">=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模型权重":"Weight (w) for Model A:",
"A模型路径":"Path to Model A:",
"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:",
"Index Rate":"Index Rate",
"Onnx导出":"Export Onnx",
"Onnx输出路径":"Onnx Export Path:",
"RVC模型路径":"RVC Model Path:",
"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":"Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1",
"step1: 填写实验配置. 实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件. ":"Step 1: Fill in the experimental configuration. Experimental data is stored in the 'logs' folder, with each experiment having a separate folder. Manually enter the experiment name path, which contains the experimental configuration, logs, and trained model files.",
"step2a: 自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练. ":"Step 2a: Automatically traverse all files in the training folder that can be decoded into audio and perform slice normalization. Generates 2 wav folders in the experiment directory. Currently, only single-singer/speaker training is supported.",
"step2b: 使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)":"Step 2b: Use CPU to extract pitch (if the model has pitch), use GPU to extract features (select GPU index):",
"step3: 填写训练设置, 开始训练模型和索引":"Step 3: Fill in the training settings and start training the model and index",
"人声伴奏分离批量处理， 使用UVR5模型。 <br>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)。 <br>模型分为三类： <br>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； <br>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； <br> 3、去混响、去延迟模型（by FoxJoy）：<br>(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；<br> (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。<br>去混响/去延迟，附：<br>1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；<br>2、MDX-Net-Dereverb模型挺慢的；<br>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.<br>Example of a valid folder path format: D:\\path\\to\\input\\folder (copy it from the file manager address bar).<br>The model is divided into three categories:<br>1. Preserve vocals: Choose this option for audio without harmonies. It preserves vocals better than HP5. It includes two built-in models: HP2 and HP3. HP3 may slightly leak accompaniment but preserves vocals slightly better than HP2.<br>2. Preserve main vocals only: Choose this option for audio with harmonies. It may weaken the main vocals. It includes one built-in model: HP5.<br>3. De-reverb and de-delay models (by FoxJoy):<br>(1) MDX-Net: The best choice for stereo reverb removal but cannot remove mono reverb;<br> (234) DeEcho: Removes delay effects. Aggressive mode removes more thoroughly than Normal mode. DeReverb additionally removes reverb and can remove mono reverb, but not very effectively for heavily reverberated high-frequency content.<br>De-reverb/de-delay notes:<br>1. The processing time for the DeEcho-DeReverb model is approximately twice as long as the other two DeEcho models.<br>2. The MDX-Net-Dereverb model is quite slow.<br>3. The recommended cleanest configuration is to apply MDX-Net first and then DeEcho-Aggressive.",
"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:",
"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:",
"修改":"Modify",
"修改模型信息(仅支持weights文件夹下提取的小模型文件)":"Modify model information (only supported for small model files extracted from the 'weights' folder)",
"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):",
"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').",
"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",
"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. <br>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<b>LICENSE</b>.":"This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible. <br>If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory <b>Agreement-LICENSE.txt</b> for details.",
"查看":"View",
"查看模型信息(仅支持weights文件夹下提取的小模型文件)":"View model information (only supported for small model files extracted from the 'weights' folder)",
"检索特征占比":"Search feature ratio (controls accent strength, too high has artifacting):",
"模型":"Model",
"模型推理":"Model Inference",
"模型提取(输入logs文件夹下大文件模型路径),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 (enter the path of the large file model under the 'logs' folder). This 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:",
"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:",
"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:",
"自动检测index路径,下拉式选择(dropdown)":"Auto-detect index path and select from the dropdown:",
"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.",
"请指定说话人id":"Please specify the speaker/singer 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":"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):",
"输入待处理音频文件路径(默认是正确格式示例)":"Enter the path of the audio file to be processed (default is the correct format example):",
"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:",
"输入训练文件夹路径":"Enter the path of the training folder:",
"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",
