diff --git a/infer-web.py b/infer-web.py index 321e5b9..5148fe0 100644 --- a/infer-web.py +++ b/infer-web.py @@ -480,6 +480,7 @@ def click_train( if_cache_gpu17, if_save_every_weights18, version19, + author, ): # 生成filelist exp_dir = "%s/logs/%s" % (now_dir, exp_dir1) @@ -548,8 +549,6 @@ def click_train( with open("%s/filelist.txt" % exp_dir, "w") as f: f.write("\n".join(opt)) logger.debug("Write filelist done") - # 生成config#无需生成config - # cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0" logger.info("Use gpus: %s", str(gpus16)) if pretrained_G14 == "": logger.info("No pretrained Generator") @@ -570,45 +569,25 @@ def click_train( sort_keys=True, ) f.write("\n") + cmd = '"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s -a "%s"' % ( + config.python_cmd, + exp_dir1, + sr2, + 1 if if_f0_3 else 0, + batch_size12, + total_epoch11, + 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, + version19, + author + ) if gpus16: - cmd = ( - '"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s' - % ( - config.python_cmd, - exp_dir1, - sr2, - 1 if if_f0_3 else 0, - batch_size12, - gpus16, - total_epoch11, - 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, - version19, - ) - ) - else: - cmd = ( - '"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s' - % ( - config.python_cmd, - exp_dir1, - sr2, - 1 if if_f0_3 else 0, - batch_size12, - total_epoch11, - 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, - version19, - ) - ) + cmd += '-g "%s"' % (gpus16) + logger.info("Execute: " + cmd) p = Popen(cmd, shell=True, cwd=now_dir) p.wait() @@ -734,6 +713,7 @@ def train1key( if_save_every_weights18, version19, gpus_rmvpe, + author ): infos = [] @@ -771,6 +751,7 @@ def train1key( if_cache_gpu17, if_save_every_weights18, version19, + 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") @@ -837,7 +818,7 @@ 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")) + modelinfo = gr.Textbox(label=i18n("模型信息"), max_lines=8) with gr.TabItem(i18n("单次推理")): with gr.Group(): with gr.Row(): @@ -1066,8 +1047,7 @@ with gr.Blocks(title="RVC WebUI") as app: interactive=True, ) but1 = gr.Button(i18n("转换"), variant="primary") - - vc_output3 = gr.Textbox(label=i18n("输出信息")) + vc_output3 = gr.Textbox(label=i18n("输出信息")) but1.click( vc.vc_multi, @@ -1165,11 +1145,21 @@ with gr.Blocks(title="RVC WebUI") as app: with gr.TabItem(i18n("训练")): gr.Markdown( value=i18n( - "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. " + "### 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." ) ) 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)")) + 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"), + value=int(np.ceil(config.n_cpu / 1.5)), + interactive=True, + ) + with gr.Row(): sr2 = gr.Radio( label=i18n("目标采样率"), choices=["40k", "48k"], @@ -1189,49 +1179,46 @@ with gr.Blocks(title="RVC WebUI") as app: interactive=True, visible=True, ) - 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"), - value=int(np.ceil(config.n_cpu / 1.5)), - interactive=True, - ) with gr.Group(): # 暂时单人的, 后面支持最多4人的#数据处理 gr.Markdown( value=i18n( - "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. " - ) - ) - with gr.Row(): - trainset_dir4 = gr.Textbox( - label=i18n("输入训练文件夹路径"), - value=i18n("E:\\语音音频+标注\\米津玄师\\src"), - ) - spk_id5 = gr.Slider( - minimum=0, - maximum=4, - step=1, - label=i18n("请指定说话人id"), - value=0, - interactive=True, - ) - but1 = gr.Button(i18n("处理数据"), variant="primary") - info1 = gr.Textbox(label=i18n("输出信息"), value="") - but1.click( - preprocess_dataset, - [trainset_dir4, exp_dir1, sr2, np7], - [info1], - api_name="train_preprocess", - ) - with gr.Group(): - gr.Markdown( - value=i18n( - "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)" + "### 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." ) ) with gr.Row(): with gr.Column(): + trainset_dir4 = gr.Textbox( + label=i18n("输入训练文件夹路径"), + value=i18n("E:\\语音音频+标注\\米津玄师\\src"), + ) + spk_id5 = gr.Slider( + minimum=0, + maximum=4, + step=1, + label=i18n("请指定说话人id"), + value=0, + interactive=True, + ) + but1 = gr.Button(i18n("处理数据"), variant="primary") + with gr.Column(): + info1 = gr.Textbox(label=i18n("输出信息"), value="") + but1.click( + preprocess_dataset, + [trainset_dir4, exp_dir1, sr2, np7], + [info1], + api_name="train_preprocess", + ) + 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)." + ) + ) + with gr.Row(): + with