File size: 5,430 Bytes
7bc29af
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
import logging
import os

# os.system("wget -P cvec/ https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt")
import gradio as gr
from dotenv import load_dotenv

from configs.config import Config
from i18n import I18nAuto
from infer.modules.vc.pipeline import Pipeline
VC = Pipeline

logging.getLogger("numba").setLevel(logging.WARNING)
logging.getLogger("markdown_it").setLevel(logging.WARNING)
logging.getLogger("urllib3").setLevel(logging.WARNING)
logging.getLogger("matplotlib").setLevel(logging.WARNING)
logger = logging.getLogger(__name__)

i18n = I18nAuto()
#(i18n)

load_dotenv()
config = Config()
vc = VC(config)

weight_root = os.getenv("weight_root")
weight_uvr5_root = os.getenv("weight_uvr5_root")
index_root = os.getenv("index_root")
names = []
hubert_model = None
for name in os.listdir(weight_root):
    if name.endswith(".pth"):
        names.append(name)
index_paths = []
for root, dirs, files in os.walk(index_root, topdown=False):
    for name in files:
        if name.endswith(".index") and "trained" not in name:
            index_paths.append("%s/%s" % (root, name))


app = gr.Blocks()
with app:
    with gr.Tabs():
        with gr.TabItem("在线demo"):
            gr.Markdown(
                value="""
                RVC 在线demo
                """
            )
            sid = gr.Dropdown(label=i18n("推理音色"), 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"),
                    value=0,
                    visible=False,
                    interactive=True,
                )
            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. ")
            )
            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)
            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"),
                choices=["pm", "harvest", "crepe", "rmvpe"],
                value="pm",
                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,
            )
            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"),
                    value="",
                    interactive=False,
                    visible=False,
                )
            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,
            )
            index_rate1 = gr.Slider(
                minimum=0,
                maximum=1,
                label=i18n("检索特征占比"),
                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"),
                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=1,
                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,
            )
            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"))
            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,
                [
                    spk_item,
                    vc_input3,
                    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],
            )


app.launch()