mirror of
https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-06-05 01:10:22 +08:00
optimize(web): remove unnecessary gt.Groups
This commit is contained in:
16
web.py
16
web.py
@@ -831,7 +831,6 @@ with gr.Blocks(title="RVC WebUI") as app:
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)
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modelinfo = gr.Textbox(label=i18n("Model info"), max_lines=8)
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with gr.TabItem(i18n("Single inference")):
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with gr.Group():
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with gr.Row():
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with gr.Column():
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vc_transform0 = gr.Number(
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@@ -936,7 +935,7 @@ with gr.Blocks(title="RVC WebUI") as app:
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outputs=[sid0, file_index2],
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api_name="infer_refresh",
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)
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with gr.Group():
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vc_output1 = gr.Textbox(label=i18n("Output information"))
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but0.click(
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@@ -1121,7 +1120,6 @@ with gr.Blocks(title="RVC WebUI") as app:
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with gr.TabItem(
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i18n("Vocals/Accompaniment Separation & Reverberation Removal")
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):
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with gr.Group():
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gr.Markdown(
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value=i18n(
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"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."
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@@ -1227,7 +1225,6 @@ with gr.Blocks(title="RVC WebUI") as app:
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interactive=True,
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visible=True,
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)
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with gr.Group(): # 暂时单人的, 后面支持最多4人的#数据处理
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gr.Markdown(
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value=i18n(
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"### 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."
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@@ -1255,7 +1252,6 @@ with gr.Blocks(title="RVC WebUI") as app:
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[info1],
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api_name="train_preprocess",
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)
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with gr.Group():
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gr.Markdown(
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value=i18n(
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"#### 2. Feature extraction.\nUse CPU to extract pitch (if the model has pitch), use GPU to extract features (select GPU index)."
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@@ -1314,7 +1310,6 @@ with gr.Blocks(title="RVC WebUI") as app:
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[info2],
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api_name="train_extract_f0_feature",
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)
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with gr.Group():
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gr.Markdown(
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value=i18n(
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"### Step 3. Start training.\nFill in the training settings and start training the model and index."
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@@ -1460,7 +1455,6 @@ with gr.Blocks(title="RVC WebUI") as app:
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)
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with gr.TabItem(i18n("ckpt Processing")):
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with gr.Group():
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gr.Markdown(
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value=i18n(
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"### Model comparison\n> You can get model ID (long) from `View model information` below.\n\nCalculate a similarity between two models."
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@@ -1486,7 +1480,7 @@ with gr.Blocks(title="RVC WebUI") as app:
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infomodelcmp,
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api_name="ckpt_merge",
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)
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with gr.Group():
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gr.Markdown(
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value=i18n("### Model fusion\nCan be used to test timbre fusion.")
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)
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@@ -1555,7 +1549,7 @@ with gr.Blocks(title="RVC WebUI") as app:
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info4,
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api_name="ckpt_merge",
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) # def merge(path1,path2,alpha1,sr,f0,info):
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with gr.Group():
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gr.Markdown(
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value=i18n(
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"### Modify model information\n> Only supported for small model files extracted from the 'weights' folder."
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@@ -1589,7 +1583,7 @@ with gr.Blocks(title="RVC WebUI") as app:
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info5,
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api_name="ckpt_modify",
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)
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with gr.Group():
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gr.Markdown(
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value=i18n(
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"### View model information\n> Only supported for small model files extracted from the 'weights' folder."
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@@ -1602,7 +1596,7 @@ with gr.Blocks(title="RVC WebUI") as app:
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with gr.Column():
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info6 = gr.Textbox(label=i18n("Output information"), value="")
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but8.click(show_info, [ckpt_path1], info6, api_name="ckpt_show")
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with gr.Group():
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gr.Markdown(
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value=i18n(
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"### 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."
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