gr.Column(): + gpu_info9 = gr.Textbox( + label=i18n("显卡信息"), 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" @@ -1240,18 +1227,6 @@ with gr.Blocks(title="RVC WebUI") as app: interactive=True, visible=F0GPUVisible, ) - gpu_info9 = gr.Textbox( - label=i18n("显卡信息"), value=gpu_info, visible=F0GPUVisible - ) - with gr.Column(): - 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" - ), - choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"], - value="rmvpe_gpu", - interactive=True, - ) 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" @@ -1260,8 +1235,17 @@ with gr.Blocks(title="RVC WebUI") as app: interactive=True, visible=F0GPUVisible, ) - but2 = gr.Button(i18n("特征提取"), variant="primary") - info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) + 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" + ), + 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="", max_lines=8) f0method8.change( fn=change_f0_method, inputs=[f0method8], @@ -1282,90 +1266,93 @@ with gr.Blocks(title="RVC WebUI") as app: api_name="train_extract_f0_feature", ) with gr.Group(): - gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引")) + 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.")) with gr.Row(): - save_epoch10 = gr.Slider( - minimum=1, - maximum=50, - step=1, - label=i18n("保存频率save_every_epoch"), - value=5, - interactive=True, - ) - total_epoch11 = gr.Slider( - minimum=2, - maximum=1000, - step=1, - label=i18n("总训练轮数total_epoch"), - value=20, - interactive=True, - ) - batch_size12 = gr.Slider( - minimum=1, - maximum=40, - step=1, - label=i18n("每张显卡的batch_size"), - 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"), - 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" - ), - choices=[i18n("是"), i18n("否")], - value=i18n("否"), - 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" - ), - choices=[i18n("是"), i18n("否")], - value=i18n("否"), - interactive=True, - ) + with gr.Column(): + save_epoch10 = gr.Slider( + minimum=1, + maximum=50, + step=1, + label=i18n("保存频率save_every_epoch"), + value=5, + interactive=True, + ) + total_epoch11 = gr.Slider( + minimum=2, + maximum=1000, + step=1, + label=i18n("总训练轮数total_epoch"), + value=20, + interactive=True, + ) + batch_size12 = gr.Slider( + minimum=1, + maximum=40, + step=1, + label=i18n("每张显卡的batch_size"), + 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"), + 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" + ), + choices=[i18n("是"), i18n("否")], + value=i18n("否"), + 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" + ), + choices=[i18n("是"), i18n("否")], + value=i18n("否"), + interactive=True, + ) + with gr.Column(): + pretrained_G14 = gr.Textbox( + label=i18n("加载预训练底模G路径"), + value="assets/pretrained_v2/f0G40k.pth", + interactive=True, + ) + pretrained_D15 = gr.Textbox( + label=i18n("加载预训练底模D路径"), + 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" + ), + value=gpus, + interactive=True, + ) + sr2.change( + change_sr2, + [sr2, if_f0_3, version19], + [pretrained_G14, pretrained_D15], + ) + version19.change( + change_version19, + [sr2, if_f0_3, version19], + [pretrained_G14, pretrained_D15, sr2], + ) + if_f0_3.change( + change_f0, + [if_f0_3, sr2, version19], + [f0method8, gpus_rmvpe, pretrained_G14, pretrained_D15], + ) + + but3 = gr.Button(i18n("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") with gr.Row(): - pretrained_G14 = gr.Textbox( - label=i18n("加载预训练底模G路径"), - value="assets/pretrained_v2/f0G40k.pth", - interactive=True, - ) - pretrained_D15 = gr.Textbox( - label=i18n("加载预训练底模D路径"), - value="assets/pretrained_v2/f0D40k.pth", - interactive=True, - ) - sr2.change( - change_sr2, - [sr2, if_f0_3, version19], - [pretrained_G14, pretrained_D15], - ) - version19.change( - change_version19, - [sr2, if_f0_3, version19], - [pretrained_G14, pretrained_D15, sr2], - ) - if_f0_3.change( - change_f0, - [if_f0_3, sr2, version19], - [f0method8, gpus_rmvpe, pretrained_G14, pretrained_D15], - ) - 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" - ), - value=gpus, - interactive=True, - ) - 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") info3 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10) but3.click( click_train, @@ -1384,6 +1371,7 @@ with gr.Blocks(title="RVC WebUI") as app: if_cache_gpu17, if_save_every_weights18, version19, + author, ], info3, api_name="train_start", @@ -1410,6 +1398,7 @@ with gr.Blocks(title="RVC WebUI") as app: if_save_every_weights18, version19, gpus_rmvpe, + author, ], info3, api_name="train_start_all", @@ -1443,52 +1432,54 @@ with gr.Blocks(title="RVC WebUI") as app: with gr.Group(): gr.Markdown(value=i18n("### 模型融合\n可用于测试音色融合")) with gr.Row(): - ckpt_a = gr.Textbox( - label=i18n("A模型路径"), value="", interactive=True - ) - ckpt_b = gr.Textbox( - label=i18n("B模型路径"), value="", interactive=True - ) - alpha_a = gr.Slider( - minimum=0, - maximum=1, - label=i18n("A模型权重"), - value=0.5, - interactive=True, - ) + with gr.Column(): + ckpt_a = gr.Textbox( + label=i18n("A模型路径"), value="", interactive=True + ) + ckpt_b = gr.Textbox( + label=i18n("B模型路径"), value="", interactive=True + ) + alpha_a = gr.Slider( + minimum=0, + maximum=1, + label=i18n("A模型权重"), + value=0.5, + interactive=True, + ) + with gr.Column(): + sr_ = gr.Radio( + label=i18n("目标采样率"), + 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"), + interactive=True, + ) + info__ = gr.Textbox( + label=i18n("要置入的模型信息"), + 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"), + value="", + max_lines=1, + interactive=True, + ) + version_2 = gr.Radio( + label=i18n("模型版本型号"), + choices=["v1", "v2"], + value="v1", + interactive=True, + ) + but6 = gr.Button(i18n("融合"), variant="primary") with gr.Row(): - sr_ = gr.Radio( - label=i18n("目标采样率"), - 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"), - interactive=True, - ) - info__ = gr.Textbox( - label=i18n("要置入的模型信息"), - value="", - max_lines=8, - interactive=True, - ) - name_to_save0 = gr.Textbox( - label=i18n("保存的模型名不带后缀"), - value="", - max_lines=1, - interactive=True, - ) - version_2 = gr.Radio( - label=i18n("模型版本型号"), - choices=["v1", "v2"], - value="v1", - interactive=True, - ) - with gr.Row(): - but6 = gr.Button(i18n("融合"), variant="primary") info4 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) but6.click( merge, @@ -1512,24 +1503,25 @@ with gr.Blocks(title="RVC WebUI") as app: ) ) with gr.Row(): - ckpt_path0 = gr.Textbox( - label=i18n("模型路径"), value="", interactive=True - ) - info_ = gr.Textbox( - label=i18n("要改的模型信息"), - 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"), - value="", - max_lines=8, - interactive=True, - ) - with gr.Row(): - but7 = gr.Button(i18n("修改"), variant="primary") - info5 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) + with gr.Column(): + ckpt_path0 = gr.Textbox( + label=i18n("模型路径"), value="", interactive=True + ) + info_ = gr.Textbox( + label=i18n("要改的模型信息"), + 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"), + value="", + max_lines=8, + interactive=True, + ) + with gr.Column(): + but7 = gr.Button(i18n("修改"), variant="primary") + info5 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) but7.click( change_info, [ckpt_path0, info_, name_to_save1], @@ -1543,11 +1535,13 @@ with gr.Blocks(title="RVC WebUI") as app: ) ) with gr.Row(): - ckpt_path1 = gr.Textbox( - label=i18n("模型路径"), value="", interactive=True - ) - but8 = gr.Button(i18n("查看"), variant="primary") - info6 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) + with gr.Column(): + ckpt_path1 = gr.Textbox( + label=i18n("模型路径"), value="", interactive=True + ) + but8 = gr.Button(i18n("查看"), variant="primary") + with gr.Column(): + info6 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) but8.click(show_info, [ckpt_path1], info6, api_name="ckpt_show") with gr.Group(): gr.Markdown( @@ -1556,46 +1550,55 @@ with gr.Blocks(title="RVC WebUI") as app: ) ) with gr.Row(): - ckpt_path2 = gr.Textbox( - label=i18n("模型路径"), - 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 - ) - sr__ = gr.Radio( - label=i18n("目标采样率"), - 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"), - choices=["1", "0"], - value="1", - interactive=True, - ) - version_1 = gr.Radio( - label=i18n("模型版本型号"), - choices=["v1", "v2"], - value="v2", - interactive=True, - ) - info___ = gr.Textbox( - label=i18n("要置入的模型信息"), - value="", - max_lines=8, - interactive=True, - ) - but9 = gr.Button(i18n("提取"), variant="primary") - info7 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) - ckpt_path2.change( - change_info_, [ckpt_path2], [sr__, if_f0__, version_1] - ) + with gr.Column(): + ckpt_path2 = gr.Textbox( + label=i18n("模型路径"), + 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 + ) + with gr.Row(): + sr__ = gr.Radio( + label=i18n("目标采样率"), + 