info": "模型信息", + "Model information to be modified": "Информация, которая будет изменена:", + "Model information to be placed": "Информация о модели:", + "Model name": "模型名", + "Modify": "Изменить", + "Multiple audio files can also be imported. If a folder path exists, this input is ignored.": "Можно также импортировать несколько аудиофайлов. Если путь к папке существует, то этот ввод игнорируется.", + "No": "Нет", "None": "None", - "Onnx导出": "Экспорт ONNX", - "Onnx输出路径": "Путь для сохранения модели в формате ONNX:", - "RVC模型路径": "Путь к модели RVC:", + "Not exist": "无", + "Number of CPU processes used for harvest pitch algorithm": "Количество процессор harvest", + "Number of CPU processes used for pitch extraction and data processing": "Число процессов ЦП, используемое для оценки высоты голоса и обработки данных:", + "One-click training": "Обучение в одно нажатие", + "Onnx Export Path": "Путь для сохранения модели в формате ONNX:", + "Output converted voice": "输出变声", + "Output device": "Выходное устройство", + "Output information": "Статистика", + "Output noise reduction": "Уменьшение выходного шума", + "Path to Model": "Путь к папке:", + "Path to Model A": "Путь к модели А:", + "Path to Model B": "Путь к модели Б:", + "Path to the feature index file. Leave blank to use the selected result from the dropdown": "Путь к файлу индекса черт. Оставьте пустым, чтобы использовать выбранный вариант из списка ниже:", + "Performance settings": "Настройки быстроты", + "Pitch detection algorithm": "Алгоритм оценки высоты звука", + "Pitch guidance (f0)": "音高引导(f0)", + "Pitch settings": "Настройка высоты звука", + "Please choose the .index file": "Пожалуйста, выберите файл индекса", + "Please choose the .pth file": "Пожалуйста, выберите файл pth", + "Please specify the speaker/singer ID": "Номер говорящего/поющего:", + "Process data": "Обработать данные", + "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": "Защитить глухие согласные и звуки дыхания для предотвращения артефактов, например, разрывания в электронной музыке. Поставьте на 0.5, чтобы выключить. Уменьшите значение для повышения защиты, но учтите, что при этом может ухудшиться точность индексирования:", + "RVC Model Path": "Путь к модели RVC:", + "Read from model": "从模型中读取", + "Refresh voice list and index path": "Обновить список голосов и индексов", + "Reload device list": "Обновить список устройств", + "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling": "Изменить частоту дискретизации в выходном файле на финальную. Поставьте 0, чтобы ничего не изменялось:", + "Response threshold": "Порог ответа", + "Sample length": "Длина сэмпла", + "Sampling rate": "采样率", + "Save a small final model to the 'weights' folder at each save point": "Сохранять маленькую финальную модель в папку 'weights' на каждой точке сохранения:", + "Save file name (default: same as the source file)": "Название сохранённого файла (по умолчанию: такое же, как и у входного):", + "Save frequency (save_every_epoch)": "Частота сохранения (save_every_epoch):", + "Save name": "Имя файла для сохранения:", + "Save only the latest '.ckpt' file to save disk space": "Сохранять только последний файл '.ckpt', чтобы сохранить место на диске:", + "Saved model name (without extension)": "Имя файла модели для сохранения (без расширения):", + "Sealing date": "封装时间", + "Search feature ratio (controls accent strength, too high has artifacting)": "Соотношение поиска черт:", + "Select Speaker/Singer ID": "Номер говорящего:", + "Select the .index file": "Выбрать файл .index", + "Select the .pth file": "Выбрать файл .pth", + "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": "Выберите алгоритм оценки высоты голоса ('pm': работает быстро, но даёт низкое качество речи; 'harvest': басы лучше, но работает очень медленно; 'crepe': лучшее качество, но сильно нагружает GPU; 'rmvpe': лучшее качество и минимальная нагрузка на GPU):", + "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": "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU", + "Similarity": "相似度", + "Similarity (from 0 to 1)": "相似度(0到1)", + "Single inference": "单次推理", + "Specify output folder": "Папка для результатов:", + "Specify the output folder for accompaniment": "Путь к папке для сохранения аккомпанемента:", + "Specify the output folder for vocals": "Путь к папке для сохранения вокала:", + "Start audio conversion": "Начать конвертацию аудио", + "Step 1: Processing data": "Шаг 1. Переработка данных", + "Step 3a: Model training started": "Шаг 3. Запуск обучения модели", + "Stop audio conversion": "Закончить конвертацию аудио", + "Successfully built index into": "成功构建索引到", + "Takeover WASAPI device": "独占 WASAPI 设备", + "Target sample rate": "Частота дискретизации аудио:", + "The audio file to be processed": "待处理音频文件", + "This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible.
If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory Agreement-LICENSE.txt for details.": "Это программное обеспечение с открытым исходным кодом распространяется по лицензии MIT. Автор никак не контролирует это программное обеспечение. Пользователи, которые используют эту программу и распространяют аудиозаписи, полученные с помощью этой программы, несут полную ответственность за это. Если вы не согласны с этим, вы не можете использовать какие-либо коды и файлы в рамках этой программы или ссылаться на них. Подробнее в файле Agreement-LICENSE.txt в корневом каталоге программы.", + "Total training epochs (total_epoch)": "Полное количество эпох (total_epoch):", + "Train": "Обучение модели", + "Train feature index": "Обучить индекс черт", + "Train model": "Обучить модель", + "Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder.": "Обучение модели завершено. Журнал обучения можно просмотреть в консоли или в файле 'train.log' в папке с моделью.", + "Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)": "Изменить высоту голоса (укажите количество полутонов; чтобы поднять голос на октаву, выберите 12, понизить на октаву — -12):", + "Unfortunately, there is no compatible GPU available to support your training.": "К сожалению, у вас нету графического процессора, который поддерживает обучение моделей.", "Unknown": "Unknown", - "ckpt处理": "Обработка ckpt", - "harvest进程数": "Количество процессор harvest", - "index文件路径不可包含中文": "Путь к файлу индекса", - "pth文件路径不可包含中文": "Путь к файлу pth", - "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程": "Введите номера графических процессоров, разделенные символом «-», например, 0-0-1, чтобы запустить два процесса на GPU 0 и один процесс на GPU 1:", - "step1:正在处理数据": "Шаг 1. Переработка данных", - "step2:正在提取音高&正在提取特征": "step2:正在提取音高&正在提取特征", - "step3a:正在训练模型": "Шаг 3. Запуск обучения модели", - "一键训练": "Обучение в одно нажатие", - "不显示": "不显示", - "也可批量输入音频文件, 二选一, 优先读文件夹": "Можно также импортировать несколько аудиофайлов. Если путь к папке существует, то этот ввод игнорируется.", - "人声伴奏分离批量处理， 使用UVR5模型。
合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
模型分为三类：
1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
3、去混响、去延迟模型（by FoxJoy）：
  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
 (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
去混响/去延迟，附：
1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
2、MDX-Net-Dereverb模型挺慢的；
3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。": "Пакетная обработка для разделения вокального сопровождения с использованием модели UVR5.
Пример допустимого формата пути к папке: D:\\path\\to\\input\\folder
Модель разделена на три категории:
1. Сохранить вокал: выберите этот вариант для звука без гармоний. Он сохраняет вокал лучше, чем HP5. Он включает в себя две встроенные модели: HP2 и HP3. HP3 может немного пропускать инструментал, но сохраняет вокал немного лучше, чем HP2.
2. Сохранить только основной вокал: выберите этот вариант для звука с гармониями. Это может ослабить основной вокал. Он включает одну встроенную модель: HP5.
3. Модели удаления реверберации и задержки (от FoxJoy):
  (1) MDX-Net: лучший выбор для удаления стереореверберации, но он не может удалить монореверберацию;
 (234) DeEcho: удаляет эффекты задержки. Агрессивный режим удаляет более тщательно, чем Нормальный режим. DeReverb дополнительно удаляет реверберацию и может удалять монореверберацию, но не очень эффективно для сильно реверберированного высокочастотного контента.
Примечания по удалению реверберации/задержки:
1. Время обработки для модели DeEcho-DeReverb примерно в два раза больше, чем для двух других моделей DeEcho.
2. Модель MDX-Net-Dereverb довольно медленная.
3. Рекомендуемая самая чистая конфигурация — сначала применить MDX-Net, а затем DeEcho-Aggressive.", - "从模型中读取": "从模型中读取", - "以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2": "Введите, какие(-ую) GPU(-у) хотите использовать через '-', например 0-1-2, чтобы использовать GPU с номерами 0, 1 и 2:", - "伴奏人声分离&去混响&去回声": "Разделение вокала/аккомпанемента и удаление эхо", - "使用模型采样率": "使用模型采样率", - "使用设备采样率": "使用设备采样率", - "保存名": "Имя файла для сохранения:", - "保存的文件名, 默认空为和源文件同名": "Название сохранённого файла (по умолчанию: такое же, как и у входного):", - "保存的模型名不带后缀": "Имя файла модели для сохранения (без расширения):", - "保存频率save_every_epoch": "Частота сохранения (save_every_epoch):", - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果": "Защитить глухие согласные и звуки дыхания для предотвращения артефактов, например, разрывания в электронной музыке. Поставьте на 0.5, чтобы выключить. Уменьшите значение для повышения защиты, но учтите, что при этом может ухудшиться точность индексирования:", - "信息": "信息", - "修改": "Изменить", - "停止音频转换": "Закончить конвертацию аудио", - "全流程结束！": "Все процессы завершены!", - "共振偏移": "共振偏移", - "刷新音色列表和索引路径": "Обновить список голосов и индексов", - "加载模型": "Загрузить модель", - "加载预训练底模D路径": "Путь к предварительно обученной базовой модели D:", - "加载预训练底模G路径": "Путь к предварительно обученной базовой модели G:", - "单次推理": "单次推理", - "卸载音色省显存": "Выгрузить модель из памяти GPU для освобождения ресурсов", - "变调(整数, 半音数量, 升八度12降八度-12)": "Изменить высоту голоса (укажите количество полутонов; чтобы поднять голос на октаву, выберите 12, понизить на октаву — -12):", - "后处理重采样至最终采样率，0为不进行重采样": "Изменить частоту дискретизации в выходном файле на финальную. Поставьте 0, чтобы ничего не изменялось:", - "否": "Нет", - "启用相位声码器": "启用相位声码器", - "响应阈值": "Порог ответа", - "响度因子": "коэффициент громкости", - "处理数据": "Обработать данные", - "失败": "失败", - "实际计算": "实际计算", - "导出Onnx模型": "Экспортировать модель", - "导出文件格式": "Формат выходных файлов", - "封装时间": "封装时间", - "常见问题解答": "ЧаВо (часто задаваемые вопросы)", - "常规设置": "Основные настройки", - "开始音频转换": "Начать конвертацию аудио", - "待处理音频文件": "待处理音频文件", - "很遗憾您这没有能用的显卡来支持您训练": "К сожалению, у вас нету графического процессора, который поддерживает обучение моделей.", - "性能设置": "Настройки быстроты", - "总训练轮数total_epoch": "Полное количество эпох (total_epoch):", - "成功构建索引到": "成功构建索引到", - "批量推理": "批量推理", - "批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ": "Массовое преобразование. Введите путь к папке, в которой находятся файлы для преобразования голоса или выгрузите несколько аудиофайлов. Сконвертированные файлы будут сохранены в указанной папке (по умолчанию: 'opt').", - "指定输出主人声文件夹": "Путь к папке для сохранения вокала:", - "指定输出文件夹": "Папка для результатов:", - "指定输出非主人声文件夹": "Путь к папке для сохранения аккомпанемента:", - "推理时间(ms)": "Время переработки (мс)", - "推理音色": "Желаемый голос:", - "提取": "Создать модель", - "提取音高和处理数据使用的CPU进程数": "Число процессов ЦП, используемое для оценки высоты голоса и обработки данных:", - "无": "无", - "是": "Да", - "是否仅保存最新的ckpt文件以节省硬盘空间": "Сохранять только последний файл '.ckpt', чтобы сохранить место на диске:", - "是否在每次保存时间点将最终小模型保存至weights文件夹": "Сохранять маленькую финальную модель в папку 'weights' на каждой точке сохранения:", - "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速": "Кэшировать все тренировочные сеты в видеопамять. Кэширование маленький датасетов (меньше 10 минут) может ускорить тренировку, но кэширование больших, наоборот, займёт много видеопамяти и не сильно ускорит тренировку:", - "显卡信息": "Информация о графических процессорах (GPUs):", - "有": "有", - "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.": "Это программное обеспечение с открытым исходным кодом распространяется по лицензии MIT. Автор никак не контролирует это программное обеспечение. Пользователи, которые используют эту программу и распространяют аудиозаписи, полученные с помощью этой программы, несут полную ответственность за это. Если вы не согласны с этим, вы не можете использовать какие-либо коды и файлы в рамках этой программы или ссылаться на них. Подробнее в файле Agreement-LICENSE.txt в корневом каталоге программы.", - "查看": "Просмотреть информацию", - "检索特征占比": "Соотношение поиска черт:", - "模型": "Модели", - "模型作者": "模型作者", - "模型作者(可空)": "模型作者(可空)", - "模型信息": "模型信息", - "模型名": "模型名", - "模型推理": "Изменение голоса", - "模型是否带音高指导": "Поддерживает ли модель изменение высоты голоса (1: да, 0: нет):", - "模型是否带音高指导(唱歌一定要, 语音可以不要)": "Поддержка изменения высоты звука (обязательно для пения, необязательно для речи):", - "模型是否带音高指导,1是0否": "Поддерживает ли модель изменение высоты голоса (1: да, 0: нет):", - "模型版本型号": "Версия архитектуры модели:", - "模型路径": "Путь к папке:", - "每张显卡的batch_size": "Размер пачки для GPU:", - "淡入淡出长度": "Длина затухания", - "版本": "Версия архитектуры модели:", - "特征提取": "Извлечь черты", - "特征检索库文件路径,为空则使用下拉的选择结果": "Путь к файлу индекса черт. Оставьте пустым, чтобы использовать выбранный вариант из списка ниже:", - "独占 WASAPI 设备": "独占 WASAPI 设备", - "男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ": "Рекомендуется выбрать +12 для конвертирования мужского голоса в женский и -12 для конвертирования женского в мужской. Если диапазон голоса слишком велик, и голос искажается, можно выбрать значение на свой вкус.", - "目标采样率": "Частота дискретизации аудио:", - "相似度": "相似度", - "相似度(0到1)": "相似度(0到1)", - "算法延迟(ms)": "算法延迟(ms)", - "自动检测index路径,下拉式选择(dropdown)": "Автоматически найденные файлы индексов черт (выберите вариант из списка):", - "融合": "Запустить слияние", - "要改的模型信息": "Информация, которая будет изменена:", - "要置入的模型信息": "Информация о модели:", - "计算": "计算", - "训练": "Обучение модели", - "训练模型": "Обучить модель", - "训练特征索引": "Обучить индекс черт", - "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": "Обучение модели завершено. Журнал обучения можно просмотреть в консоли или в файле 'train.log' в папке с моделью.", - "设备类型": "设备类型", - "请指定说话人id": "Номер говорящего/поющего:", - "请选择index文件": "Пожалуйста, выберите файл индекса", - "请选择pth文件": "Пожалуйста, выберите файл pth", - "请选择说话人id": "Номер говорящего:", - "转换": "Преобразовать", - "输入实验名": "Название модели:", - "输入待处理音频文件夹路径": "Путь к папке с аудиофайлами для обработки:", - "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)": "Путь к папке с аудиофайлами для переработки (можно скопировать путь из адресной строки файлового менеджера):", - "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": "Использовать громкость входного файла для замены или перемешивания с громкостью выходного файла. Чем ближе соотношение к 1, тем больше используется звука из выходного файла:", - "输入监听": "输入监听", - "输入训练文件夹路径": "Путь к папке с аудиозаписями, на которых будет обучаться модель:", - "输入设备": "Входное устройство", - "输入降噪": "Уменьшение входного шума", - "输出信息": "Статистика", - "输出变声": "输出变声", - "输出设备": "Выходное устройство", - "输出降噪": "Уменьшение выходного шума", - "输出音频(右下角三个点,点了可以下载)": "Аудиофайл (чтобы скачать, нажмите на три точки справа в плеере)", - "选择.index文件": "Выбрать файл .index", - "选择.pth文件": "Выбрать файл .pth", - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU": "Выберите алгоритм оценки высоты голоса ('pm': работает быстро, но даёт низкое качество речи; 'harvest': басы лучше, но работает очень медленно; 'crepe': лучшее качество, но сильно нагружает GPU; 'rmvpe': лучшее качество и минимальная нагрузка на GPU):", - "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU": "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU", - "采样率": "采样率", - "采样长度": "Длина сэмпла", - "重载设备列表": "Обновить список устройств", - "链接索引到外部": "链接索引到外部", - "音调设置": "Настройка высоты звука", - "音频设备": "Аудиоустройство", - "音高引导(f0)": "音高引导(f0)", - "音高算法": "Алгоритм оценки высоты звука", - "额外推理时长": "Доп. время переработки" + "Unload model to save GPU memory": "Выгрузить модель из памяти GPU для освобождения ресурсов", + "Version": "Версия архитектуры модели:", + "View": "Просмотреть информацию", + "Vocals/Accompaniment Separation & Reverberation Removal": "Разделение вокала/аккомпанемента и удаление эхо", + "Weight (w) for Model A": "Весы (w) модели А:", + "Whether the model has pitch guidance": "Поддерживает ли модель изменение высоты голоса (1: да, 0: нет):", + "Whether the model has pitch guidance (1: yes, 0: no)": "Поддерживает ли модель изменение высоты голоса (1: да, 0: нет):", + "Whether the model has pitch guidance (required for singing, optional for speech)": "Поддержка изменения высоты звука (обязательно для пения, необязательно для речи):", + "Yes": "Да", + "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" } diff --git a/i18n/locale/tr_TR.json b/i18n/locale/tr_TR.json index b05150f..5bf105a 100644 --- a/i18n/locale/tr_TR.json +++ b/i18n/locale/tr_TR.json @@ -1,160 +1,157 @@ { - "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### Model bilgilerini düzenle\n> Sadece 'weights' klasöründen çıkarılan küçük model dosyaları desteklenir.", - "### 查看模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### Model bilgilerini görüntüle\n> Sadece 'weights' klasöründen çıkarılan küçük model dosyaları desteklenir.", - "### 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 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.", - "### 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", - "### 模型融合\n可用于测试音色融合": "### Model birleştirme\nSes rengi birleştirmesi için kullanılabilir.", - "### 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.": "### Adım 1. Deneysel yapılandırmayı doldurun.\nDeneysel veriler 'logs' klasöründe saklanır ve her bir deney için ayrı bir klasör vardır. Deneysel adı yolu manuel olarak girin; bu yol, deneysel yapılandırmayı, günlükleri ve eğitilmiş model dosyalarını içerir.", - "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.": "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.", - "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.": "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.", - "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).": "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).", - ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音": "Eğer >=3 ise, elde edilen pitch sonuçlarına median filtreleme uygula. Bu değer, filtre yarıçapını temsil eder ve nefesliliği azaltabilir.", - "A模型ID(长)": "A模型ID(长)", - "A模型权重": "A Modeli Ağırlığı:", - "A模型路径": "A Modeli Yolu:", - "B模型ID(长)": "B模型ID(长)", - "B模型路径": "B Modeli Yolu:", - "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调": "F0 eğrisi dosyası (isteğe bağlı). Her satırda bir pitch değeri bulunur. Varsayılan F0 ve pitch modülasyonunu değiştirir:", - "ID(短)": "ID(短)", + "### 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.", + "### 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 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.", + "#### 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:", + "Algorithmic delays (ms)": "算法延迟(ms)", + "All processes have been completed!": "Tüm işlemler tamamlandı!", + "Audio device": "Ses cihazı", + "Auto-detect index path and select from the dropdown": "İndeks yolunu otomatik olarak tespit et ve açılır menüden seçim yap.", + "Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt').": "Toplu dönüştür. Dönüştürülecek ses dosyalarının bulunduğu klasörü girin veya birden çok ses dosyasını yükleyin. Dönüştürülen ses dosyaları belirtilen klasöre ('opt' varsayılan olarak) dönüştürülecektir", + "Batch inference": "批量推理", + "Batch processing for vocal accompaniment separation using the UVR5 model.
Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).
The model is divided into three categories:
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.
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.
3. De-reverb and de-delay models (by FoxJoy):
  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;
 (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.
De-reverb/de-delay notes:
1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.
2. The MDX-Net-Dereverb model is quite slow.
3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive.": "Batch işleme kullanarak vokal eşlik ayrımı için UVR5 modeli kullanılır.
Geçerli bir klasör yol formatı örneği: D:\\path\\to\\input\\folder (dosya yöneticisi adres çubuğundan kopyalanır).
Model üç kategoriye ayrılır:
1. Vokalleri koru: Bu seçeneği, harmoni içermeyen sesler için kullanın. HP5'ten daha iyi bir şekilde vokalleri korur. İki dahili model içerir: HP2 ve HP3. HP3, eşlik sesini hafifçe sızdırabilir, ancak vokalleri HP2'den biraz daha iyi korur.
2. Sadece ana vokalleri koru: Bu seçeneği, harmoni içeren sesler için kullanın. Ana vokalleri zayıflatabilir. Bir dahili model içerir: HP5.
3. Reverb ve gecikme modelleri (FoxJoy tarafından):
  (1) MDX-Net: Stereo reverb'i kaldırmak için en iyi seçenek, ancak mono reverb'i kaldıramaz;
 (234) DeEcho: Gecikme efektlerini kaldırır. Agresif mod, Normal moda göre daha kapsamlı bir şekilde kaldırma yapar. DeReverb ayrıca reverb'i kaldırır ve mono reverb'i kaldırabilir, ancak yoğun yankılı yüksek frekanslı içerikler için çok etkili değildir.
Reverb/gecikme notları:
1. DeEcho-DeReverb modelinin işleme süresi diğer iki DeEcho modeline göre yaklaşık olarak iki kat daha uzundur.
2. MDX-Net-Dereverb modeli oldukça yavaştır.
3. Tavsiye edilen en temiz yapılandırma önce MDX-Net'i uygulamak ve ardından DeEcho-Aggressive uygulamaktır.", + "Batch size per GPU": "Her GPU için yığın boyutu (batch_size):", + "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": "Tüm eğitim verilerini GPU belleğine önbelleğe alıp almayacağınızı belirtin. Küçük veri setlerini (10 dakikadan az) önbelleğe almak eğitimi hızlandırabilir, ancak büyük veri setlerini önbelleğe almak çok fazla GPU belleği tüketir ve çok fazla hız artışı sağlamaz:", + "Calculate": "计算", + "Choose sample rate of the device": "使用设备采样率", + "Choose sample rate of the model": "使用模型采样率", + "Convert": "Dönüştür", + "Device type": "设备类型", + "Enable phase vocoder": "启用相位声码器", + "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1": "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程", + "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2": "GPU indekslerini '-' ile ayırarak girin, örneğin 0-1-2, GPU 0, 1 ve 2'yi kullanmak için:", + "Enter the experiment name": "Deneysel adı girin:", + "Enter the path of the audio folder to be processed": "İşlenecek ses klasörünün yolunu girin:", + "Enter the path of the audio folder to be processed (copy it from the address bar of the file manager)": "İşlenecek ses klasörünün yolunu girin (dosya yöneticisinin adres çubuğundan kopyalayın):", + "Enter the path of the training folder": "Eğitim klasörünün yolunu girin:", + "Exist": "有", + "Export Onnx": "Onnx Dışa Aktar", + "Export Onnx Model": "Onnx Modeli Dışa Aktar", + "Export audio (click on the three dots in the lower right corner to download)": "Ses dosyasını dışa aktar (indirmek için sağ alt köşedeki üç noktaya tıklayın)", + "Export file format": "Dışa aktarma dosya formatı", + "Extra inference time": "Ekstra çıkartma süresi", + "Extract": "Çıkart", + "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation": "F0 eğrisi dosyası (isteğe bağlı). Her satırda bir pitch değeri bulunur. Varsayılan F0 ve pitch modülasyonunu değiştirir:", + "FAQ (Frequently Asked Questions)": "Sıkça Sorulan Sorular (SSS)", + "Fade length": "Geçiş (Fade) uzunluğu", + "Fail": "失败", + "Feature extraction": "Özellik çıkartma", + "Formant offset": "共振偏移", + "Fusion": "Birleştir", + "GPU Information": "GPU Bilgisi", + "General settings": "Genel ayarlar", + "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(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):", + "Information": "信息", + "Input device": "Giriş cihazı", + "Input noise reduction": "Giriş gürültü azaltma", + "Input voice monitor": "输入监听", + "Link index to outside folder": "链接索引到外部", + "Load model": "Model yükle", + "Load pre-trained base model D path": "Önceden eğitilmiş temel D modelini yükleme yolu:", + "Load pre-trained base model G path": "Önceden eğitilmiş temel G modelini yükleme yolu:", + "Loudness factor": "ses yüksekliği faktörü", + "Model": "Model", + "Model Author": "模型作者", + "Model Author (Nullable)": "模型作者(可空)", + "Model Inference": "Model çıkartma (Inference)", + "Model architecture version": "Model mimari versiyonu:", + "Model info": "模型信息", + "Model information to be modified": "Düzenlenecek model bilgileri:", + "Model information to be placed": "Eklemek için model bilgileri:", + "Model name": "模型名", + "Modify": "Düzenle", + "Multiple audio files can also be imported. If a folder path exists, this input is ignored.": "Ses dosyaları ayrıca toplu olarak, iki seçimle, öncelikli okuma klasörüyle içe aktarılabilir", + "No": "Hayır", "None": "None", - "Onnx导出": "Onnx Dışa Aktar", - "Onnx输出路径": "Onnx Dışa Aktarım Yolu:", - "RVC模型路径": "RVC Model Yolu:", + "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": "Ses yüksekliği çıkartmak (Pitch) ve verileri işlemek için kullanılacak CPU işlemci sayısı:", + "One-click training": "Tek Tuşla Eğit", + "Onnx Export Path": "Onnx Dışa Aktarım Yolu:", + "Output converted voice": "输出变声", + "Output device": "Çıkış cihazı", + "Output information": "Çıkış bilgisi", + "Output noise reduction": "Çıkış gürültü azaltma", + "Path to Model": "Model Yolu:", + "Path to Model A": "A Modeli Yolu:", + "Path to Model B": "B Modeli Yolu:", + "Path to the feature index file. Leave blank to use the selected result from the dropdown": "Özellik indeksi dosyasının yolunu belirtin. Seçilen sonucu kullanmak için boş bırakın veya açılır menüden seçim yapın.", + "Performance settings": "Performans ayarları", + "Pitch detection algorithm": "音高算法", + "Pitch guidance (f0)": "音高引导(f0)", + "Pitch settings": "Pitch ayarları", + "Please choose the .index file": "Lütfen .index dosyası seçin", + "Please choose the .pth file": "Lütfen .pth dosyası seçin", + "Please specify the speaker/singer ID": "Lütfen konuşmacı/sanatçı no belirtin:", + "Process data": "Verileri işle", + "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": "Sessiz ünsüzleri ve nefes seslerini koruyarak elektronik müzikte yırtılma gibi sanal hataların oluşmasını engeller. 0.5 olarak ayarlandığında devre dışı kalır. Değerin azaltılması korumayı artırabilir, ancak indeksleme doğruluğunu azaltabilir:", + "RVC Model Path": "RVC Model Yolu:", + "Read from model": "从模型中读取", + "Refresh voice list and index path": "Ses listesini ve indeks yolunu yenile", + "Reload device list": "Cihaz listesini yeniden yükle", + "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling": "Son işleme aşamasında çıktı sesini son örnekleme hızına yeniden örnekle. 0 değeri için yeniden örnekleme yapılmaz:", + "Response threshold": "Tepki eşiği", + "Sample length": "Örnekleme uzunluğu", + "Sampling rate": "采样率", + "Save a small final model to the 'weights' folder at each save point": "Her kaydetme noktasında son küçük bir modeli 'weights' klasörüne kaydetmek için:", + "Save file name (default: same as the source file)": "Kaydedilecek dosya adı (varsayılan: kaynak dosya ile aynı):", + "Save frequency (save_every_epoch)": "Kaydetme sıklığı (save_every_epoch):", + "Save name": "Kaydetme Adı:", + "Save only the latest '.ckpt' file to save disk space": "Sadece en son '.ckpt' dosyasını kaydet:", + "Saved model name (without extension)": "Kaydedilecek model adı (uzantı olmadan):", + "Sealing date": "封装时间", + "Search feature ratio (controls accent strength, too high has artifacting)": "Arama özelliği oranı (vurgu gücünü kontrol eder, çok yüksek olması sanal etkilere neden olur)", + "Select Speaker/Singer ID": "Konuşmacı/Şarkıcı No seçin:", + "Select the .index file": ".index dosyası seç", + "Select the .pth file": ".pth dosyası seç", + "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": "Pitch algoritmasını seçin ('pm': daha hızlı çıkarır ancak daha düşük kaliteli konuşma; 'harvest': daha iyi konuşma sesi ancak son derece yavaş; 'crepe': daha da iyi kalite ancak GPU yoğunluğu gerektirir):", + "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": "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU", + "Similarity": "相似度", + "Similarity (from 0 to 1)": "相似度(0到1)", + "Single inference": "单次推理", + "Specify output folder": "Çıkış klasörünü belirt:", + "Specify the output folder for accompaniment": "Müzik ve diğer sesler için çıkış klasörünü belirtin:", + "Specify the output folder for vocals": "Vokal için çıkış klasörünü belirtin:", + "Start audio conversion": "Ses dönüştürmeyi başlat", + "Step 1: Processing data": "Adım 1: Veri işleme", + "Step 3a: Model training started": "Adım 3a: Model eğitimi başladı", + "Stop audio conversion": "Ses dönüştürmeyi durdur", + "Successfully built index into": "成功构建索引到", + "Takeover WASAPI device": "独占 WASAPI 设备", + "Target sample rate": "Hedef örnekleme oranı:", + "The audio file to be processed": "待处理音频文件", + "This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible.
If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory Agreement-LICENSE.txt for details.": "Bu yazılım, MIT lisansı altında açık kaynaklıdır. Yazarın yazılım üzerinde herhangi bir kontrolü yoktur. Yazılımı kullanan ve yazılım tarafından dışa aktarılan sesleri dağıtan kullanıcılar sorumludur.
Eğer bu maddeyle aynı fikirde değilseniz, yazılım paketi içindeki herhangi bir kod veya dosyayı kullanamaz veya referans göremezsiniz. Detaylar için kök dizindeki Agreement-LICENSE.txt dosyasına bakınız.", + "Total training epochs (total_epoch)": "Toplam eğitim turu (total_epoch):", + "Train": "Eğitim", + "Train feature index": "Özellik Dizinini Eğit", + "Train model": "Modeli Eğit", + "Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder.": "Eğitim tamamlandı. Eğitim günlüklerini konsolda veya deney klasörü altındaki train.log dosyasında kontrol edebilirsiniz.", + "Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)": "Transpoze et (tamsayı, yarıton sayısıyla; bir oktav yükseltmek için: 12, bir oktav düşürmek için: -12):", + "Unfortunately, there is no compatible GPU available to support your training.": "Maalesef, eğitiminizi desteklemek için uyumlu bir GPU bulunmamaktadır.", "Unknown": "Unknown", - "ckpt处理": "ckpt İşleme", - "harvest进程数": "harvest进程数", - "index文件路径不可包含中文": ".index dosya yolu Çince karakter içeremez", - "pth文件路径不可包含中文": ".pth dosya yolu Çince karakter içeremez", - "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程": "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程", - "step1:正在处理数据": "Adım 1: Veri işleme", - "step2:正在提取音高&正在提取特征": "step2:正在提取音高&正在提取特征", - "step3a:正在训练模型": "Adım 3a: Model eğitimi başladı", - "一键训练": "Tek Tuşla Eğit", - "不显示": "不显示", - "也可批量输入音频文件, 二选一, 优先读文件夹": "Ses dosyaları ayrıca toplu olarak, iki seçimle, öncelikli okuma klasörüyle içe aktarılabilir", - "人声伴奏分离批量处理， 使用UVR5模型。
合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
模型分为三类：
1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
3、去混响、去延迟模型（by FoxJoy）：
  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
 (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
去混响/去延迟，附：
1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
2、MDX-Net-Dereverb模型挺慢的；
3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。": "Batch işleme kullanarak vokal eşlik ayrımı için UVR5 modeli kullanılır.
Geçerli bir klasör yol formatı örneği: D:\\path\\to\\input\\folder (dosya yöneticisi adres çubuğundan kopyalanır).
Model üç kategoriye ayrılır:
1. Vokalleri koru: Bu seçeneği, harmoni içermeyen sesler için kullanın. HP5'ten daha iyi bir şekilde vokalleri korur. İki dahili model içerir: HP2 ve HP3. HP3, eşlik sesini hafifçe sızdırabilir, ancak vokalleri HP2'den biraz daha iyi korur.
2. Sadece ana vokalleri koru: Bu seçeneği, harmoni içeren sesler için kullanın. Ana vokalleri zayıflatabilir. Bir dahili model içerir: HP5.
3. Reverb ve gecikme modelleri (FoxJoy tarafından):
  (1) MDX-Net: Stereo reverb'i kaldırmak için en iyi seçenek, ancak mono reverb'i kaldıramaz;
 (234) DeEcho: Gecikme efektlerini kaldırır. Agresif mod, Normal moda göre daha kapsamlı bir şekilde kaldırma yapar. DeReverb ayrıca reverb'i kaldırır ve mono reverb'i kaldırabilir, ancak yoğun yankılı yüksek frekanslı içerikler için çok etkili değildir.
Reverb/gecikme notları:
1. DeEcho-DeReverb modelinin işleme süresi diğer iki DeEcho modeline göre yaklaşık olarak iki kat daha uzundur.
2. MDX-Net-Dereverb modeli oldukça yavaştır.
3. Tavsiye edilen en temiz yapılandırma önce MDX-Net'i uygulamak ve ardından DeEcho-Aggressive uygulamaktır.", - "从模型中读取": "从模型中读取", - "以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2": "GPU indekslerini '-' ile ayırarak girin, örneğin 0-1-2, GPU 0, 1 ve 2'yi kullanmak için:", - "伴奏人声分离&去混响&去回声": "Vokal/Müzik Ayrıştırma ve Yankı Giderme", - "使用模型采样率": "使用模型采样率", - "使用设备采样率": "使用设备采样率", - "保存名": "Kaydetme Adı:", - "保存的文件名, 默认空为和源文件同名": "Kaydedilecek dosya adı (varsayılan: kaynak dosya ile aynı):", - "保存的模型名不带后缀": "Kaydedilecek model adı (uzantı olmadan):", - "保存频率save_every_epoch": "Kaydetme sıklığı (save_every_epoch):", - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果": "Sessiz ünsüzleri ve nefes seslerini koruyarak elektronik müzikte yırtılma gibi sanal hataların oluşmasını engeller. 0.5 olarak ayarlandığında devre dışı kalır. Değerin azaltılması korumayı artırabilir, ancak indeksleme doğruluğunu azaltabilir:", - "信息": "信息", - "修改": "Düzenle", - "停止音频转换": "Ses dönüştürmeyi durdur", - "全流程结束！": "Tüm işlemler tamamlandı!", - "共振偏移": "共振偏移", - "刷新音色列表和索引路径": "Ses listesini ve indeks yolunu yenile", - "加载模型": "Model yükle", - "加载预训练底模D路径": "Önceden eğitilmiş temel D modelini yükleme yolu:", - "加载预训练底模G路径": "Önceden eğitilmiş temel G modelini yükleme yolu:", - "单次推理": "单次推理", - "卸载音色省显存": "GPU bellek kullanımını azaltmak için sesi kaldır", - "变调(整数, 半音数量, 升八度12降八度-12)": "Transpoze et (tamsayı, yarıton sayısıyla; bir oktav yükseltmek için: 12, bir oktav düşürmek için: -12):", - "后处理重采样至最终采样率，0为不进行重采样": "Son işleme aşamasında çıktı sesini son örnekleme hızına yeniden örnekle. 0 değeri için yeniden örnekleme yapılmaz:", - "否": "Hayır", - "启用相位声码器": "启用相位声码器", - "响应阈值": "Tepki eşiği", - "响度因子": "ses yüksekliği faktörü", - "处理数据": "Verileri işle", - "失败": "失败", - "实际计算": "实际计算", - "导出Onnx模型": "Onnx Modeli Dışa Aktar", - "导出文件格式": "Dışa aktarma dosya formatı", - "封装时间": "封装时间", - "常见问题解答": "Sıkça Sorulan Sorular (SSS)", - "常规设置": "Genel ayarlar", - "开始音频转换": "Ses dönüştürmeyi başlat", - "待处理音频文件": "待处理音频文件", - "很遗憾您这没有能用的显卡来支持您训练": "Maalesef, eğitiminizi desteklemek için uyumlu bir GPU bulunmamaktadır.", - "性能设置": "Performans ayarları", - "总训练轮数total_epoch": "Toplam eğitim turu (total_epoch):", - "成功构建索引到": "成功构建索引到", - "批量推理": "批量推理", - "批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ": "Toplu dönüştür. Dönüştürülecek ses dosyalarının bulunduğu klasörü girin veya birden çok ses dosyasını yükleyin. Dönüştürülen ses dosyaları belirtilen klasöre ('opt' varsayılan olarak) dönüştürülecektir", - "指定输出主人声文件夹": "Vokal için çıkış klasörünü belirtin:", - "指定输出文件夹": "Çıkış klasörünü belirt:", - "指定输出非主人声文件夹": "Müzik ve diğer sesler için çıkış klasörünü belirtin:", - "推理时间(ms)": "Çıkarsama süresi (ms)", - "推理音色": "Ses çıkartma (Inference):", - "提取": "Çıkart", - "提取音高和处理数据使用的CPU进程数": "Ses yüksekliği çıkartmak (Pitch) ve verileri işlemek için kullanılacak CPU işlemci sayısı:", - "无": "无", - "是": "Evet", - "是否仅保存最新的ckpt文件以节省硬盘空间": "Sadece en son '.ckpt' dosyasını kaydet:", - "是否在每次保存时间点将最终小模型保存至weights文件夹": "Her kaydetme noktasında son küçük bir modeli 'weights' klasörüne kaydetmek için:", - "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速": "Tüm eğitim verilerini GPU belleğine önbelleğe alıp almayacağınızı belirtin. Küçük veri setlerini (10 dakikadan az) önbelleğe almak eğitimi hızlandırabilir, ancak büyük veri setlerini önbelleğe almak çok fazla GPU belleği tüketir ve çok fazla hız artışı sağlamaz:", - "显卡信息": "GPU Bilgisi", - "有": "有", - "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.": "Bu yazılım, MIT lisansı altında açık kaynaklıdır. Yazarın yazılım üzerinde herhangi bir kontrolü yoktur. Yazılımı kullanan ve yazılım tarafından dışa aktarılan sesleri dağıtan kullanıcılar sorumludur.
