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from app.original import *
def infer_tabs():
with gr.TabItem(i18n("模型推理")):
with gr.Row():
sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names))
with gr.Column():
refresh_button = gr.Button(
i18n("刷新音色列表和索引路径"), 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"),
value=0,
visible=False,
interactive=True,
)
clean_button.click(
fn=clean, inputs=[], outputs=[sid0], api_name="infer_clean"
)
with gr.TabItem(i18n("单次推理")):
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)"),
value=0,
)
input_audio0 = gr.Textbox(
label=i18n(
"输入待处理音频文件路径(默认是正确格式示例)"
),
placeholder="C:\\Users\\Desktop\\audio_example.wav",
)
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"
),
placeholder="C:\\Users\\Desktop\\model_example.index",
interactive=True,
)
file_index2 = gr.Dropdown(
label=i18n("自动检测index路径,下拉式选择(dropdown)"),
choices=sorted(index_paths),
interactive=True,
)
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"
),
choices=(
["pm", "harvest", "crepe", "rmvpe"]
if config.dml == False
else ["pm", "harvest", "rmvpe"]
),
value="rmvpe",
interactive=True,
)
with gr.Column():
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"),
value=0,
step=1,
interactive=True,
)
rms_mix_rate0 = gr.Slider(
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"
),
value=0.25,
interactive=True,
)
protect0 = gr.Slider(
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"
),
value=0.33,
step=0.01,
interactive=True,
)
filter_radius0 = gr.Slider(
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"
),
value=3,
step=1,
interactive=True,
)
index_rate1 = gr.Slider(
minimum=0,
maximum=1,
label=i18n("检索特征占比"),
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"
),
visible=False,
)
refresh_button.click(
fn=change_choices,
inputs=[],
outputs=[sid0, file_index2],
api_name="infer_refresh",
)
# file_big_npy1 = gr.Textbox(
# label=i18n("特征文件路径"),
# value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
# interactive=True,
# )
with gr.Group():
with gr.Column():
but0 = gr.Button(i18n("转换"), variant="primary")
with gr.Row():
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,
[
spk_item,
input_audio0,
vc_transform0,
f0_file,
f0method0,
file_index1,
file_index2,
# file_big_npy1,
index_rate1,
filter_radius0,
resample_sr0,
rms_mix_rate0,
protect0,
],
[vc_output1, vc_output2],
api_name="infer_convert",
)
with gr.TabItem(i18n("批量推理")):
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. "
)
)
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)"),
value=0,
)
opt_input = gr.Textbox(
label=i18n("指定输出文件夹"), value="opt"
)
file_index3 = 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"),
value="",
interactive=True,
)
file_index4 = gr.Dropdown(
label=i18n("自动检测index路径,下拉式选择(dropdown)"),
choices=sorted(index_paths),
interactive=True,
)
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"
),
choices=(
["pm", "harvest", "crepe", "rmvpe"]
if config.dml == False
else ["pm", "harvest", "rmvpe"]
),
value="rmvpe",
interactive=True,
)
format1 = gr.Radio(
label=i18n("导出文件格式"),
choices=["wav", "flac", "mp3", "m4a"],
value="wav",
interactive=True,
)
refresh_button.click(
fn=lambda: change_choices()[1],
inputs=[],
outputs=file_index4,
api_name="infer_refresh_batch",
)
# file_big_npy2 = gr.Textbox(
# label=i18n("特征文件路径"),
# value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
# interactive=True,
# )
with gr.Column():
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"),
value=0,
step=1,
interactive=True,
)
rms_mix_rate1 = gr.Slider(
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"
),
value=1,
interactive=True,
)
protect1 = gr.Slider(
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"
),
value=0.33,
step=0.01,
interactive=True,
)
filter_radius1 = gr.Slider(
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"
),
value=3,
step=1,
interactive=True,
)
index_rate2 = gr.Slider(
minimum=0,
maximum=1,
label=i18n("检索特征占比"),
value=1,
interactive=True,
)
with gr.Row():
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)"
),
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"),
)
with gr.Row():
but1 = gr.Button(i18n("转换"), variant="primary")
vc_output3 = gr.Textbox(label=i18n("输出信息"))
but1.click(
vc.vc_multi,
[
spk_item,
dir_input,
opt_input,
inputs,
vc_transform1,
f0method1,
file_index3,
file_index4,
# file_big_npy2,
index_rate2,
filter_radius1,
resample_sr1,
rms_mix_rate1,
protect1,
format1,
],
[vc_output3],
api_name="infer_convert_batch",
)
sid0.change(
fn=vc.get_vc,
inputs=[sid0, protect0, protect1],
outputs=[spk_item, protect0, protect1, file_index2, file_index4],
api_name="infer_change_voice",
)
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