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"), + choices=["1", "0"], + value="1", + interactive=True, + ) + version_1 = gr.Radio( + label=i18n("模型版本型号"), + choices=["v1", "v2"], + value="v2", + interactive=True, + ) + info___ = gr.Textbox( + label=i18n("要置入的模型信息"), + value="", + max_lines=8, + interactive=True, + ) + extauthor = gr.Textbox( + label=i18n("模型作者"), + 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="", max_lines=8) + ckpt_path2.change( + change_info_, [ckpt_path2], [sr__, if_f0__, version_1] + ) but9.click( extract_small_model, - [ckpt_path2, save_name, sr__, if_f0__, info___, version_1], + [ckpt_path2, save_name, extauthor, sr__, if_f0__, info___, version_1], info7, api_name="ckpt_extract", ) diff --git a/infer/lib/train/process_ckpt.py b/infer/lib/train/process_ckpt.py index 47a0bc7..7c1b3d4 100644 --- a/infer/lib/train/process_ckpt.py +++ b/infer/lib/train/process_ckpt.py @@ -43,6 +43,7 @@ def save_small_model(ckpt, sr, if_f0, name, epoch, version, hps): opt["info"] = "%sepoch" % epoch opt["name"] = name opt["timestamp"] = int(time()) + if hps.author: opt["author"] = hps.author opt["sr"] = sr opt["f0"] = if_f0 opt["version"] = version @@ -55,7 +56,7 @@ def save_small_model(ckpt, sr, if_f0, name, epoch, version, hps): return traceback.format_exc() -def extract_small_model(path, name, sr, if_f0, info, version): +def extract_small_model(path, name, author, sr, if_f0, info, version): try: ckpt = torch.load(path, map_location="cpu") if "model" in ckpt: @@ -178,6 +179,7 @@ def extract_small_model(path, name, sr, if_f0, info, version): opt["info"] = info opt["name"] = name opt["timestamp"] = int(time()) + if author: opt["author"] = author opt["version"] = version opt["sr"] = sr opt["f0"] = int(if_f0) @@ -214,6 +216,13 @@ def merge(path1, path2, alpha1, sr, f0, info, name, version): continue opt["weight"][key] = a[key] return opt + + def authors(c1, c2): + a1, a2 = c1.get("author", ""), c2.get("author", "") + if a1 == a2: return a1 + if not a1: a1 = "Unknown" + if not a2: a2 = "Unknown" + return f"{a1} & {a2}" ckpt1 = torch.load(path1, map_location="cpu") ckpt2 = torch.load(path2, map_location="cpu") @@ -242,8 +251,7 @@ def merge(path1, path2, alpha1, sr, f0, info, name, version): opt["weight"][key] = ( alpha1 * (ckpt1[key].float()) + (1 - alpha1) * (ckpt2[key].float()) ).half() - # except: - # pdb.set_trace() + author = authors(ckpt1, ckpt2) opt["config"] = cfg """ if(sr=="40k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 40000] @@ -252,6 +260,7 @@ def merge(path1, path2, alpha1, sr, f0, info, name, version): """ opt["name"] = name opt["timestamp"] = int(time()) + if author: opt["author"] = author opt["sr"] = sr opt["f0"] = 1 if f0 == i18n("是") else 0 opt["version"] = version diff --git a/infer/lib/train/utils.py b/infer/lib/train/utils.py index e7bb783..e55a4df 100644 --- a/infer/lib/train/utils.py +++ b/infer/lib/train/utils.py @@ -358,6 +358,9 @@ def get_hparams(init=True): required=True, help="if caching the dataset in GPU memory, 1 or 0", ) + parser.add_argument( + "-a", "--author", type=str, default="", help="Model author" + ) args = parser.parse_args() name = args.experiment_dir @@ -383,9 +386,10 @@ def get_hparams(init=True): hparams.save_every_weights = args.save_every_weights hparams.if_cache_data_in_gpu = args.if_cache_data_in_gpu hparams.data.training_files = "%s/filelist.txt" % experiment_dir + hparams.author = args.author return hparams - +""" def get_hparams_from_dir(model_dir): config_save_path = os.path.join(model_dir, "config.json") with open(config_save_path, "r") as f: @@ -429,7 +433,7 @@ def check_git_hash(model_dir): ) else: open(path, "w").write(cur_hash) - +""" def get_logger(model_dir, filename="train.log"): global logger diff --git a/infer/modules/vc/info.py b/infer/modules/vc/info.py index a7647bb..ea99c73 100644 --- a/infer/modules/vc/info.py +++ b/infer/modules/vc/info.py @@ -38,18 +38,20 @@ def show_model_info(cpt, show_long_id=False): ) txt = f"""{i18n("模型名")}: %s {i18n("封装时间")}: %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""" % ( - cpt.get("name", "None"), + cpt.get("name", i18n("Unknown")), datetime.fromtimestamp(float(cpt.get("timestamp", 0))), - cpt.get("info", "None"), - cpt.get("sr", "None"), + 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"), - cpt.get("version", "None"), + cpt.get("version", i18n("None")), id, h, )