Eğer bu maddeyle aynı fikirde değilseniz, yazılım paketi içindeki herhangi bir kod veya dosyayı kullanamaz veya referans göremezsiniz. Detaylar için kök dizindeki Agreement-LICENSE.txt dosyasına bakınız.", - "查看": "Görüntüle", - "检索特征占比": "Arama özelliği oranı (vurgu gücünü kontrol eder, çok yüksek olması sanal etkilere neden olur)", - "模型": "Model", - "模型作者": "模型作者", - "模型作者(可空)": "模型作者(可空)", - "模型信息": "模型信息", - "模型名": "模型名", - "模型推理": "Model çıkartma (Inference)", - "模型是否带音高指导": "Modelin ses yüksekliği rehberi içerip içermediği:", - "模型是否带音高指导(唱歌一定要, 语音可以不要)": "Modelin ses yüksekliği (Pitch) rehberliği içerip içermediği (şarkı söyleme için şarttır, konuşma için isteğe bağlıdır):", - "模型是否带音高指导,1是0否": "Modelin ses yüksekliği rehberi içerip içermediği (1: evet, 0: hayır):", - "模型版本型号": "Model mimari versiyonu:", - "模型路径": "Model Yolu:", - "每张显卡的batch_size": "Her GPU için yığın boyutu (batch_size):", - "淡入淡出长度": "Geçiş (Fade) uzunluğu", - "版本": "Sürüm", - "特征提取": "Özellik çıkartma", - "特征检索库文件路径,为空则使用下拉的选择结果": "Özellik indeksi dosyasının yolunu belirtin. Seçilen sonucu kullanmak için boş bırakın veya açılır menüden seçim yapın.", - "独占 WASAPI 设备": "独占 WASAPI 设备", - "男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ": "Erkekten kadına çevirmek için +12 tuş önerilir, kadından erkeğe çevirmek için ise -12 tuş önerilir. Eğer ses aralığı çok fazla genişler ve ses bozulursa, isteğe bağlı olarak uygun aralığa kendiniz de ayarlayabilirsiniz.", - "目标采样率": "Hedef örnekleme oranı:", - "相似度": "相似度", - "相似度(0到1)": "相似度(0到1)", - "算法延迟(ms)": "算法延迟(ms)", - "自动检测index路径,下拉式选择(dropdown)": "İndeks yolunu otomatik olarak tespit et ve açılır menüden seçim yap.", - "融合": "Birleştir", - "要改的模型信息": "Düzenlenecek model bilgileri:", - "要置入的模型信息": "Eklemek için model bilgileri:", - "计算": "计算", - "训练": "Eğitim", - "训练模型": "Modeli Eğit", - "训练特征索引": "Özellik Dizinini Eğit", - "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": "Eğitim tamamlandı. Eğitim günlüklerini konsolda veya deney klasörü altındaki train.log dosyasında kontrol edebilirsiniz.", - "设备类型": "设备类型", - "请指定说话人id": "Lütfen konuşmacı/sanatçı no belirtin:", - "请选择index文件": "Lütfen .index dosyası seçin", - "请选择pth文件": "Lütfen .pth dosyası seçin", - "请选择说话人id": "Konuşmacı/Şarkıcı No seçin:", - "转换": "Dönüştür", - "输入实验名": "Deneysel adı girin:", - "输入待处理音频文件夹路径": "İşlenecek ses klasörünün yolunu girin:", - "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)": "İşlenecek ses klasörünün yolunu girin (dosya yöneticisinin adres çubuğundan kopyalayın):", - "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": "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:", - "输入监听": "输入监听", - "输入训练文件夹路径": "Eğitim klasörünün yolunu girin:", - "输入设备": "Giriş cihazı", - "输入降噪": "Giriş gürültü azaltma", - "输出信息": "Çıkış bilgisi", - "输出变声": "输出变声", - "输出设备": "Çıkış cihazı", - "输出降噪": "Çıkış gürültü azaltma", - "输出音频(右下角三个点,点了可以下载)": "Ses dosyasını dışa aktar (indirmek için sağ alt köşedeki üç noktaya tıklayın)", - "选择.index文件": ".index dosyası seç", - "选择.pth文件": ".pth dosyası seç", - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU": "Pitch algoritmasını seçin ('pm': daha hızlı çıkarır ancak daha düşük kaliteli konuşma; 'harvest': daha iyi konuşma sesi ancak son derece yavaş; 'crepe': daha da iyi kalite ancak GPU yoğunluğu gerektirir):", - "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU": "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU", - "采样率": "采样率", - "采样长度": "Örnekleme uzunluğu", - "重载设备列表": "Cihaz listesini yeniden yükle", - "链接索引到外部": "链接索引到外部", - "音调设置": "Pitch ayarları", - "音频设备": "Ses cihazı", - "音高引导(f0)": "音高引导(f0)", - "音高算法": "音高算法", - "额外推理时长": "Ekstra çıkartma süresi" + "Unload model to save GPU memory": "GPU bellek kullanımını azaltmak için sesi kaldır", + "Version": "Sürüm", + "View": "Görüntüle", + "Vocals/Accompaniment Separation & Reverberation Removal": "Vokal/Müzik Ayrıştırma ve Yankı Giderme", + "Weight (w) for Model A": "A Modeli Ağırlığı:", + "Whether the model has pitch guidance": "Modelin ses yüksekliği rehberi içerip içermediği:", + "Whether the model has pitch guidance (1: yes, 0: no)": "Modelin ses yüksekliği rehberi içerip içermediği (1: evet, 0: hayır):", + "Whether the model has pitch guidance (required for singing, optional for speech)": "Modelin ses yüksekliği (Pitch) rehberliği içerip içermediği (şarkı söyleme için şarttır, konuşma için isteğe bağlıdır):", + "Yes": "Evet", + "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" } diff --git a/i18n/locale/zh_CN.json b/i18n/locale/zh_CN.json index 9c774ad..5b85d39 100644 --- a/i18n/locale/zh_CN.json +++ b/i18n/locale/zh_CN.json @@ -1,160 +1,157 @@ { - "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件", - "### 查看模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### 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": "### 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", - "### 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", - "### 模型融合\n可用于测试音色融合": "### 模型融合\n可用于测试音色融合", - "### 第一步 填写实验配置\n实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件.": "### 第一步 填写实验配置\n实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件.", - "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.": "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.", - "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.": "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.", - "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).": "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).", - ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音": ">=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", - "A模型ID(长)": "A模型ID(长)", - "A模型权重": "A模型权重", - "A模型路径": "A模型路径", - "B模型ID(长)": "B模型ID(长)", - "B模型路径": "B模型路径", - "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调": "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调", - "ID(短)": "ID(短)", + "### 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", + "### 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 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", + "#### 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", + "Algorithmic delays (ms)": "算法延迟(ms)", + "All processes have been completed!": "全流程结束！", + "Audio device": "音频设备", + "Auto-detect index path and select from the dropdown": "自动检测index路径,下拉式选择(dropdown)", + "Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt').": "批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ", + "Batch inference": "批量推理", + "Batch processing for vocal accompaniment separation using the UVR5 model.
Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).
The model is divided into three categories:
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.
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.
3. De-reverb and de-delay models (by FoxJoy):
  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;
 (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.
De-reverb/de-delay notes:
1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.
2. The MDX-Net-Dereverb model is quite slow.
3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive.": "人声伴奏分离批量处理， 使用UVR5模型。
合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
模型分为三类：
1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
3、去混响、去延迟模型（by FoxJoy）：
  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
 (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
去混响/去延迟，附：
1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
2、MDX-Net-Dereverb模型挺慢的；
3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。", + "Batch size per GPU": "每张显卡的batch_size", + "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": "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速", + "Calculate": "计算", + "Choose sample rate of the device": "使用设备采样率", + "Choose sample rate of the model": "使用模型采样率", + "Convert": "转换", + "Device type": "设备类型", + "Enable phase vocoder": "启用相位声码器", + "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1": "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程", + "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2": "以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2", + "Enter the experiment name": "输入实验名", + "Enter the path of the audio folder to be processed": "输入待处理音频文件夹路径", + "Enter the path of the audio folder to be processed (copy it from the address bar of the file manager)": "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)", + "Enter the path of the training folder": "输入训练文件夹路径", + "Exist": "有", + "Export Onnx": "Onnx导出", + "Export Onnx Model": "导出Onnx模型", + "Export audio (click on the three dots in the lower right corner to download)": "输出音频(右下角三个点,点了可以下载)", + "Export file format": "导出文件格式", + "Extra inference time": "额外推理时长", + "Extract": "提取", + "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation": "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调", + "FAQ (Frequently Asked Questions)": "常见问题解答", + "Fade length": "淡入淡出长度", + "Fail": "失败", + "Feature extraction": "特征提取", + "Formant offset": "共振偏移", + "Fusion": "融合", + "GPU Information": "显卡信息", + "General settings": "常规设置", + "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(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", + "Information": "信息", + "Input device": "输入设备", + "Input noise reduction": "输入降噪", + "Input voice monitor": "输入监听", + "Link index to outside folder": "链接索引到外部", + "Load model": "加载模型", + "Load pre-trained base model D path": "加载预训练底模D路径", + "Load pre-trained base model G path": "加载预训练底模G路径", + "Loudness factor": "响度因子", + "Model": "模型", + "Model Author": "模型作者", + "Model Author (Nullable)": "模型作者(可空)", + "Model Inference": "模型推理", + "Model architecture version": "模型版本型号", + "Model info": "模型信息", + "Model information to be modified": "要改的模型信息", + "Model information to be placed": "要置入的模型信息", + "Model name": "模型名", + "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", - "Onnx导出": "Onnx导出", - "Onnx输出路径": "Onnx输出路径", - "RVC模型路径": "RVC模型路径", + "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", + "One-click training": "一键训练", + "Onnx Export Path": "Onnx输出路径", + "Output converted voice": "输出变声", + "Output device": "输出设备", + "Output information": "输出信息", + "Output noise reduction": "输出降噪", + "Path to Model": "模型路径", + "Path to Model A": "A模型路径", + "Path to Model B": "B模型路径", + "Path to the feature index file. Leave blank to use the selected result from the dropdown": "特征检索库文件路径,为空则使用下拉的选择结果", + "Performance settings": "性能设置", + "Pitch detection algorithm": "音高算法", + "Pitch guidance (f0)": "音高引导(f0)", + "Pitch settings": "音调设置", + "Please choose the .index file": "请选择index文件", + "Please choose the .pth file": "请选择pth文件", + "Please specify the speaker/singer ID": "请指定说话人id", + "Process data": "处理数据", + "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": "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果", + "RVC Model Path": "RVC模型路径", + "Read from model": "从模型中读取", + "Refresh voice list and index path": "刷新音色列表和索引路径", + "Reload device list": "重载设备列表", + "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling": "后处理重采样至最终采样率，0为不进行重采样", + "Response threshold": "响应阈值", + "Sample length": "采样长度", + "Sampling rate": "采样率", + "Save a small final model to the 'weights' folder at each save point": "是否在每次保存时间点将最终小模型保存至weights文件夹", + "Save file name (default: same as the source file)": "保存的文件名, 默认空为和源文件同名", + "Save frequency (save_every_epoch)": "保存频率save_every_epoch", + "Save name": "保存名", + "Save only the latest '.ckpt' file to save disk space": "是否仅保存最新的ckpt文件以节省硬盘空间", + "Saved model name (without extension)": "保存的模型名不带后缀", + "Sealing date": "封装时间", + "Search feature ratio (controls accent strength, too high has artifacting)": "检索特征占比", + "Select Speaker/Singer ID": "请选择说话人id", + "Select the .index file": "选择.index文件", + "Select the .pth file": "选择.pth文件", + "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": "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU", + "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": "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU", + "Similarity": "相似度", + "Similarity (from 0 to 1)": "相似度(0到1)", + "Single inference": "单次推理", + "Specify output folder": "指定输出文件夹", + "Specify the output folder for accompaniment": "指定输出非主人声文件夹", + "Specify the output folder for vocals": "指定输出主人声文件夹", + "Start audio conversion": "开始音频转换", + "Step 1: Processing data": "step1:正在处理数据", + "Step 3a: Model training started": "step3a:正在训练模型", + "Stop audio conversion": "停止音频转换", + "Successfully built index into": "成功构建索引到", + "Takeover WASAPI device": "独占 WASAPI 设备", + "Target sample rate": "目标采样率", + "The audio file to be processed": "待处理音频文件", + "This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible.
If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory Agreement-LICENSE.txt for details.": "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.", + "Total training epochs (total_epoch)": "总训练轮数total_epoch", + "Train": "训练", + "Train feature index": "训练特征索引", + "Train model": "训练模型", + "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", - "ckpt处理": "ckpt处理", - "harvest进程数": "harvest进程数", - "index文件路径不可包含中文": "index文件路径不可包含中文", - "pth文件路径不可包含中文": "pth文件路径不可包含中文", - "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程": "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程", - "step1:正在处理数据": "step1:正在处理数据", - "step2:正在提取音高&正在提取特征": "step2:正在提取音高&正在提取特征", - "step3a:正在训练模型": "step3a:正在训练模型", - "一键训练": "一键训练", - "不显示": "不显示", - "也可批量输入音频文件, 二选一, 优先读文件夹": "也可批量输入音频文件, 二选一, 优先读文件夹", - "人声伴奏分离批量处理， 使用UVR5模型。
合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
模型分为三类：
1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
3、去混响、去延迟模型（by FoxJoy）：
  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
 (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
去混响/去延迟，附：
1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
2、MDX-Net-Dereverb模型挺慢的；
3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。": "人声伴奏分离批量处理， 使用UVR5模型。
合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
模型分为三类：
1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
3、去混响、去延迟模型（by FoxJoy）：
  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
 (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
去混响/去延迟，附：
1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
2、MDX-Net-Dereverb模型挺慢的；
3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。", - "从模型中读取": "从模型中读取", - "以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2": "以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2", - "伴奏人声分离&去混响&去回声": "伴奏人声分离&去混响&去回声", - "使用模型采样率": "使用模型采样率", - "使用设备采样率": "使用设备采样率", - "保存名": "保存名", - "保存的文件名, 默认空为和源文件同名": "保存的文件名, 默认空为和源文件同名", - "保存的模型名不带后缀": "保存的模型名不带后缀", - "保存频率save_every_epoch": "保存频率save_every_epoch", - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果": "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果", - "信息": "信息", - "修改": "修改", - "停止音频转换": "停止音频转换", - "全流程结束！": "全流程结束！", - "共振偏移": "共振偏移", - "刷新音色列表和索引路径": "刷新音色列表和索引路径", - "加载模型": "加载模型", - "加载预训练底模D路径": "加载预训练底模D路径", - "加载预训练底模G路径": "加载预训练底模G路径", - "单次推理": "单次推理", - "卸载音色省显存": "卸载音色省显存", - "变调(整数, 半音数量, 升八度12降八度-12)": "变调(整数, 半音数量, 升八度12降八度-12)", - "后处理重采样至最终采样率，0为不进行重采样": "后处理重采样至最终采样率，0为不进行重采样", - "否": "否", - "启用相位声码器": "启用相位声码器", - "响应阈值": "响应阈值", - "响度因子": "响度因子", - "处理数据": "处理数据", - "失败": "失败", - "实际计算": "实际计算", - "导出Onnx模型": "导出Onnx模型", - "导出文件格式": "导出文件格式", - "封装时间": "封装时间", - "常见问题解答": "常见问题解答", - "常规设置": "常规设置", - "开始音频转换": "开始音频转换", - "待处理音频文件": "待处理音频文件", - "很遗憾您这没有能用的显卡来支持您训练": "很遗憾您这没有能用的显卡来支持您训练", - "性能设置": "性能设置", - "总训练轮数total_epoch": "总训练轮数total_epoch", - "成功构建索引到": "成功构建索引到", - "批量推理": "批量推理", - "批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ": "批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ", - "指定输出主人声文件夹": "指定输出主人声文件夹", - "指定输出文件夹": "指定输出文件夹", - "指定输出非主人声文件夹": "指定输出非主人声文件夹", - "推理时间(ms)": "推理时间(ms)", - "推理音色": "推理音色", - "提取": "提取", - "提取音高和处理数据使用的CPU进程数": "提取音高和处理数据使用的CPU进程数", - "无": "无", - "是": "是", - "是否仅保存最新的ckpt文件以节省硬盘空间": "是否仅保存最新的ckpt文件以节省硬盘空间", - "是否在每次保存时间点将最终小模型保存至weights文件夹": "是否在每次保存时间点将最终小模型保存至weights文件夹", - "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速": "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速", - "显卡信息": "显卡信息", - "有": "有", - "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.": "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.", - "查看": "查看", - "检索特征占比": "检索特征占比", - "模型": "模型", - "模型作者": "模型作者", - "模型作者(可空)": "模型作者(可空)", - "模型信息": "模型信息", - "模型名": "模型名", - "模型推理": "模型推理", - "模型是否带音高指导": "模型是否带音高指导", - "模型是否带音高指导(唱歌一定要, 语音可以不要)": "模型是否带音高指导(唱歌一定要, 语音可以不要)", - "模型是否带音高指导,1是0否": "模型是否带音高指导,1是0否", - "模型版本型号": "模型版本型号", - "模型路径": "模型路径", - "每张显卡的batch_size": "每张显卡的batch_size", - "淡入淡出长度": "淡入淡出长度", - "版本": "版本", - "特征提取": "特征提取", - "特征检索库文件路径,为空则使用下拉的选择结果": "特征检索库文件路径,为空则使用下拉的选择结果", - "独占 WASAPI 设备": "独占 WASAPI 设备", - "男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ": "男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ", - "目标采样率": "目标采样率", - "相似度": "相似度", - "相似度(0到1)": "相似度(0到1)", - "算法延迟(ms)": "算法延迟(ms)", - "自动检测index路径,下拉式选择(dropdown)": "自动检测index路径,下拉式选择(dropdown)", - "融合": "融合", - "要改的模型信息": "要改的模型信息", - "要置入的模型信息": "要置入的模型信息", - "计算": "计算", - "训练": "训练", - "训练模型": "训练模型", - "训练特征索引": "训练特征索引", - "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": "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", - "设备类型": "设备类型", - "请指定说话人id": "请指定说话人id", - "请选择index文件": "请选择index文件", - "请选择pth文件": "请选择pth文件", - "请选择说话人id": "请选择说话人id", - "转换": "转换", - "输入实验名": "输入实验名", - "输入待处理音频文件夹路径": "输入待处理音频文件夹路径", - "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)": "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)", - "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": "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", - "输入监听": "输入监听", - "输入训练文件夹路径": "输入训练文件夹路径", - "输入设备": "输入设备", - "输入降噪": "输入降噪", - "输出信息": "输出信息", - "输出变声": "输出变声", - "输出设备": "输出设备", - "输出降噪": "输出降噪", - "输出音频(右下角三个点,点了可以下载)": "输出音频(右下角三个点,点了可以下载)", - "选择.index文件": "选择.index文件", - "选择.pth文件": "选择.pth文件", - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU": "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU", - "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU": "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU", - "采样率": "采样率", - "采样长度": "采样长度", - "重载设备列表": "重载设备列表", - "链接索引到外部": "链接索引到外部", - "音调设置": "音调设置", - "音频设备": "音频设备", - "音高引导(f0)": "音高引导(f0)", - "音高算法": "音高算法", - "额外推理时长": "额外推理时长" + "Unload model to save GPU memory": "卸载音色省显存", + "Version": "版本", + "View": "查看", + "Vocals/Accompaniment Separation & Reverberation Removal": "伴奏人声分离&去混响&去回声", + "Weight (w) for Model A": "A模型权重", + "Whether the model has pitch guidance": "模型是否带音高指导", + "Whether the model has pitch guidance (1: yes, 0: no)": "模型是否带音高指导,1是0否", + "Whether the model has pitch guidance (required for singing, optional for speech)": "模型是否带音高指导(唱歌一定要, 语音可以不要)", + "Yes": "是", + "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" } diff --git a/i18n/locale/zh_HK.json b/i18n/locale/zh_HK.json index b628585..bd7aa7b 100644 --- a/i18n/locale/zh_HK.json +++ b/i18n/locale/zh_HK.json @@ -1,160 +1,157 @@ { - "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件", - "### 查看模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### 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": "### 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", - "### 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", - "### 模型融合\n可用于测试音色融合": "### 模型融合\n可用于测试音色融合", - "### 第一步 填写实验配置\n实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件.": "### 第一步 填写实验配置\n实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件.", - "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.": "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.", - "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.": "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.", - "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).": "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).", - ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音": ">=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", - "A模型ID(长)": "A模型ID(长)", - "A模型权重": "A模型權重", - "A模型路径": "A模型路徑", - "B模型ID(长)": "B模型ID(长)", - "B模型路径": "B模型路徑", - "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调": "F0曲線檔案，可選，一行一個音高，代替預設的F0及升降調", - "ID(短)": "ID(短)", + "### 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.", + "### 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 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", + "#### 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", + "Algorithmic delays (ms)": "算法延遲(ms)", + "All processes have been completed!": "全流程结束！", + "Audio device": "音訊設備", + "Auto-detect index path and select from the dropdown": "自動檢測index路徑,下拉式選擇(dropdown)", + "Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt').": "批量轉換，輸入待轉換音頻資料夾，或上傳多個音頻檔案，在指定資料夾(默認opt)下輸出轉換的音頻。", + "Batch inference": "批量推理", + "Batch processing for vocal accompaniment separation using the UVR5 model.
Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).
The model is divided into three categories:
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.
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.
3. De-reverb and de-delay models (by FoxJoy):
  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;
 (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.
De-reverb/de-delay notes:
1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.
2. The MDX-Net-Dereverb model is quite slow.
3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive.": "使用UVR5模型進行人聲伴奏分離的批次處理。
有效資料夾路徑格式的例子：D:\\path\\to\\input\\folder（從檔案管理員地址欄複製）。
模型分為三類：
1. 保留人聲：選擇這個選項適用於沒有和聲的音訊。它比HP5更好地保留了人聲。它包括兩個內建模型：HP2和HP3。HP3可能輕微漏出伴奏，但比HP2更好地保留了人聲；
2. 僅保留主人聲：選擇這個選項適用於有和聲的音訊。它可能會削弱主人聲。它包括一個內建模型：HP5。
3. 消除混響和延遲模型（由FoxJoy提供）：
  (1) MDX-Net：對於立體聲混響的移除是最好的選擇，但不能移除單聲道混響；
 (234) DeEcho：移除延遲效果。Aggressive模式比Normal模式移除得更徹底。DeReverb另外移除混響，可以移除單聲道混響，但對於高頻重的板式混響移除不乾淨。
消除混響/延遲注意事項：
1. DeEcho-DeReverb模型的處理時間是其他兩個DeEcho模型的近兩倍；
2. MDX-Net-Dereverb模型相當慢；
3. 個人推薦的最乾淨配置是先使用MDX-Net，然後使用DeEcho-Aggressive。", + "Batch size per GPU": "每张显卡的batch_size", + "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": "是否緩存所有訓練集至 VRAM。小於10分鐘的小數據可緩存以加速訓練，大數據緩存會爆 VRAM 也加不了多少速度", + "Calculate": "计算", + "Choose sample rate of the device": "使用设备采样率", + "Choose sample rate of the model": "使用模型采样率", + "Convert": "轉換", + "Device type": "设备类型", + "Enable phase vocoder": "启用相位声码器", + "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1": "rmvpe卡號配置：以-分隔輸入使用的不同進程卡號,例如0-0-1使用在卡0上跑2個進程並在卡1上跑1個進程", + "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2": "以-分隔輸入使用的卡號, 例如 0-1-2 使用卡0和卡1和卡2", + "Enter the experiment name": "輸入實驗名稱", + "Enter the path of the audio folder to be processed": "輸入待處理音頻資料夾路徑", + "Enter the path of the audio folder to be processed (copy it from the address bar of the file manager)": "輸入待處理音頻資料夾路徑(去檔案管理器地址欄拷貝即可)", + "Enter the path of the training folder": "輸入訓練檔案夾路徑", + "Exist": "有", + "Export Onnx": "Onnx导出", + "Export Onnx Model": "导出Onnx模型", + "Export audio (click on the three dots in the lower right corner to download)": "輸出音頻(右下角三個點，點了可以下載)", + "Export file format": "導出檔格式", + "Extra inference time": "額外推理時長", + "Extract": "提取", + "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation": "F0曲線檔案，可選，一行一個音高，代替預設的F0及升降調", + "FAQ (Frequently Asked Questions)": "常見問題解答", + "Fade length": "淡入淡出長度", + "Fail": "失败", + "Feature extraction": "特徵提取", + "Formant offset": "共振偏移", + "Fusion": "融合", + "GPU Information": "顯示卡資訊", + "General settings": "一般設定", + "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(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", + "Information": "信息", + "Input device": "輸入設備", + "Input noise reduction": "輸入降噪", + "Input voice monitor": "输入监听", + "Link index to outside folder": "链接索引到外部", + "Load model": "載入模型", + "Load pre-trained base model D path": "加載預訓練底模D路徑", + "Load pre-trained base model G path": "加載預訓練底模G路徑", + "Loudness factor": "響度因子", + "Model": "模型", + "Model Author": "模型作者", + "Model Author (Nullable)": "模型作者(可空)", + "Model Inference": "模型推理", + "Model architecture version": "模型版本型號", + "Model info": "模型信息", + "Model information to be modified": "要改的模型資訊", + "Model information to be placed": "要置入的模型資訊", + "Model name": "模型名", + "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", - "Onnx导出": "Onnx导出", - "Onnx输出路径": "Onnx输出路径", - "RVC模型路径": "RVC模型路径", + "Not exist": "无", + "Number of CPU processes used for harvest pitch algorithm": "harvest進程數", + "Number of CPU processes used for pitch extraction and data processing": "提取音高和處理數據使用的CPU進程數", + "One-click training": "一鍵訓練", + "Onnx Export Path": "Onnx输出路径", + "Output converted voice": "输出变声", + "Output device": "輸出設備", + "Output information": "輸出訊息", + "Output noise reduction": "輸出降噪", + "Path to Model": "模型路徑", + "Path to Model A": "A模型路徑", + "Path to Model B": "B模型路徑", + "Path to the feature index file. Leave blank to use the selected result from the dropdown": "特徵檢索庫檔路徑,為空則使用下拉的選擇結果", + "Performance settings": "效能設定", + "Pitch detection algorithm": "音高演算法", + "Pitch guidance (f0)": "音高引导(f0)", + "Pitch settings": "音調設定", + "Please choose the .index file": "请选择index文件", + "Please choose the .pth file": "请选择pth文件", + "Please specify the speaker/singer ID": "請指定說話人id", + "Process data": "處理資料", + "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": "保護清輔音和呼吸聲，防止電音撕裂等artifact，拉滿0.5不開啟，調低加大保護力度但可能降低索引效果", + "RVC Model Path": "RVC模型路径", + "Read from model": "从模型中读取", + "Refresh voice list and index path": "刷新音色列表和索引路徑", + "Reload device list": "重載設備列表", + "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling": "後處理重採樣至最終採樣率，0為不進行重採樣", + "Response threshold": "響應閾值", + "Sample length": "取樣長度", + "Sampling rate": "采样率", + "Save a small final model to the 'weights' folder at each save point": "是否在每次保存時間點將最終小模型保存至weights檔夾", + "Save file name (default: same as the source file)": "儲存的檔案名，預設空為與來源檔案同名", + "Save frequency (save_every_epoch)": "保存頻率save_every_epoch", + "Save name": "儲存名", + "Save only the latest '.ckpt' file to save disk space": "是否僅保存最新的ckpt檔案以節省硬碟空間", + "Saved model name (without extension)": "儲存的模型名不帶副檔名", + "Sealing date": "封装时间", + "Search feature ratio (controls accent strength, too high has artifacting)": "檢索特徵佔比", + "Select Speaker/Singer ID": "請選擇說話人ID", + "Select the .index file": "選擇 .index 檔案", + "Select the .pth file": "選擇 .pth 檔案", + "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": "選擇音高提取演算法,輸入歌聲可用pm提速,harvest低音好但巨慢無比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU", + "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": "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU", + "Similarity": "相似度", + "Similarity (from 0 to 1)": "相似度(0到1)", + "Single inference": "单次推理", + "Specify output folder": "指定輸出資料夾", + "Specify the output folder for accompaniment": "指定输出非主人声文件夹", + "Specify the output folder for vocals": "指定输出主人声文件夹", + "Start audio conversion": "開始音訊轉換", + "Step 1: Processing data": "step1:正在处理数据", + "Step 3a: Model training started": "step3a:正在训练模型", + "Stop audio conversion": "停止音訊轉換", + "Successfully built index into": "成功构建索引到", + "Takeover WASAPI device": "独占 WASAPI 设备", + "Target sample rate": "目標取樣率", + "The audio file to be processed": "待处理音频文件", + "This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible.
If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory Agreement-LICENSE.txt for details.": "本軟體以MIT協議開源，作者不對軟體具備任何控制力，使用軟體者、傳播軟體導出的聲音者自負全責。
如不認可該條款，則不能使用或引用軟體包內任何程式碼和檔案。詳見根目錄使用需遵守的協議-LICENSE.txt。", + "Total training epochs (total_epoch)": "總訓練輪數total_epoch", + "Train": "訓練", + "Train feature index": "訓練特徵索引", + "Train model": "訓練模型", + "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)": "變調(整數、半音數量、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", - "ckpt处理": "ckpt處理", - "harvest进程数": "harvest進程數", - "index文件路径不可包含中文": "index文件路径不可包含中文", - "pth文件路径不可包含中文": "pth文件路径不可包含中文", - "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程": "rmvpe卡號配置：以-分隔輸入使用的不同進程卡號,例如0-0-1使用在卡0上跑2個進程並在卡1上跑1個進程", - "step1:正在处理数据": "step1:正在处理数据", - "step2:正在提取音高&正在提取特征": "step2:正在提取音高&正在提取特征", - "step3a:正在训练模型": "step3a:正在训练模型", - "一键训练": "一鍵訓練", - "不显示": "不显示", - "也可批量输入音频文件, 二选一, 优先读文件夹": "也可批量输入音频文件, 二选一, 优先读文件夹", - "人声伴奏分离批量处理， 使用UVR5模型。
合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
模型分为三类：
1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
3、去混响、去延迟模型（by FoxJoy）：
  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
 (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
去混响/去延迟，附：
1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
2、MDX-Net-Dereverb模型挺慢的；
3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。": "使用UVR5模型進行人聲伴奏分離的批次處理。
有效資料夾路徑格式的例子：D:\\path\\to\\input\\folder（從檔案管理員地址欄複製）。
模型分為三類：
1. 保留人聲：選擇這個選項適用於沒有和聲的音訊。它比HP5更好地保留了人聲。它包括兩個內建模型：HP2和HP3。HP3可能輕微漏出伴奏，但比HP2更好地保留了人聲；
2. 僅保留主人聲：選擇這個選項適用於有和聲的音訊。它可能會削弱主人聲。它包括一個內建模型：HP5。
3. 消除混響和延遲模型（由FoxJoy提供）：
  (1) MDX-Net：對於立體聲混響的移除是最好的選擇，但不能移除單聲道混響；
 (234) DeEcho：移除延遲效果。Aggressive模式比Normal模式移除得更徹底。DeReverb另外移除混響，可以移除單聲道混響，但對於高頻重的板式混響移除不乾淨。
消除混響/延遲注意事項：
1. DeEcho-DeReverb模型的處理時間是其他兩個DeEcho模型的近兩倍；
2. MDX-Net-Dereverb模型相當慢；
3. 個人推薦的最乾淨配置是先使用MDX-Net，然後使用DeEcho-Aggressive。", - "从模型中读取": "从模型中读取", - "以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2": "以-分隔輸入使用的卡號, 例如 0-1-2 使用卡0和卡1和卡2", - "伴奏人声分离&去混响&去回声": "伴奏人聲分離&去混響&去回聲", - "使用模型采样率": "使用模型采样率", - "使用设备采样率": "使用设备采样率", - "保存名": "儲存名", - "保存的文件名, 默认空为和源文件同名": "儲存的檔案名，預設空為與來源檔案同名", - "保存的模型名不带后缀": "儲存的模型名不帶副檔名", - "保存频率save_every_epoch": "保存頻率save_every_epoch", - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果": "保護清輔音和呼吸聲，防止電音撕裂等artifact，拉滿0.5不開啟，調低加大保護力度但可能降低索引效果", - "信息": "信息", - "修改": "修改", - "停止音频转换": "停止音訊轉換", - "全流程结束！": "全流程结束！", - "共振偏移": "共振偏移", - "刷新音色列表和索引路径": "刷新音色列表和索引路徑", - "加载模型": "載入模型", - "加载预训练底模D路径": "加載預訓練底模D路徑", - "加载预训练底模G路径": "加載預訓練底模G路徑", - "单次推理": "单次推理", - "卸载音色省显存": "卸載音色節省 VRAM", - "变调(整数, 半音数量, 升八度12降八度-12)": "變調(整數、半音數量、升八度12降八度-12)", - "后处理重采样至最终采样率，0为不进行重采样": "後處理重採樣至最終採樣率，0為不進行重採樣", - "否": "否", - "启用相位声码器": "启用相位声码器", - "响应阈值": "響應閾值", - "响度因子": "響度因子", - "处理数据": "處理資料", - "失败": "失败", - "实际计算": "实际计算", - "导出Onnx模型": "导出Onnx模型", - "导出文件格式": "導出檔格式", - "封装时间": "封装时间", - "常见问题解答": "常見問題解答", - "常规设置": "一般設定", - "开始音频转换": "開始音訊轉換", - "待处理音频文件": "待处理音频文件", - "很遗憾您这没有能用的显卡来支持您训练": "很遗憾您这没有能用的显卡来支持您训练", - "性能设置": "效能設定", - "总训练轮数total_epoch": "總訓練輪數total_epoch", - "成功构建索引到": "成功构建索引到", - "批量推理": "批量推理", - "批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ": "批量轉換，輸入待轉換音頻資料夾，或上傳多個音頻檔案，在指定資料夾(默認opt)下輸出轉換的音頻。", - "指定输出主人声文件夹": "指定输出主人声文件夹", - "指定输出文件夹": "指定輸出資料夾", - "指定输出非主人声文件夹": "指定输出非主人声文件夹", - "推理时间(ms)": "推理時間(ms)", - "推理音色": "推理音色", - "提取": "提取", - "提取音高和处理数据使用的CPU进程数": "提取音高和處理數據使用的CPU進程數", - "无": "无", - "是": "是", - "是否仅保存最新的ckpt文件以节省硬盘空间": "是否僅保存最新的ckpt檔案以節省硬碟空間", - "是否在每次保存时间点将最终小模型保存至weights文件夹": "是否在每次保存時間點將最終小模型保存至weights檔夾", - "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速": "是否緩存所有訓練集至 VRAM。小於10分鐘的小數據可緩存以加速訓練，大數據緩存會爆 VRAM 也加不了多少速度", - "显卡信息": "顯示卡資訊", - "有": "有", - "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.": "本軟體以MIT協議開源，作者不對軟體具備任何控制力，使用軟體者、傳播軟體導出的聲音者自負全責。
如不認可該條款，則不能使用或引用軟體包內任何程式碼和檔案。詳見根目錄使用需遵守的協議-LICENSE.txt。", - "查看": "查看", - "检索特征占比": "檢索特徵佔比", - "模型": "模型", - "模型作者": "模型作者", - "模型作者(可空)": "模型作者(可空)", - "模型信息": "模型信息", - "模型名": "模型名", - "模型推理": "模型推理", - "模型是否带音高指导": "模型是否帶音高指導", - "模型是否带音高指导(唱歌一定要, 语音可以不要)": "模型是否帶音高指導（唱歌一定要，語音可以不要）", - "模型是否带音高指导,1是0否": "模型是否帶音高指導，1是0否", - "模型版本型号": "模型版本型號", - "模型路径": "模型路徑", - "每张显卡的batch_size": "每张显卡的batch_size", - "淡入淡出长度": "淡入淡出長度", - "版本": "版本", - "特征提取": "特徵提取", - "特征检索库文件路径,为空则使用下拉的选择结果": "特徵檢索庫檔路徑,為空則使用下拉的選擇結果", - "独占 WASAPI 设备": "独占 WASAPI 设备", - "男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ": "男性轉女性推薦+12key，女性轉男性推薦-12key，如果音域爆炸導致音色失真也可以自己調整到合適音域。", - "目标采样率": "目標取樣率", - "相似度": "相似度", - "相似度(0到1)": "相似度(0到1)", - "算法延迟(ms)": "算法延遲(ms)", - "自动检测index路径,下拉式选择(dropdown)": "自動檢測index路徑,下拉式選擇(dropdown)", - "融合": "融合", - "要改的模型信息": "要改的模型資訊", - "要置入的模型信息": "要置入的模型資訊", - "计算": "计算", - "训练": "訓練", - "训练模型": "訓練模型", - "训练特征索引": "訓練特徵索引", - "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": "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", - "设备类型": "设备类型", - "请指定说话人id": "請指定說話人id", - "请选择index文件": "请选择index文件", - "请选择pth文件": "请选择pth文件", - "请选择说话人id": "請選擇說話人ID", - "转换": "轉換", - "输入实验名": "輸入實驗名稱", - "输入待处理音频文件夹路径": "輸入待處理音頻資料夾路徑", - "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)": "輸入待處理音頻資料夾路徑(去檔案管理器地址欄拷貝即可)", - "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": "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", - "输入监听": "输入监听", - "输入训练文件夹路径": "輸入訓練檔案夾路徑", - "输入设备": "輸入設備", - "输入降噪": "輸入降噪", - "输出信息": "輸出訊息", - "输出变声": "输出变声", - "输出设备": "輸出設備", - "输出降噪": "輸出降噪", - "输出音频(右下角三个点,点了可以下载)": "輸出音頻(右下角三個點，點了可以下載)", - "选择.index文件": "選擇 .index 檔案", - "选择.pth文件": "選擇 .pth 檔案", - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU": "選擇音高提取演算法,輸入歌聲可用pm提速,harvest低音好但巨慢無比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU", - "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU": "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU", - "采样率": "采样率", - "采样长度": "取樣長度", - "重载设备列表": "重載設備列表", - "链接索引到外部": "链接索引到外部", - "音调设置": "音調設定", - "音频设备": "音訊設備", - "音高引导(f0)": "音高引导(f0)", - "音高算法": "音高演算法", - "额外推理时长": "額外推理時長" + "Unload model to save GPU memory": "卸載音色節省 VRAM", + "Version": "版本", + "View": "查看", + "Vocals/Accompaniment Separation & Reverberation Removal": "伴奏人聲分離&去混響&去回聲", + "Weight (w) for Model A": "A模型權重", + "Whether the model has pitch guidance": "模型是否帶音高指導", + "Whether the model has pitch guidance (1: yes, 0: no)": "模型是否帶音高指導，1是0否", + "Whether the model has pitch guidance (required for singing, optional for speech)": "模型是否帶音高指導（唱歌一定要，語音可以不要）", + "Yes": "是", + "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" } diff --git a/i18n/locale/zh_SG.json b/i18n/locale/zh_SG.json index b628585..bd7aa7b 100644 --- a/i18n/locale/zh_SG.json +++ b/i18n/locale/zh_SG.json @@ -1,160 +1,157 @@ { - "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件", - "### 查看模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### 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": "### 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", - "### 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", - "### 模型融合\n可用于测试音色融合": "### 模型融合\n可用于测试音色融合", - "### 第一步 填写实验配置\n实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件.": "### 第一步 填写实验配置\n实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件.", - "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.": "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.", - "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.": "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.", - "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).": "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).", - ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音": ">=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", - "A模型ID(长)": "A模型ID(长)", - "A模型权重": "A模型權重", - "A模型路径": "A模型路徑", - "B模型ID(长)": "B模型ID(长)", - "B模型路径": "B模型路徑", - "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调": "F0曲線檔案，可選，一行一個音高，代替預設的F0及升降調", - "ID(短)": "ID(短)", + "### 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.", + "### 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 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", + "#### 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", + "Algorithmic delays (ms)": "算法延遲(ms)", + "All processes have been completed!": "全流程结束！", + "Audio device": "音訊設備", + "Auto-detect index path and select from the dropdown": "自動檢測index路徑,下拉式選擇(dropdown)", + "Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt').": "批量轉換，輸入待轉換音頻資料夾，或上傳多個音頻檔案，在指定資料夾(默認opt)下輸出轉換的音頻。", + "Batch inference": "批量推理", + "Batch processing for vocal accompaniment separation using the UVR5 model.
Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).
The model is divided into three categories:
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.
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.
3. De-reverb and de-delay models (by FoxJoy):
  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;
 (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.
De-reverb/de-delay notes:
1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.
2. The MDX-Net-Dereverb model is quite slow.
3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive.": "使用UVR5模型進行人聲伴奏分離的批次處理。
有效資料夾路徑格式的例子：D:\\path\\to\\input\\folder（從檔案管理員地址欄複製）。
模型分為三類：
1. 保留人聲：選擇這個選項適用於沒有和聲的音訊。它比HP5更好地保留了人聲。它包括兩個內建模型：HP2和HP3。HP3可能輕微漏出伴奏，但比HP2更好地保留了人聲；
2. 僅保留主人聲：選擇這個選項適用於有和聲的音訊。它可能會削弱主人聲。它包括一個內建模型：HP5。
3. 消除混響和延遲模型（由FoxJoy提供）：
  (1) MDX-Net：對於立體聲混響的移除是最好的選擇，但不能移除單聲道混響；
 (234) DeEcho：移除延遲效果。Aggressive模式比Normal模式移除得更徹底。DeReverb另外移除混響，可以移除單聲道混響，但對於高頻重的板式混響移除不乾淨。
消除混響/延遲注意事項：
1. DeEcho-DeReverb模型的處理時間是其他兩個DeEcho模型的近兩倍；
2. MDX-Net-Dereverb模型相當慢；
3. 個人推薦的最乾淨配置是先使用MDX-Net，然後使用DeEcho-Aggressive。", + "Batch size per GPU": "每张显卡的batch_size", + "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": "是否緩存所有訓練集至 VRAM。小於10分鐘的小數據可緩存以加速訓練，大數據緩存會爆 VRAM 也加不了多少速度", + "Calculate": "计算", + "Choose sample rate of the device": "使用设备采样率", + "Choose sample rate of the model": "使用模型采样率", + "Convert": "轉換", + "Device type": "设备类型", + "Enable phase vocoder": "启用相位声码器", + "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1": "rmvpe卡號配置：以-分隔輸入使用的不同進程卡號,例如0-0-1使用在卡0上跑2個進程並在卡1上跑1個進程", + "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2": "以-分隔輸入使用的卡號, 例如 0-1-2 使用卡0和卡1和卡2", + "Enter the experiment name": "輸入實驗名稱", + "Enter the path of the audio folder to be processed": "輸入待處理音頻資料夾路徑", + "Enter the path of the audio folder to be processed (copy it from the address bar of the file manager)": "輸入待處理音頻資料夾路徑(去檔案管理器地址欄拷貝即可)", + "Enter the path of the training folder": "輸入訓練檔案夾路徑", + "Exist": "有", + "Export Onnx": "Onnx导出", + "Export Onnx Model": "导出Onnx模型", + "Export audio (click on the three dots in the lower right corner to download)": "輸出音頻(右下角三個點，點了可以下載)", + "Export file format": "導出檔格式", + "Extra inference time": "額外推理時長", + "Extract": "提取", + "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation": "F0曲線檔案，可選，一行一個音高，代替預設的F0及升降調", + "FAQ (Frequently Asked Questions)": "常見問題解答", + "Fade length": "淡入淡出長度", + "Fail": "失败", + "Feature extraction": "特徵提取", + "Formant offset": "共振偏移", + "Fusion": "融合", + "GPU Information": "顯示卡資訊", + "General settings": "一般設定", + "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(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", + "Information": "信息", + "Input device": "輸入設備", + "Input noise reduction": "輸入降噪", + "Input voice monitor": "输入监听", + "Link index to outside folder": "链接索引到外部", + "Load model": "載入模型", + "Load pre-trained base model D path": "加載預訓練底模D路徑", + "Load pre-trained base model G path": "加載預訓練底模G路徑", + "Loudness factor": "響度因子", + "Model": "模型", + "Model Author": "模型作者", + "Model Author (Nullable)": "模型作者(可空)", + "Model Inference": "模型推理", + "Model architecture version": "模型版本型號", + "Model info": "模型信息", + "Model information to be modified": "要改的模型資訊", + "Model information to be placed": "要置入的模型資訊", + "Model name": "模型名", + "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", - "Onnx导出": "Onnx导出", - "Onnx输出路径": "Onnx输出路径", - "RVC模型路径": "RVC模型路径", + "Not exist": "无", + "Number of CPU processes used for harvest pitch algorithm": "harvest進程數", + "Number of CPU processes used for pitch extraction and data processing": "提取音高和處理數據使用的CPU進程數", + "One-click training": "一鍵訓練", + "Onnx Export Path": "Onnx输出路径", + "Output converted voice": "输出变声", + "Output device": "輸出設備", + "Output information": "輸出訊息", + "Output noise reduction": "輸出降噪", + "Path to Model": "模型路徑", + "Path to Model A": "A模型路徑", + "Path to Model B": "B模型路徑", + "Path to the feature index file. Leave blank to use the selected result from the dropdown": "特徵檢索庫檔路徑,為空則使用下拉的選擇結果", + "Performance settings": "效能設定", + "Pitch detection algorithm": "音高演算法", + "Pitch guidance (f0)": "音高引导(f0)", + "Pitch settings": "音調設定", + "Please choose the .index file": "请选择index文件", + "Please choose the .pth file": "请选择pth文件", + "Please specify the speaker/singer ID": "請指定說話人id", + "Process data": "處理資料", + "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": "保護清輔音和呼吸聲，防止電音撕裂等artifact，拉滿0.5不開啟，調低加大保護力度但可能降低索引效果", + "RVC Model Path": "RVC模型路径", + "Read from model": "从模型中读取", + "Refresh voice list and index path": "刷新音色列表和索引路徑", + "Reload device list": "重載設備列表", + "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling": "後處理重採樣至最終採樣率，0為不進行重採樣", + "Response threshold": "響應閾值", + "Sample length": "取樣長度", + "Sampling rate": "采样率", + "Save a small final model to the 'weights' folder at each save point": "是否在每次保存時間點將最終小模型保存至weights檔夾", + "Save file name (default: same as the source file)": "儲存的檔案名，預設空為與來源檔案同名", + "Save frequency (save_every_epoch)": "保存頻率save_every_epoch", + "Save name": "儲存名", + "Save only the latest '.ckpt' file to save disk space": "是否僅保存最新的ckpt檔案以節省硬碟空間", + "Saved model name (without extension)": "儲存的模型名不帶副檔名", + "Sealing date": "封装时间", + "Search feature ratio (controls accent strength, too high has artifacting)": "檢索特徵佔比", + "Select Speaker/Singer ID": "請選擇說話人ID", + "Select the .index file": "選擇 .index 檔案", + "Select the .pth file": "選擇 .pth 檔案", + "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": "選擇音高提取演算法,輸入歌聲可用pm提速,harvest低音好但巨慢無比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU", + "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": "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU", + "Similarity": "相似度", + "Similarity (from 0 to 1)": "相似度(0到1)", + "Single inference": "单次推理", + "Specify output folder": "指定輸出資料夾", + "Specify the output folder for accompaniment": "指定输出非主人声文件夹", + "Specify the output folder for vocals": "指定输出主人声文件夹", + "Start audio conversion": "開始音訊轉換", + "Step 1: Processing data": "step1:正在处理数据", + "Step 3a: Model training started": "step3a:正在训练模型", + "Stop audio conversion": "停止音訊轉換", + "Successfully built index into": "成功构建索引到", + "Takeover WASAPI device": "独占 WASAPI 设备", + "Target sample rate": "目標取樣率", + "The audio file to be processed": "待处理音频文件", + "This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible.
If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory Agreement-LICENSE.txt for details.": "本軟體以MIT協議開源，作者不對軟體具備任何控制力，使用軟體者、傳播軟體導出的聲音者自負全責。
如不認可該條款，則不能使用或引用軟體包內任何程式碼和檔案。詳見根目錄使用需遵守的協議-LICENSE.txt。", + "Total training epochs (total_epoch)": "總訓練輪數total_epoch", + "Train": "訓練", + "Train feature index": "訓練特徵索引", + "Train model": "訓練模型", + "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)": "變調(整數、半音數量、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", - "ckpt处理": "ckpt處理", - "harvest进程数": "harvest進程數", - "index文件路径不可包含中文": "index文件路径不可包含中文", - "pth文件路径不可包含中文": "pth文件路径不可包含中文", - "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程": "rmvpe卡號配置：以-分隔輸入使用的不同進程卡號,例如0-0-1使用在卡0上跑2個進程並在卡1上跑1個進程", - "step1:正在处理数据": "step1:正在处理数据", - "step2:正在提取音高&正在提取特征": "step2:正在提取音高&正在提取特征", - "step3a:正在训练模型": "step3a:正在训练模型", - "一键训练": "一鍵訓練", - "不显示": "不显示", - "也可批量输入音频文件, 二选一, 优先读文件夹": "也可批量输入音频文件, 二选一, 优先读文件夹", - "人声伴奏分离批量处理， 使用UVR5模型。
合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
模型分为三类：
1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
3、去混响、去延迟模型（by FoxJoy）：
  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
 (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
去混响/去延迟，附：
1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
2、MDX-Net-Dereverb模型挺慢的；
3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。": "使用UVR5模型進行人聲伴奏分離的批次處理。
有效資料夾路徑格式的例子：D:\\path\\to\\input\\folder（從檔案管理員地址欄複製）。
模型分為三類：
1. 保留人聲：選擇這個選項適用於沒有和聲的音訊。它比HP5更好地保留了人聲。它包括兩個內建模型：HP2和HP3。HP3可能輕微漏出伴奏，但比HP2更好地保留了人聲；
2. 僅保留主人聲：選擇這個選項適用於有和聲的音訊。它可能會削弱主人聲。它包括一個內建模型：HP5。
3. 消除混響和延遲模型（由FoxJoy提供）：
  (1) MDX-Net：對於立體聲混響的移除是最好的選擇，但不能移除單聲道混響；
 (234) DeEcho：移除延遲效果。Aggressive模式比Normal模式移除得更徹底。DeReverb另外移除混響，可以移除單聲道混響，但對於高頻重的板式混響移除不乾淨。
消除混響/延遲注意事項：
1. DeEcho-DeReverb模型的處理時間是其他兩個DeEcho模型的近兩倍；
2. MDX-Net-Dereverb模型相當慢；
3. 個人推薦的最乾淨配置是先使用MDX-Net，然後使用DeEcho-Aggressive。", - "从模型中读取": "从模型中读取", - "以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2": "以-分隔輸入使用的卡號, 例如 0-1-2 使用卡0和卡1和卡2", - "伴奏人声分离&去混响&去回声": "伴奏人聲分離&去混響&去回聲", - "使用模型采样率": "使用模型采样率", - "使用设备采样率": "使用设备采样率", - "保存名": "儲存名", - "保存的文件名, 默认空为和源文件同名": "儲存的檔案名，預設空為與來源檔案同名", - "保存的模型名不带后缀": "儲存的模型名不帶副檔名", - "保存频率save_every_epoch": "保存頻率save_every_epoch", - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果": "保護清輔音和呼吸聲，防止電音撕裂等artifact，拉滿0.5不開啟，調低加大保護力度但可能降低索引效果", - "信息": "信息", - "修改": "修改", - "停止音频转换": "停止音訊轉換", - "全流程结束！": "全流程结束！", - "共振偏移": "共振偏移", - "刷新音色列表和索引路径": "刷新音色列表和索引路徑", - "加载模型": "載入模型", - "加载预训练底模D路径": "加載預訓練底模D路徑", - "加载预训练底模G路径": "加載預訓練底模G路徑", - "单次推理": "单次推理", - "卸载音色省显存": "卸載音色節省 VRAM", - "变调(整数, 半音数量, 升八度12降八度-12)": "變調(整數、半音數量、升八度12降八度-12)", - "后处理重采样至最终采样率，0为不进行重采样": "後處理重採樣至最終採樣率，0為不進行重採樣", - "否": "否", - "启用相位声码器": "启用相位声码器", - "响应阈值": "響應閾值", - "响度因子": "響度因子", - "处理数据": "處理資料", - "失败": "失败", - "实际计算": "实际计算", - "导出Onnx模型": "导出Onnx模型", - "导出文件格式": "導出檔格式", - "封装时间": "封装时间", - "常见问题解答": "常見問題解答", - "常规设置": "一般設定", - "开始音频转换": "開始音訊轉換", - "待处理音频文件": "待处理音频文件", - "很遗憾您这没有能用的显卡来支持您训练": "很遗憾您这没有能用的显卡来支持您训练", - "性能设置": "效能設定", - "总训练轮数total_epoch": "總訓練輪數total_epoch", - "成功构建索引到": "成功构建索引到", - "批量推理": "批量推理", - "批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ": "批量轉換，輸入待轉換音頻資料夾，或上傳多個音頻檔案，在指定資料夾(默認opt)下輸出轉換的音頻。", - "指定输出主人声文件夹": "指定输出主人声文件夹", - "指定输出文件夹": "指定輸出資料夾", - "指定输出非主人声文件夹": "指定输出非主人声文件夹", - "推理时间(ms)": "推理時間(ms)", - "推理音色": "推理音色", - "提取": "提取", - "提取音高和处理数据使用的CPU进程数": "提取音高和處理數據使用的CPU進程數", - "无": "无", - "是": "是", - "是否仅保存最新的ckpt文件以节省硬盘空间": "是否僅保存最新的ckpt檔案以節省硬碟空間", - "是否在每次保存时间点将最终小模型保存至weights文件夹": "是否在每次保存時間點將最終小模型保存至weights檔夾", - "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速": "是否緩存所有訓練集至 VRAM。小於10分鐘的小數據可緩存以加速訓練，大數據緩存會爆 VRAM 也加不了多少速度", - "显卡信息": "顯示卡資訊", - "有": "有", - "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.": "本軟體以MIT協議開源，作者不對軟體具備任何控制力，使用軟體者、傳播軟體導出的聲音者自負全責。
如不認可該條款，則不能使用或引用軟體包內任何程式碼和檔案。詳見根目錄使用需遵守的協議-LICENSE.txt。", - "查看": "查看", - "检索特征占比": "檢索特徵佔比", - "模型": "模型", - "模型作者": "模型作者", - "模型作者(可空)": "模型作者(可空)", - "模型信息": "模型信息", - "模型名": "模型名", - "模型推理": "模型推理", - "模型是否带音高指导": "模型是否帶音高指導", - "模型是否带音高指导(唱歌一定要, 语音可以不要)": "模型是否帶音高指導（唱歌一定要，語音可以不要）", - "模型是否带音高指导,1是0否": "模型是否帶音高指導，1是0否", - "模型版本型号": "模型版本型號", - "模型路径": "模型路徑", - "每张显卡的batch_size": "每张显卡的batch_size", - "淡入淡出长度": "淡入淡出長度", - "版本": "版本", - "特征提取": "特徵提取", - "特征检索库文件路径,为空则使用下拉的选择结果": "特徵檢索庫檔路徑,為空則使用下拉的選擇結果", - "独占 WASAPI 设备": "独占 WASAPI 设备", - "男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ": "男性轉女性推薦+12key，女性轉男性推薦-12key，如果音域爆炸導致音色失真也可以自己調整到合適音域。", - "目标采样率": "目標取樣率", - "相似度": "相似度", - "相似度(0到1)": "相似度(0到1)", - "算法延迟(ms)": "算法延遲(ms)", - "自动检测index路径,下拉式选择(dropdown)": "自動檢測index路徑,下拉式選擇(dropdown)", - "融合": "融合", - "要改的模型信息": "要改的模型資訊", - "要置入的模型信息": "要置入的模型資訊", - "计算": "计算", - "训练": "訓練", - "训练模型": "訓練模型", - "训练特征索引": "訓練特徵索引", - "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": "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", - "设备类型": "设备类型", - "请指定说话人id": "請指定說話人id", - "请选择index文件": "请选择index文件", - "请选择pth文件": "请选择pth文件", - "请选择说话人id": "請選擇說話人ID", - "转换": "轉換", - "输入实验名": "輸入實驗名稱", - "输入待处理音频文件夹路径": "輸入待處理音頻資料夾路徑", - "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)": "輸入待處理音頻資料夾路徑(去檔案管理器地址欄拷貝即可)", - "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": "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", - "输入监听": "输入监听", - "输入训练文件夹路径": "輸入訓練檔案夾路徑", - "输入设备": "輸入設備", - "输入降噪": "輸入降噪", - "输出信息": "輸出訊息", - "输出变声": "输出变声", - "输出设备": "輸出設備", - "输出降噪": "輸出降噪", - "输出音频(右下角三个点,点了可以下载)": "輸出音頻(右下角三個點，點了可以下載)", - "选择.index文件": "選擇 .index 檔案", - "选择.pth文件": "選擇 .pth 檔案", - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU": "選擇音高提取演算法,輸入歌聲可用pm提速,harvest低音好但巨慢無比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU", - "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU": "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU", - "采样率": "采样率", - "采样长度": "取樣長度", - "重载设备列表": "重載設備列表", - "链接索引到外部": "链接索引到外部", - "音调设置": "音調設定", - "音频设备": "音訊設備", - "音高引导(f0)": "音高引导(f0)", - "音高算法": "音高演算法", - "额外推理时长": "額外推理時長" + "Unload model to save GPU memory": "卸載音色節省 VRAM", + "Version": "版本", + "View": "查看", + "Vocals/Accompaniment Separation & Reverberation Removal": "伴奏人聲分離&去混響&去回聲", + "Weight (w) for Model A": "A模型權重", + "Whether the model has pitch guidance": "模型是否帶音高指導", + "Whether the model has pitch guidance (1: yes, 0: no)": "模型是否帶音高指導，1是0否", + "Whether the model has pitch guidance (required for singing, optional for speech)": "模型是否帶音高指導（唱歌一定要，語音可以不要）", + "Yes": "是", + "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" } diff --git a/i18n/locale/zh_TW.json b/i18n/locale/zh_TW.json index b628585..bd7aa7b 100644 --- a/i18n/locale/zh_TW.json +++ b/i18n/locale/zh_TW.json @@ -1,160 +1,157 @@ { - "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件", - "### 查看模型信息\n> 仅支持weights文件夹下提取的小模型文件": "### 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": "### 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", - "### 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", - "### 模型融合\n可用于测试音色融合": "### 模型融合\n可用于测试音色融合", - "### 第一步 填写实验配置\n实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件.": "### 第一步 填写实验配置\n实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件.", - "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.": "### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.", - "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.": "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练.", - "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).": "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号).", - ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音": ">=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", - "A模型ID(长)": "A模型ID(长)", - "A模型权重": "A模型權重", - "A模型路径": "A模型路徑", - "B模型ID(长)": "B模型ID(长)", - "B模型路径": "B模型路徑", - "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调": "F0曲線檔案，可選，一行一個音高，代替預設的F0及升降調", - "ID(短)": "ID(短)", + "### 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.", + "### 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 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", + "#### 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", + "Algorithmic delays (ms)": "算法延遲(ms)", + "All processes have been completed!": "全流程结束！", + "Audio device": "音訊設備", + "Auto-detect index path and select from the dropdown": "自動檢測index路徑,下拉式選擇(dropdown)", + "Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt').": "批量轉換，輸入待轉換音頻資料夾，或上傳多個音頻檔案，在指定資料夾(默認opt)下輸出轉換的音頻。", + "Batch inference": "批量推理", + "Batch processing for vocal accompaniment separation using the UVR5 model.
Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).
The model is divided into three categories:
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.
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.
3. De-reverb and de-delay models (by FoxJoy):
  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;
 (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.
De-reverb/de-delay notes:
1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.
2. The MDX-Net-Dereverb model is quite slow.
3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive.": "使用UVR5模型進行人聲伴奏分離的批次處理。
有效資料夾路徑格式的例子：D:\\path\\to\\input\\folder（從檔案管理員地址欄複製）。
模型分為三類：
1. 保留人聲：選擇這個選項適用於沒有和聲的音訊。它比HP5更好地保留了人聲。它包括兩個內建模型：HP2和HP3。HP3可能輕微漏出伴奏，但比HP2更好地保留了人聲；
2. 僅保留主人聲：選擇這個選項適用於有和聲的音訊。它可能會削弱主人聲。它包括一個內建模型：HP5。
3. 消除混響和延遲模型（由FoxJoy提供）：
  (1) MDX-Net：對於立體聲混響的移除是最好的選擇，但不能移除單聲道混響；
 (234) DeEcho：移除延遲效果。Aggressive模式比Normal模式移除得更徹底。DeReverb另外移除混響，可以移除單聲道混響，但對於高頻重的板式混響移除不乾淨。
消除混響/延遲注意事項：
1. DeEcho-DeReverb模型的處理時間是其他兩個DeEcho模型的近兩倍；
2. MDX-Net-Dereverb模型相當慢；
3. 個人推薦的最乾淨配置是先使用MDX-Net，然後使用DeEcho-Aggressive。", + "Batch size per GPU": "每张显卡的batch_size", + "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": "是否緩存所有訓練集至 VRAM。小於10分鐘的小數據可緩存以加速訓練，大數據緩存會爆 VRAM 也加不了多少速度", + "Calculate": "计算", + "Choose sample rate of the device": "使用设备采样率", + "Choose sample rate of the model": "使用模型采样率", + "Convert": "轉換", + "Device type": "设备类型", + "Enable phase vocoder": "启用相位声码器", + "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1": "rmvpe卡號配置：以-分隔輸入使用的不同進程卡號,例如0-0-1使用在卡0上跑2個進程並在卡1上跑1個進程", + "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2": "以-分隔輸入使用的卡號, 例如 0-1-2 使用卡0和卡1和卡2", + "Enter the experiment name": "輸入實驗名稱", + "Enter the path of the audio folder to be processed": "輸入待處理音頻資料夾路徑", + "Enter the path of the audio folder to be processed (copy it from the address bar of the file manager)": "輸入待處理音頻資料夾路徑(去檔案管理器地址欄拷貝即可)", + "Enter the path of the training folder": "輸入訓練檔案夾路徑", + "Exist": "有", + "Export Onnx": "Onnx导出", + "Export Onnx Model": "导出Onnx模型", + "Export audio (click on the three dots in the lower right corner to download)": "輸出音頻(右下角三個點，點了可以下載)", + "Export file format": "導出檔格式", + "Extra inference time": "額外推理時長", + "Extract": "提取", + "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation": "F0曲線檔案，可選，一行一個音高，代替預設的F0及升降調", + "FAQ (Frequently Asked Questions)": "常見問題解答", + "Fade length": "淡入淡出長度", + "Fail": "失败", + "Feature extraction": "特徵提取", + "Formant offset": "共振偏移", + "Fusion": "融合", + "GPU Information": "顯示卡資訊", + "General settings": "一般設定", + "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(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", + "Information": "信息", + "Input device": "輸入設備", + "Input noise reduction": "輸入降噪", + "Input voice monitor": "输入监听", + "Link index to outside folder": "链接索引到外部", + "Load model": "載入模型", + "Load pre-trained base model D path": "加載預訓練底模D路徑", + "Load pre-trained base model G path": "加載預訓練底模G路徑", + "Loudness factor": "響度因子", + "Model": "模型", + "Model Author": "模型作者", + "Model Author (Nullable)": "模型作者(可空)", + "Model Inference": "模型推理", + "Model architecture version": "模型版本型號", + "Model info": "模型信息", + "Model information to be modified": "要改的模型資訊", + "Model information to be placed": "要置入的模型資訊", + "Model name": "模型名", + "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", - "Onnx导出": "Onnx导出", - "Onnx输出路径": "Onnx输出路径", - "RVC模型路径": "RVC模型路径", + "Not exist": "无", + "Number of CPU processes used for harvest pitch algorithm": "harvest進程數", + "Number of CPU processes used for pitch extraction and data processing": "提取音高和處理數據使用的CPU進程數", + "One-click training": "一鍵訓練", + "Onnx Export Path": "Onnx输出路径", + "Output converted voice": "输出变声", + "Output device": "輸出設備", + "Output information": "輸出訊息", + "Output noise reduction": "輸出降噪", + "Path to Model": "模型路徑", + "Path to Model A": "A模型路徑", + "Path to Model B": "B模型路徑", + "Path to the feature index file. Leave blank to use the selected result from the dropdown": "特徵檢索庫檔路徑,為空則使用下拉的選擇結果", + "Performance settings": "效能設定", + "Pitch detection algorithm": "音高演算法", + "Pitch guidance (f0)": "音高引导(f0)", + "Pitch settings": "音調設定", + "Please choose the .index file": "请选择index文件", + "Please choose the .pth file": "请选择pth文件", + "Please specify the speaker/singer ID": "請指定說話人id", + "Process data": "處理資料", + "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": "保護清輔音和呼吸聲，防止電音撕裂等artifact，拉滿0.5不開啟，調低加大保護力度但可能降低索引效果", + "RVC Model Path": "RVC模型路径", + "Read from model": "从模型中读取", + "Refresh voice list and index path": "刷新音色列表和索引路徑", + "Reload device list": "重載設備列表", + "Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling": "後處理重採樣至最終採樣率，0為不進行重採樣", + "Response threshold": "響應閾值", + "Sample length": "取樣長度", + "Sampling rate": "采样率", + "Save a small final model to the 'weights' folder at each save point": "是否在每次保存時間點將最終小模型保存至weights檔夾", + "Save file name (default: same as the source file)": "儲存的檔案名，預設空為與來源檔案同名", + "Save frequency (save_every_epoch)": "保存頻率save_every_epoch", + "Save name": "儲存名", + "Save only the latest '.ckpt' file to save disk space": "是否僅保存最新的ckpt檔案以節省硬碟空間", + "Saved model name (without extension)": "儲存的模型名不帶副檔名", + "Sealing date": "封装时间", + "Search feature ratio (controls accent strength, too high has artifacting)": "檢索特徵佔比", + "Select Speaker/Singer ID": "請選擇說話人ID", + "Select the .index file": "選擇 .index 檔案", + "Select the .pth file": "選擇 .pth 檔案", + "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": "選擇音高提取演算法,輸入歌聲可用pm提速,harvest低音好但巨慢無比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU", + "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": "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU", + "Similarity": "相似度", + "Similarity (from 0 to 1)": "相似度(0到1)", + "Single inference": "单次推理", + "Specify output folder": "指定輸出資料夾", + "Specify the output folder for accompaniment": "指定输出非主人声文件夹", + "Specify the output folder for vocals": "指定输出主人声文件夹", + "Start audio conversion": "開始音訊轉換", + "Step 1: Processing data": "step1:正在处理数据", + "Step 3a: Model training started": "step3a:正在训练模型", + "Stop audio conversion": "停止音訊轉換", + "Successfully built index into": "成功构建索引到", + "Takeover WASAPI device": "独占 WASAPI 设备", + "Target sample rate": "目標取樣率", + "The audio file to be processed": "待处理音频文件", + "This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible.
If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory Agreement-LICENSE.txt for details.": "本軟體以MIT協議開源，作者不對軟體具備任何控制力，使用軟體者、傳播軟體導出的聲音者自負全責。
如不認可該條款，則不能使用或引用軟體包內任何程式碼和檔案。詳見根目錄使用需遵守的協議-LICENSE.txt。", + "Total training epochs (total_epoch)": "總訓練輪數total_epoch", + "Train": "訓練", + "Train feature index": "訓練特徵索引", + "Train model": "訓練模型", + "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)": "變調(整數、半音數量、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", - "ckpt处理": "ckpt處理", - "harvest进程数": "harvest進程數", - "index文件路径不可包含中文": "index文件路径不可包含中文", - "pth文件路径不可包含中文": "pth文件路径不可包含中文", - "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程": "rmvpe卡號配置：以-分隔輸入使用的不同進程卡號,例如0-0-1使用在卡0上跑2個進程並在卡1上跑1個進程", - "step1:正在处理数据": "step1:正在处理数据", - "step2:正在提取音高&正在提取特征": "step2:正在提取音高&正在提取特征", - "step3a:正在训练模型": "step3a:正在训练模型", - "一键训练": "一鍵訓練", - "不显示": "不显示", - "也可批量输入音频文件, 二选一, 优先读文件夹": "也可批量输入音频文件, 二选一, 优先读文件夹", - "人声伴奏分离批量处理， 使用UVR5模型。
合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
模型分为三类：
1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
3、去混响、去延迟模型（by FoxJoy）：
  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
 (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
去混响/去延迟，附：
1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
2、MDX-Net-Dereverb模型挺慢的；
3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。": "使用UVR5模型進行人聲伴奏分離的批次處理。
有效資料夾路徑格式的例子：D:\\path\\to\\input\\folder（從檔案管理員地址欄複製）。
模型分為三類：
1. 保留人聲：選擇這個選項適用於沒有和聲的音訊。它比HP5更好地保留了人聲。它包括兩個內建模型：HP2和HP3。HP3可能輕微漏出伴奏，但比HP2更好地保留了人聲；
2. 僅保留主人聲：選擇這個選項適用於有和聲的音訊。它可能會削弱主人聲。它包括一個內建模型：HP5。
3. 消除混響和延遲模型（由FoxJoy提供）：
  (1) MDX-Net：對於立體聲混響的移除是最好的選擇，但不能移除單聲道混響；
 (234) DeEcho：移除延遲效果。Aggressive模式比Normal模式移除得更徹底。DeReverb另外移除混響，可以移除單聲道混響，但對於高頻重的板式混響移除不乾淨。
消除混響/延遲注意事項：
1. DeEcho-DeReverb模型的處理時間是其他兩個DeEcho模型的近兩倍；
2. MDX-Net-Dereverb模型相當慢；
3. 個人推薦的最乾淨配置是先使用MDX-Net，然後使用DeEcho-Aggressive。", - "从模型中读取": "从模型中读取", - "以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2": "以-分隔輸入使用的卡號, 例如 0-1-2 使用卡0和卡1和卡2", - "伴奏人声分离&去混响&去回声": "伴奏人聲分離&去混響&去回聲", - "使用模型采样率": "使用模型采样率", - "使用设备采样率": "使用设备采样率", - "保存名": "儲存名", - "保存的文件名, 默认空为和源文件同名": "儲存的檔案名，預設空為與來源檔案同名", - "保存的模型名不带后缀": "儲存的模型名不帶副檔名", - "保存频率save_every_epoch": "保存頻率save_every_epoch", - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果": "保護清輔音和呼吸聲，防止電音撕裂等artifact，拉滿0.5不開啟，調低加大保護力度但可能降低索引效果", - "信息": "信息", - "修改": "修改", - "停止音频转换": "停止音訊轉換", - "全流程结束！": "全流程结束！", - "共振偏移": "共振偏移", - "刷新音色列表和索引路径": "刷新音色列表和索引路徑", - "加载模型": "載入模型", - "加载预训练底模D路径": "加載預訓練底模D路徑", - "加载预训练底模G路径": "加載預訓練底模G路徑", - "单次推理": "单次推理", - "卸载音色省显存": "卸載音色節省 VRAM", - "变调(整数, 半音数量, 升八度12降八度-12)": "變調(整數、半音數量、升八度12降八度-12)", - "后处理重采样至最终采样率，0为不进行重采样": "後處理重採樣至最終採樣率，0為不進行重採樣", - "否": "否", - "启用相位声码器": "启用相位声码器", - "响应阈值": "響應閾值", - "响度因子": "響度因子", - "处理数据": "處理資料", - "失败": "失败", - "实际计算": "实际计算", - "导出Onnx模型": "导出Onnx模型", - "导出文件格式": "導出檔格式", - "封装时间": "封装时间", - "常见问题解答": "常見問題解答", - "常规设置": "一般設定", - "开始音频转换": "開始音訊轉換", - "待处理音频文件": "待处理音频文件", - "很遗憾您这没有能用的显卡来支持您训练": "很遗憾您这没有能用的显卡来支持您训练", - "性能设置": "效能設定", - "总训练轮数total_epoch": "總訓練輪數total_epoch", - "成功构建索引到": "成功构建索引到", - "批量推理": "批量推理", - "批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ": "批量轉換，輸入待轉換音頻資料夾，或上傳多個音頻檔案，在指定資料夾(默認opt)下輸出轉換的音頻。", - "指定输出主人声文件夹": "指定输出主人声文件夹", - "指定输出文件夹": "指定輸出資料夾", - "指定输出非主人声文件夹": "指定输出非主人声文件夹", - "推理时间(ms)": "推理時間(ms)", - "推理音色": "推理音色", - "提取": "提取", - "提取音高和处理数据使用的CPU进程数": "提取音高和處理數據使用的CPU進程數", - "无": "无", - "是": "是", - "是否仅保存最新的ckpt文件以节省硬盘空间": "是否僅保存最新的ckpt檔案以節省硬碟空間", - "是否在每次保存时间点将最终小模型保存至weights文件夹": "是否在每次保存時間點將最終小模型保存至weights檔夾", - "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速": "是否緩存所有訓練集至 VRAM。小於10分鐘的小數據可緩存以加速訓練，大數據緩存會爆 VRAM 也加不了多少速度", - "显卡信息": "顯示卡資訊", - "有": "有", - "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.": "本軟體以MIT協議開源，作者不對軟體具備任何控制力，使用軟體者、傳播軟體導出的聲音者自負全責。
如不認可該條款，則不能使用或引用軟體包內任何程式碼和檔案。詳見根目錄使用需遵守的協議-LICENSE.txt。", - "查看": "查看", - "检索特征占比": "檢索特徵佔比", - "模型": "模型", - "模型作者": "模型作者", - "模型作者(可空)": "模型作者(可空)", - "模型信息": "模型信息", - "模型名": "模型名", - "模型推理": "模型推理", - "模型是否带音高指导": "模型是否帶音高指導", - "模型是否带音高指导(唱歌一定要, 语音可以不要)": "模型是否帶音高指導（唱歌一定要，語音可以不要）", - "模型是否带音高指导,1是0否": "模型是否帶音高指導，1是0否", - "模型版本型号": "模型版本型號", - "模型路径": "模型路徑", - "每张显卡的batch_size": "每张显卡的batch_size", - "淡入淡出长度": "淡入淡出長度", - "版本": "版本", - "特征提取": "特徵提取", - "特征检索库文件路径,为空则使用下拉的选择结果": "特徵檢索庫檔路徑,為空則使用下拉的選擇結果", - "独占 WASAPI 设备": "独占 WASAPI 设备", - "男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ": "男性轉女性推薦+12key，女性轉男性推薦-12key，如果音域爆炸導致音色失真也可以自己調整到合適音域。", - "目标采样率": "目標取樣率", - "相似度": "相似度", - "相似度(0到1)": "相似度(0到1)", - "算法延迟(ms)": "算法延遲(ms)", - "自动检测index路径,下拉式选择(dropdown)": "自動檢測index路徑,下拉式選擇(dropdown)", - "融合": "融合", - "要改的模型信息": "要改的模型資訊", - "要置入的模型信息": "要置入的模型資訊", - "计算": "计算", - "训练": "訓練", - "训练模型": "訓練模型", - "训练特征索引": "訓練特徵索引", - "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": "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", - "设备类型": "设备类型", - "请指定说话人id": "請指定說話人id", - "请选择index文件": "请选择index文件", - "请选择pth文件": "请选择pth文件", - "请选择说话人id": "請選擇說話人ID", - "转换": "轉換", - "输入实验名": "輸入實驗名稱", - "输入待处理音频文件夹路径": "輸入待處理音頻資料夾路徑", - "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)": "輸入待處理音頻資料夾路徑(去檔案管理器地址欄拷貝即可)", - "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": "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", - "输入监听": "输入监听", - "输入训练文件夹路径": "輸入訓練檔案夾路徑", - "输入设备": "輸入設備", - "输入降噪": "輸入降噪", - "输出信息": "輸出訊息", - "输出变声": "输出变声", - "输出设备": "輸出設備", - "输出降噪": "輸出降噪", - "输出音频(右下角三个点,点了可以下载)": "輸出音頻(右下角三個點，點了可以下載)", - "选择.index文件": "選擇 .index 檔案", - "选择.pth文件": "選擇 .pth 檔案", - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU": "選擇音高提取演算法,輸入歌聲可用pm提速,harvest低音好但巨慢無比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU", - "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU": "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU", - "采样率": "采样率", - "采样长度": "取樣長度", - "重载设备列表": "重載設備列表", - "链接索引到外部": "链接索引到外部", - "音调设置": "音調設定", - "音频设备": "音訊設備", - "音高引导(f0)": "音高引导(f0)", - "音高算法": "音高演算法", - "额外推理时长": "額外推理時長" + "Unload model to save GPU memory": "卸載音色節省 VRAM", + "Version": "版本", + "View": "查看", + "Vocals/Accompaniment Separation & Reverberation Removal": "伴奏人聲分離&去混響&去回聲", + "Weight (w) for Model A": "A模型權重", + "Whether the model has pitch guidance": "模型是否帶音高指導", + "Whether the model has pitch guidance (1: yes, 0: no)": "模型是否帶音高指導，1是0否", + "Whether the model has pitch guidance (required for singing, optional for speech)": "模型是否帶音高指導（唱歌一定要，語音可以不要）", + "Yes": "是", + "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" } diff --git a/i18n/scan_i18n.py b/i18n/scan_i18n.py index d1b490e..901802f 100644 --- a/i18n/scan_i18n.py +++ b/i18n/scan_i18n.py @@ -40,7 +40,7 @@ print() print("Total unique:", len(code_keys)) -standard_file = "i18n/locale/zh_CN.json" +standard_file = "i18n/locale/en_US.json" with open(standard_file, "r", encoding="utf-8") as f: standard_data = json.load(f, object_pairs_hook=OrderedDict) standard_keys = set(standard_data.keys()) diff --git a/infer/lib/train/process_ckpt.py b/infer/lib/train/process_ckpt.py index 1dd37cb..cf9697f 100644 --- a/infer/lib/train/process_ckpt.py +++ b/infer/lib/train/process_ckpt.py @@ -268,7 +268,7 @@ def merge(path1, path2, alpha1, sr, f0, info, name, version): if author: opt["author"] = author opt["sr"] = sr - opt["f0"] = 1 if f0 == i18n("是") else 0 + opt["f0"] = 1 if f0 == i18n("Yes") else 0 opt["version"] = version opt["info"] = info h = model_hash_ckpt(opt) diff --git a/infer/modules/vc/info.py b/infer/modules/vc/info.py index 0d9db1d..a396a59 100644 --- a/infer/modules/vc/info.py +++ b/infer/modules/vc/info.py @@ -17,50 +17,50 @@ def show_model_info(cpt, show_long_id=False): if id != idread: id += ( "(" - + i18n("实际计算") + + i18n("Actually calculated") + "), " + idread + "(" - + i18n("从模型中读取") + + i18n("Read from model") + ")" ) sim = hash_similarity(h, hread) if not isinstance(sim, str): sim = "%.2f%%" % (sim * 100) if not show_long_id: - h = i18n("不显示") + h = i18n("Hidden") if h != hread: - h = i18n("相似度") + " " + sim + " -> " + h + h = i18n("Similarity") + " " + sim + " -> " + h elif h != hread: h = ( - i18n("相似度") + i18n("Similarity") + " " + sim + " -> " + h + "(" - + i18n("实际计算") + + i18n("Actually calculated") + "), " + hread + "(" - + i18n("从模型中读取") + + i18n("Read from model") + ")" ) - txt = f"""{i18n("模型名")}: %s -{i18n("封装时间")}: %s + txt = f"""{i18n("Model name")}: %s +{i18n("Sealing date")}: %s {i18n("模型作者")}: %s -{i18n("信息")}: %s -{i18n("采样率")}: %s -{i18n("音高引导(f0)")}: %s -{i18n("版本")}: %s -{i18n("ID(短)")}: %s -{i18n("ID(长)")}: %s""" % ( +{i18n("Information")}: %s +{i18n("Sampling rate")}: %s +{i18n("Pitch guidance (f0)")}: %s +{i18n("Version")}: %s +{i18n("ID(short)")}: %s +{i18n("ID(long)")}: %s""" % ( cpt.get("name", i18n("Unknown")), datetime.fromtimestamp(float(cpt.get("timestamp", 0))), cpt.get("author", i18n("Unknown")), cpt.get("info", i18n("None")), cpt.get("sr", i18n("Unknown")), - i18n("有") if cpt.get("f0", 0) == 1 else i18n("无"), + i18n("Exist") if cpt.get("f0", 0) == 1 else i18n("Not exist"), cpt.get("version", i18n("None")), id, h, diff --git a/tools/web/infer-only.py b/tools/web/infer-only.py index 48f197e..29f096a 100644 --- a/tools/web/infer-only.py +++ b/tools/web/infer-only.py @@ -46,13 +46,13 @@ with app: RVC 在线demo """ ) - sid = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names)) + sid = gr.Dropdown(label=i18n("Inferencing voice"), choices=sorted(names)) with gr.Column(): spk_item = gr.Slider( minimum=0, maximum=2333, step=1, - label=i18n("请选择说话人id"), + label=i18n("Select Speaker/Singer ID"), value=0, visible=False, interactive=True, @@ -60,16 +60,19 @@ with app: sid.change(fn=vc.get_vc, inputs=[sid], outputs=[spk_item]) gr.Markdown( value=i18n( - "男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. " + "Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)" ) ) vc_input3 = gr.Audio(label="上传音频（长度小于90秒）") vc_transform0 = gr.Number( - label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 + label=i18n( + "Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)" + ), + value=0, ) f0method0 = gr.Radio( label=i18n( - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU" + "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"], value="pm", @@ -79,7 +82,7 @@ with app: minimum=0, maximum=7, label=i18n( - ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音" + "If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness." ), value=3, step=1, @@ -87,27 +90,33 @@ with app: ) with gr.Column(): file_index1 = gr.Textbox( - label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), + label=i18n( + "Path to the feature index file. Leave blank to use the selected result from the dropdown" + ), value="", interactive=False, visible=False, ) file_index2 = gr.Dropdown( - label=i18n("自动检测index路径,下拉式选择(dropdown)"), + label=i18n("Auto-detect index path and select from the dropdown"), choices=sorted(index_paths), interactive=True, ) index_rate1 = gr.Slider( minimum=0, maximum=1, - label=i18n("检索特征占比"), + label=i18n( + "Search feature ratio (controls accent strength, too high has artifacting)" + ), value=0.88, interactive=True, ) resample_sr0 = gr.Slider( minimum=0, maximum=48000, - label=i18n("后处理重采样至最终采样率，0为不进行重采样"), + 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, @@ -116,7 +125,7 @@ with app: minimum=0, maximum=1, label=i18n( - "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" + "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=1, interactive=True, @@ -125,18 +134,24 @@ with app: minimum=0, maximum=0.5, label=i18n( - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果" + "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, ) f0_file = gr.File( - label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调") + label=i18n( + "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation" + ) + ) + but0 = gr.Button(i18n("Convert"), variant="primary") + vc_output1 = gr.Textbox(label=i18n("Output information")) + vc_output2 = gr.Audio( + label=i18n( + "Export audio (click on the three dots in the lower right corner to download)" + ) ) - but0 = gr.Button(i18n("转换"), variant="primary") - vc_output1 = gr.Textbox(label=i18n("输出信息")) - vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)")) but0.click( vc.vc_single, [ diff --git a/web.py b/web.py index cea661e..bf61dfe 100644 --- a/web.py +++ b/web.py @@ -126,7 +126,9 @@ if if_gpu_ok and len(gpu_infos) > 0: gpu_info = "\n".join(gpu_infos) default_batch_size = min(mem) // 2 else: - gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练") + gpu_info = i18n( + "Unfortunately, there is no compatible GPU available to support your training." + ) default_batch_size = 1 gpus = "-".join([i[0] for i in gpu_infos]) @@ -579,9 +581,9 @@ def click_train( save_epoch10, '-pg "%s"' % pretrained_G14 if pretrained_G14 != "" else "", '-pd "%s"' % pretrained_D15 if pretrained_D15 != "" else "", - 1 if if_save_latest13 == i18n("是") else 0, - 1 if if_cache_gpu17 == i18n("是") else 0, - 1 if if_save_every_weights18 == i18n("是") else 0, + 1 if if_save_latest13 == i18n("Yes") else 0, + 1 if if_cache_gpu17 == i18n("Yes") else 0, + 1 if if_save_every_weights18 == i18n("Yes") else 0, version19, author, ) @@ -592,7 +594,7 @@ def click_train( logger.info("Execute: " + cmd) p = Popen(cmd, shell=True, cwd=now_dir) p.wait() - return "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" + return "Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder." # but4.click(train_index, [exp_dir1], info3) @@ -669,7 +671,7 @@ def train_index(exp_dir1, version19): version19, ) faiss.write_index(index, index_save_path) - infos.append(i18n("成功构建索引到") + " " + index_save_path) + infos.append(i18n("Successfully built index into") + " " + index_save_path) link_target = "%s/%s_IVF%s_Flat_nprobe_%s_%s_%s.index" % ( outside_index_root, exp_dir1, @@ -681,9 +683,15 @@ def train_index(exp_dir1, version19): try: link = os.link if platform.system() == "Windows" else os.symlink link(index_save_path, link_target) - infos.append(i18n("链接索引到外部") + " " + link_target) + infos.append(i18n("Link index to outside folder") + " " + link_target) except: - infos.append(i18n("链接索引到外部") + " " + link_target + " " + i18n("失败")) + infos.append( + i18n("Link index to outside folder") + + " " + + link_target + + " " + + i18n("Fail") + ) # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19)) # infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19)) @@ -718,12 +726,12 @@ def train1key( infos.append(strr) return "\n".join(infos) - # step1:处理数据 - yield get_info_str(i18n("step1:正在处理数据")) + # step1:Process data + yield get_info_str(i18n("Step 1: Processing data")) [get_info_str(_) for _ in preprocess_dataset(trainset_dir4, exp_dir1, sr2, np7)] # step2a:提取音高 - yield get_info_str(i18n("step2:正在提取音高&正在提取特征")) + yield get_info_str(i18n("step2:Pitch extraction & feature extraction")) [ get_info_str(_) for _ in extract_f0_feature( @@ -731,8 +739,8 @@ def train1key( ) ] - # step3a:训练模型 - yield get_info_str(i18n("step3a:正在训练模型")) + # step3a:Train model + yield get_info_str(i18n("Step 3a: Model training started")) click_train( exp_dir1, sr2, @@ -751,12 +759,14 @@ def train1key( author, ) yield get_info_str( - i18n("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") + i18n( + "Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder." + ) ) # step3b:训练索引 [get_info_str(_) for _ in train_index(exp_dir1, version19)] - yield get_info_str(i18n("全流程结束！")) + yield get_info_str(i18n("All processes have been completed!")) # ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__]) @@ -791,23 +801,27 @@ with gr.Blocks(title="RVC WebUI") as app: gr.Markdown("## RVC WebUI") gr.Markdown( value=i18n( - "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE." + "This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible.
If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory Agreement-LICENSE.txt for details." ) ) with gr.Tabs(): - with gr.TabItem(i18n("模型推理")): + with gr.TabItem(i18n("Model Inference")): with gr.Row(): - sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names)) + sid0 = gr.Dropdown( + label=i18n("Inferencing voice"), choices=sorted(names) + ) with gr.Column(): refresh_button = gr.Button( - i18n("刷新音色列表和索引路径"), variant="primary" + i18n("Refresh voice list and index path"), variant="primary" + ) + clean_button = gr.Button( + i18n("Unload model to save GPU memory"), variant="primary" ) - clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary") spk_item = gr.Slider( minimum=0, maximum=2333, step=1, - label=i18n("请选择说话人id"), + label=i18n("Select Speaker/Singer ID"), value=0, visible=False, interactive=True, @@ -815,32 +829,37 @@ with gr.Blocks(title="RVC WebUI") as app: clean_button.click( fn=clean, inputs=[], outputs=[sid0], api_name="infer_clean" ) - modelinfo = gr.Textbox(label=i18n("模型信息"), max_lines=8) - with gr.TabItem(i18n("单次推理")): + 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("变调(整数, 半音数量, 升八度12降八度-12)"), + 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("待处理音频文件"), type="filepath" + label=i18n("The audio file to be processed"), + type="filepath", ) file_index2 = gr.Dropdown( - label=i18n("自动检测index路径,下拉式选择(dropdown)"), + label=i18n( + "Auto-detect index path and select from the dropdown" + ), choices=sorted(index_paths), interactive=True, ) file_index1 = gr.File( label=i18n( - "特征检索库文件路径,为空则使用下拉的选择结果" + "Path to the feature index file. Leave blank to use the selected result from the dropdown" ), ) with gr.Column(): f0method0 = gr.Radio( label=i18n( - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU" + "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"] @@ -853,7 +872,9 @@ with gr.Blocks(title="RVC WebUI") as app: resample_sr0 = gr.Slider( minimum=0, maximum=48000, - label=i18n("后处理重采样至最终采样率，0为不进行重采样"), + 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, @@ -862,7 +883,7 @@ with gr.Blocks(title="RVC WebUI") as app: minimum=0, maximum=1, label=i18n( - "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" + "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, @@ -871,7 +892,7 @@ with gr.Blocks(title="RVC WebUI") as app: minimum=0, maximum=0.5, label=i18n( - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果" + "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, @@ -881,7 +902,7 @@ with gr.Blocks(title="RVC WebUI") as app: minimum=0, maximum=7, label=i18n( - ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音" + "If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness." ), value=3, step=1, @@ -890,19 +911,23 @@ with gr.Blocks(title="RVC WebUI") as app: index_rate1 = gr.Slider( minimum=0, maximum=1, - label=i18n("检索特征占比"), + label=i18n( + "Search feature ratio (controls accent strength, too high has artifacting)" + ), value=0.75, interactive=True, ) f0_file = gr.File( label=i18n( - "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调" + "F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation" ), visible=False, ) - but0 = gr.Button(i18n("转换"), variant="primary") + but0 = gr.Button(i18n("Convert"), variant="primary") vc_output2 = gr.Audio( - label=i18n("输出音频(右下角三个点,点了可以下载)") + label=i18n( + "Export audio (click on the three dots in the lower right corner to download)" + ) ) refresh_button.click( @@ -912,7 +937,7 @@ with gr.Blocks(title="RVC WebUI") as app: api_name="infer_refresh", ) with gr.Group(): - vc_output1 = gr.Textbox(label=i18n("输出信息")) + vc_output1 = gr.Textbox(label=i18n("Output information")) but0.click( vc.vc_single, @@ -934,38 +959,46 @@ with gr.Blocks(title="RVC WebUI") as app: [vc_output1, vc_output2], api_name="infer_convert", ) - with gr.TabItem(i18n("批量推理")): + with gr.TabItem(i18n("Batch inference")): gr.Markdown( value=i18n( - "批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. " + "Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt')." ) ) with gr.Row(): with gr.Column(): vc_transform1 = gr.Number( - label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), + label=i18n( + "Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)" + ), value=0, ) dir_input = gr.Textbox( label=i18n( - "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)" + "Enter the path of the audio folder to be processed (copy it from the address bar of the file manager)" ), placeholder="C:\\Users\\Desktop\\input_vocal_dir", ) inputs = gr.File( file_count="multiple", - label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹"), + label=i18n( + "Multiple audio files can also be imported. If a folder path exists, this input is ignored." + ), ) opt_input = gr.Textbox( - label=i18n("指定输出文件夹"), value="opt" + label=i18n("Specify output folder"), value="opt" ) file_index4 = gr.Dropdown( - label=i18n("自动检测index路径,下拉式选择(dropdown)"), + label=i18n( + "Auto-detect index path and select from the dropdown" + ), choices=sorted(index_paths), interactive=True, ) file_index3 = gr.File( - label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), + label=i18n( + "Path to the feature index file. Leave blank to use the selected result from the dropdown" + ), ) refresh_button.click( @@ -983,7 +1016,7 @@ with gr.Blocks(title="RVC WebUI") as app: with gr.Column(): f0method1 = gr.Radio( label=i18n( - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU" + "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"] @@ -996,7 +1029,9 @@ with gr.Blocks(title="RVC WebUI") as app: resample_sr1 = gr.Slider( minimum=0, maximum=48000, - label=i18n("后处理重采样至最终采样率，0为不进行重采样"), + 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, @@ -1005,7 +1040,7 @@ with gr.Blocks(title="RVC WebUI") as app: minimum=0, maximum=1, label=i18n( - "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" + "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=1, interactive=True, @@ -1014,7 +1049,7 @@ with gr.Blocks(title="RVC WebUI") as app: minimum=0, maximum=0.5, label=i18n( - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果" + "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, @@ -1024,7 +1059,7 @@ with gr.Blocks(title="RVC WebUI") as app: minimum=0, maximum=7, label=i18n( - ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音" + "If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness." ), value=3, step=1, @@ -1033,18 +1068,20 @@ with gr.Blocks(title="RVC WebUI") as app: index_rate2 = gr.Slider( minimum=0, maximum=1, - label=i18n("检索特征占比"), + label=i18n( + "Search feature ratio (controls accent strength, too high has artifacting)" + ), value=1, interactive=True, ) format1 = gr.Radio( - label=i18n("导出文件格式"), + label=i18n("Export file format"), choices=["wav", "flac", "mp3", "m4a"], value="wav", interactive=True, ) - but1 = gr.Button(i18n("转换"), variant="primary") - vc_output3 = gr.Textbox(label=i18n("输出信息")) + but1 = gr.Button(i18n("Convert"), variant="primary") + vc_output3 = gr.Textbox(label=i18n("Output information")) but1.click( vc.vc_multi, @@ -1081,26 +1118,32 @@ with gr.Blocks(title="RVC WebUI") as app: ], api_name="infer_change_voice", ) - with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")): + with gr.TabItem( + i18n("Vocals/Accompaniment Separation & Reverberation Removal") + ): with gr.Group(): gr.Markdown( value=i18n( - "人声伴奏分离批量处理， 使用UVR5模型。
合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
模型分为三类：
1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
3、去混响、去延迟模型（by FoxJoy）：
  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
 (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
去混响/去延迟，附：
1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
2、MDX-Net-Dereverb模型挺慢的；
3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。" + "Batch processing for vocal accompaniment separation using the UVR5 model.
Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).
The model is divided into three categories:
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.
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.
3. De-reverb and de-delay models (by FoxJoy):
  (1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;
 (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.
De-reverb/de-delay notes:
1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.
2. The MDX-Net-Dereverb model is quite slow.
3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive." ) ) with gr.Row(): with gr.Column(): dir_wav_input = gr.Textbox( - label=i18n("输入待处理音频文件夹路径"), + 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("也可批量输入音频文件, 二选一, 优先读文件夹"), + 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("模型"), choices=uvr5_names + label=i18n("Model"), choices=uvr5_names ) agg = gr.Slider( minimum=0, @@ -1112,19 +1155,21 @@ with gr.Blocks(title="RVC WebUI") as app: visible=False, # 先不开放调整 ) opt_vocal_root = gr.Textbox( - label=i18n("指定输出主人声文件夹"), value="opt" + label=i18n("Specify the output folder for vocals"), + value="opt", ) opt_ins_root = gr.Textbox( - label=i18n("指定输出非主人声文件夹"), value="opt" + label=i18n("Specify the output folder for accompaniment"), + value="opt", ) format0 = gr.Radio( - label=i18n("导出文件格式"), + label=i18n("Export file format"), choices=["wav", "flac", "mp3", "m4a"], value="flac", interactive=True, ) - but2 = gr.Button(i18n("转换"), variant="primary") - vc_output4 = gr.Textbox(label=i18n("输出信息")) + but2 = gr.Button(i18n("Convert"), variant="primary") + vc_output4 = gr.Textbox(label=i18n("Output information")) but2.click( uvr, [ @@ -1139,38 +1184,44 @@ with gr.Blocks(title="RVC WebUI") as app: [vc_output4], api_name="uvr_convert", ) - with gr.TabItem(i18n("训练")): + with gr.TabItem(i18n("Train")): gr.Markdown( value=i18n( - "### 第一步 填写实验配置\n实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件." + "### Step 1. Fill in the experimental configuration.\nExperimental data is stored in the 'logs' folder, with each experiment having a separate folder. Manually enter the experiment name path, which contains the experimental configuration, logs, and trained model files." ) ) with gr.Row(): - exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="mi-test") - author = gr.Textbox(label=i18n("模型作者(可空)")) + exp_dir1 = gr.Textbox( + label=i18n("Enter the experiment name"), value="mi-test" + ) + author = gr.Textbox(label=i18n("Model Author (Nullable)")) np7 = gr.Slider( minimum=0, maximum=config.n_cpu, step=1, - label=i18n("提取音高和处理数据使用的CPU进程数"), + label=i18n( + "Number of CPU processes used for pitch extraction and data processing" + ), value=int(np.ceil(config.n_cpu / 1.5)), interactive=True, ) with gr.Row(): sr2 = gr.Radio( - label=i18n("目标采样率"), + label=i18n("Target sample rate"), choices=["40k", "48k"], value="40k", interactive=True, ) if_f0_3 = gr.Radio( - label=i18n("模型是否带音高指导(唱歌一定要, 语音可以不要)"), - choices=[i18n("是"), i18n("否")], - value=i18n("是"), + label=i18n( + "Whether the model has pitch guidance (required for singing, optional for speech)" + ), + choices=[i18n("Yes"), i18n("No")], + value=i18n("Yes"), interactive=True, ) version19 = gr.Radio( - label=i18n("版本"), + label=i18n("Version"), choices=["v1", "v2"], value="v2", interactive=True, @@ -1179,25 +1230,25 @@ with gr.Blocks(title="RVC WebUI") as app: 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( - "### 第二步 音频处理\n#### 1. 音频切片\n自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练." + "### Step 2. Audio processing. \n#### 1. Slicing.\nAutomatically traverse all files in the training folder that can be decoded into audio and perform slice normalization. Generates 2 wav folders in the experiment directory. Currently, only single-singer/speaker training is supported." ) ) with gr.Row(): with gr.Column(): trainset_dir4 = gr.Textbox( - label=i18n("输入训练文件夹路径"), + label=i18n("Enter the path of the training folder"), ) spk_id5 = gr.Slider( minimum=0, maximum=4, step=1, - label=i18n("请指定说话人id"), + label=i18n("Please specify the speaker/singer ID"), value=0, interactive=True, ) - but1 = gr.Button(i18n("处理数据"), variant="primary") + but1 = gr.Button(i18n("Process data"), variant="primary") with gr.Column(): - info1 = gr.Textbox(label=i18n("输出信息"), value="") + info1 = gr.Textbox(label=i18n("Output information"), value="") but1.click( preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], @@ -1207,17 +1258,19 @@ with gr.Blocks(title="RVC WebUI") as app: with gr.Group(): gr.Markdown( value=i18n( - "#### 2. 特征提取\n使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)." + "#### 2. Feature extraction.\nUse CPU to extract pitch (if the model has pitch), use GPU to extract features (select GPU index)." ) ) with gr.Row(): with gr.Column(): gpu_info9 = gr.Textbox( - label=i18n("显卡信息"), value=gpu_info, visible=F0GPUVisible + label=i18n("GPU Information"), + value=gpu_info, + visible=F0GPUVisible, ) gpus6 = gr.Textbox( label=i18n( - "以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2" + "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2" ), value=gpus, interactive=True, @@ -1225,7 +1278,7 @@ with gr.Blocks(title="RVC WebUI") as app: ) gpus_rmvpe = gr.Textbox( label=i18n( - "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程" + "Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1" ), value="%s-%s" % (gpus, gpus), interactive=True, @@ -1233,15 +1286,15 @@ with gr.Blocks(title="RVC WebUI") as app: ) f0method8 = gr.Radio( label=i18n( - "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU" + "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("特征提取"), variant="primary") - info2 = gr.Textbox(label=i18n("输出信息"), value="") + but2 = gr.Button(i18n("Feature extraction"), variant="primary") + info2 = gr.Textbox(label=i18n("Output information"), value="") f0method8.change( fn=change_f0_method, inputs=[f0method8], @@ -1263,7 +1316,9 @@ with gr.Blocks(title="RVC WebUI") as app: ) with gr.Group(): gr.Markdown( - value=i18n("### 第三步 开始训练\n填写训练设置, 开始训练模型和索引.") + value=i18n( + "### Step 3. Start training.\nFill in the training settings and start training the model and index." + ) ) with gr.Row(): with gr.Column(): @@ -1271,7 +1326,7 @@ with gr.Blocks(title="RVC WebUI") as app: minimum=1, maximum=50, step=1, - label=i18n("保存频率save_every_epoch"), + label=i18n("Save frequency (save_every_epoch)"), value=5, interactive=True, ) @@ -1279,7 +1334,7 @@ with gr.Blocks(title="RVC WebUI") as app: minimum=2, maximum=1000, step=1, - label=i18n("总训练轮数total_epoch"), + label=i18n("Total training epochs (total_epoch)"), value=20, interactive=True, ) @@ -1287,46 +1342,48 @@ with gr.Blocks(title="RVC WebUI") as app: minimum=1, maximum=40, step=1, - label=i18n("每张显卡的batch_size"), + label=i18n("Batch size per GPU"), value=default_batch_size, interactive=True, ) if_save_latest13 = gr.Radio( - label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"), - choices=[i18n("是"), i18n("否")], - value=i18n("否"), + 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( - "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速" + "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("是"), i18n("否")], - value=i18n("否"), + choices=[i18n("Yes"), i18n("No")], + value=i18n("No"), interactive=True, ) if_save_every_weights18 = gr.Radio( label=i18n( - "是否在每次保存时间点将最终小模型保存至weights文件夹" + "Save a small final model to the 'weights' folder at each save point" ), - choices=[i18n("是"), i18n("否")], - value=i18n("否"), + choices=[i18n("Yes"), i18n("No")], + value=i18n("No"), interactive=True, ) with gr.Column(): pretrained_G14 = gr.Textbox( - label=i18n("加载预训练底模G路径"), + label=i18n("Load pre-trained base model G path"), value="assets/pretrained_v2/f0G40k.pth", interactive=True, ) pretrained_D15 = gr.Textbox( - label=i18n("加载预训练底模D路径"), + label=i18n("Load pre-trained base model D path"), value="assets/pretrained_v2/f0D40k.pth", interactive=True, ) gpus16 = gr.Textbox( label=i18n( - "以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2" + "Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2" ), value=gpus, interactive=True, @@ -1347,11 +1404,11 @@ with gr.Blocks(title="RVC WebUI") as app: [f0method8, gpus_rmvpe, pretrained_G14, pretrained_D15], ) - but3 = gr.Button(i18n("训练模型"), variant="primary") - but4 = gr.Button(i18n("训练特征索引"), variant="primary") - but5 = gr.Button(i18n("一键训练"), variant="primary") + 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("输出信息"), value="") + info3 = gr.Textbox(label=i18n("Output information"), value="") but3.click( click_train, [ @@ -1402,7 +1459,7 @@ with gr.Blocks(title="RVC WebUI") as app: api_name="train_start_all", ) - with gr.TabItem(i18n("ckpt处理")): + with gr.TabItem(i18n("ckpt Processing")): with gr.Group(): gr.Markdown( value=i18n( @@ -1411,12 +1468,14 @@ with gr.Blocks(title="RVC WebUI") as app: ) with gr.Row(): with gr.Column(): - id_a = gr.Textbox(label=i18n("A模型ID(长)"), value="") - id_b = gr.Textbox(label=i18n("B模型ID(长)"), value="") + 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("计算"), variant="primary") + butmodelcmp = gr.Button(i18n("Calculate"), variant="primary") infomodelcmp = gr.Textbox( - label=i18n("相似度(0到1)"), value="", max_lines=1 + label=i18n("Similarity (from 0 to 1)"), + value="", + max_lines=1, ) butmodelcmp.click( hash_similarity, @@ -1428,57 +1487,59 @@ with gr.Blocks(title="RVC WebUI") as app: api_name="ckpt_merge", ) with gr.Group(): - gr.Markdown(value=i18n("### 模型融合\n可用于测试音色融合")) + 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("A模型路径"), value="", interactive=True + label=i18n("Path to Model A"), value="", interactive=True ) ckpt_b = gr.Textbox( - label=i18n("B模型路径"), value="", interactive=True + label=i18n("Path to Model B"), value="", interactive=True ) alpha_a = gr.Slider( minimum=0, maximum=1, - label=i18n("A模型权重"), + label=i18n("Weight (w) for Model A"), value=0.5, interactive=True, ) with gr.Column(): sr_ = gr.Radio( - label=i18n("目标采样率"), + label=i18n("Target sample rate"), choices=["40k", "48k"], value="40k", interactive=True, ) if_f0_ = gr.Radio( - label=i18n("模型是否带音高指导"), - choices=[i18n("是"), i18n("否")], - value=i18n("是"), + label=i18n("Whether the model has pitch guidance"), + choices=[i18n("Yes"), i18n("No")], + value=i18n("Yes"), interactive=True, ) info__ = gr.Textbox( - label=i18n("要置入的模型信息"), + label=i18n("Model information to be placed"), value="", max_lines=8, interactive=True, ) with gr.Column(): name_to_save0 = gr.Textbox( - label=i18n("保存的模型名不带后缀"), + label=i18n("Saved model name (without extension)"), value="", max_lines=1, interactive=True, ) version_2 = gr.Radio( - label=i18n("模型版本型号"), + label=i18n("Model architecture version"), choices=["v1", "v2"], value="v1", interactive=True, ) - but6 = gr.Button(i18n("融合"), variant="primary") + but6 = gr.Button(i18n("Fusion"), variant="primary") with gr.Row(): - info4 = gr.Textbox(label=i18n("输出信息"), value="") + info4 = gr.Textbox(label=i18n("Output information"), value="") but6.click( merge, [ @@ -1497,29 +1558,31 @@ with gr.Blocks(title="RVC WebUI") as app: with gr.Group(): gr.Markdown( value=i18n( - "### 修改模型信息\n> 仅支持weights文件夹下提取的小模型文件" + "### Modify model information\n> Only supported for small model files extracted from the 'weights' folder." ) ) with gr.Row(): with gr.Column(): ckpt_path0 = gr.Textbox( - label=i18n("模型路径"), value="", interactive=True + label=i18n("Path to Model"), value="", interactive=True ) info_ = gr.Textbox( - label=i18n("要改的模型信息"), + label=i18n("Model information to be modified"), value="", max_lines=8, interactive=True, ) name_to_save1 = gr.Textbox( - label=i18n("保存的文件名, 默认空为和源文件同名"), + label=i18n( + "Save file name (default: same as the source file)" + ), value="", max_lines=1, interactive=True, ) with gr.Column(): - but7 = gr.Button(i18n("修改"), variant="primary") - info5 = gr.Textbox(label=i18n("输出信息"), value="") + 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], @@ -1529,66 +1592,68 @@ with gr.Blocks(title="RVC WebUI") as app: with gr.Group(): gr.Markdown( value=i18n( - "### 查看模型信息\n> 仅支持weights文件夹下提取的小模型文件" + "### View model information\n> Only supported for small model files extracted from the 'weights' folder." ) ) with gr.Row(): with gr.Column(): - ckpt_path1 = gr.File(label=i18n("模型路径")) - but8 = gr.Button(i18n("查看"), variant="primary") + ckpt_path1 = gr.File(label=i18n("Path to Model")) + but8 = gr.Button(i18n("View"), variant="primary") with gr.Column(): - info6 = gr.Textbox(label=i18n("输出信息"), value="") + 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( - "### 模型提取\n> 输入logs文件夹下大文件模型路径\n\n适用于训一半不想训了模型没有自动提取保存小文件模型, 或者想测试中间模型的情况" + "### Model extraction\n> Enter the path of the large file model under the 'logs' folder.\n\nThis is useful if you want to stop training halfway and manually extract and save a small model file, or if you want to test an intermediate model." ) ) with gr.Row(): with gr.Column(): ckpt_path2 = gr.Textbox( - label=i18n("模型路径"), + 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("保存名"), value="", interactive=True + label=i18n("Save name"), value="", interactive=True ) with gr.Row(): sr__ = gr.Radio( - label=i18n("目标采样率"), + label=i18n("Target sample rate"), choices=["32k", "40k", "48k"], value="40k", interactive=True, ) if_f0__ = gr.Radio( - label=i18n("模型是否带音高指导,1是0否"), + 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("模型版本型号"), + label=i18n("Model architecture version"), choices=["v1", "v2"], value="v2", interactive=True, ) info___ = gr.Textbox( - label=i18n("要置入的模型信息"), + label=i18n("Model information to be placed"), value="", max_lines=8, interactive=True, ) extauthor = gr.Textbox( - label=i18n("模型作者"), + label=i18n("Model Author"), value="", max_lines=1, interactive=True, ) with gr.Column(): - but9 = gr.Button(i18n("提取"), variant="primary") - info7 = gr.Textbox(label=i18n("输出信息"), value="") + 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] ) @@ -1607,27 +1672,27 @@ with gr.Blocks(title="RVC WebUI") as app: api_name="ckpt_extract", ) - with gr.TabItem(i18n("Onnx导出")): + with gr.TabItem(i18n("Export Onnx")): with gr.Row(): ckpt_dir = gr.Textbox( - label=i18n("RVC模型路径"), value="", interactive=True + label=i18n("RVC Model Path"), value="", interactive=True ) with gr.Row(): onnx_dir = gr.Textbox( - label=i18n("Onnx输出路径"), value="", interactive=True + label=i18n("Onnx Export Path"), value="", interactive=True ) with gr.Row(): infoOnnx = gr.Label(label="info") with gr.Row(): - butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary") + butOnnx = gr.Button(i18n("Export Onnx Model"), variant="primary") butOnnx.click( export_onnx, [ckpt_dir, onnx_dir], infoOnnx, api_name="export_onnx" ) - tab_faq = i18n("常见问题解答") + tab_faq = i18n("FAQ (Frequently Asked Questions)") with gr.TabItem(tab_faq): try: - if tab_faq == "常见问题解答": + if tab_faq == "FAQ (Frequently Asked Questions)": with open("docs/cn/faq.md", "r", encoding="utf8") as f: info = f.read() else: