File size: 9,490 Bytes
745a860
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
from original import *
import shutil, glob
from easyfuncs import download_from_url, CachedModels
os.makedirs("dataset",exist_ok=True)
model_library = CachedModels()

with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="rose",neutral_hue="zinc")) as app:
    with gr.Row():
        gr.HTML("<img  src='file/a.png' alt='image'>")
    with gr.Tabs():
        with gr.TabItem("Inference"):
            with gr.Row():
                voice_model = gr.Dropdown(label="Model Voice", choices=sorted(names), value=lambda:sorted(names)[0] if len(sorted(names)) > 0 else '', interactive=True)
                refresh_button = gr.Button("Refresh", variant="primary")
                spk_item = gr.Slider(
                    minimum=0,
                    maximum=2333,
                    step=1,
                    label="Speaker ID",
                    value=0,
                    visible=False,
                    interactive=True,
                )
                vc_transform0 = gr.Number(
                    label="Pitch", 
                    value=0
                )
                but0 = gr.Button(value="Convert", variant="primary")
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        dropbox = gr.File(label="Drop your audio here & hit the Reload button.")
                    with gr.Row():
                        record_button=gr.Audio(source="microphone", label="OR Record audio.", type="filepath")
                    with gr.Row():
                        paths_for_files = lambda path:[os.path.abspath(os.path.join(path, f)) for f in os.listdir(path) if os.path.splitext(f)[1].lower() in ('.mp3', '.wav', '.flac', '.ogg')]
                        input_audio0 = gr.Dropdown(
                            label="Input Path",
                            value=paths_for_files('audios')[0] if len(paths_for_files('audios')) > 0 else '',
                            choices=paths_for_files('audios'), # Only show absolute paths for audio files ending in .mp3, .wav, .flac or .ogg
                            allow_custom_value=True
                        )
                    with gr.Row():
                        audio_player = gr.Audio()
                        input_audio0.change(
                            inputs=[input_audio0],
                            outputs=[audio_player],
                            fn=lambda path: {"value":path,"__type__":"update"} if os.path.exists(path) else None
                        )
                        record_button.stop_recording(
                            fn=lambda audio:audio, #TODO save wav lambda
                            inputs=[record_button], 
                            outputs=[input_audio0])
                        dropbox.upload(
                            fn=lambda audio:audio.name,
                            inputs=[dropbox], 
                            outputs=[input_audio0])
                with gr.Column():
                    with gr.Accordion("Change Index", open=False):
                        file_index2 = gr.Dropdown(
                            label="Change Index",
                            choices=sorted(index_paths),
                            interactive=True,
                            value=sorted(index_paths)[0] if len(sorted(index_paths)) > 0 else ''
                        )
                        index_rate1 = gr.Slider(
                            minimum=0,
                            maximum=1,
                            label="Index Strength",
                            value=0.5,
                            interactive=True,
                        )
                    vc_output2 = gr.Audio(label="Output")
                    with gr.Accordion("General Settings", open=False):
                        f0method0 = gr.Radio(
                            label="Method",
                            choices=["pm", "harvest", "crepe", "rmvpe"]
                            if config.dml == False
                            else ["pm", "harvest", "rmvpe"],
                            value="rmvpe",
                            interactive=True,
                        )
                        filter_radius0 = gr.Slider(
                            minimum=0,
                            maximum=7,
                            label="Breathiness Reduction (Harvest only)",
                            value=3,
                            step=1,
                            interactive=True,
                        )
                        resample_sr0 = gr.Slider(
                            minimum=0,
                            maximum=48000,
                            label="Resample",
                            value=0,
                            step=1,
                            interactive=True,
                            visible=False
                        )
                        rms_mix_rate0 = gr.Slider(
                            minimum=0,
                            maximum=1,
                            label="Volume Normalization",
                            value=0,
                            interactive=True,
                        )
                        protect0 = gr.Slider(
                            minimum=0,
                            maximum=0.5,
                            label="Breathiness Protection (0 is enabled, 0.5 is disabled)",
                            value=0.33,
                            step=0.01,
                            interactive=True,
                        )
                        if voice_model != None: vc.get_vc(voice_model.value,protect0,protect0)
                    file_index1 = gr.Textbox(
                        label="Index Path",
                        interactive=True,
                        visible=False#Not used here
                    )
                    refresh_button.click(
                        fn=change_choices,
                        inputs=[],
                        outputs=[voice_model, file_index2],
                        api_name="infer_refresh",
                    )
                    refresh_button.click(
                        fn=lambda:{"choices":paths_for_files('audios'),"__type__":"update"}, #TODO check if properly returns a sorted list of audio files in the 'audios' folder that have the extensions '.wav', '.mp3', '.ogg', or '.flac'
                        inputs=[],
                        outputs = [input_audio0],   
                    )
                    refresh_button.click(
                        fn=lambda:{"value":paths_for_files('audios')[0],"__type__":"update"} if len(paths_for_files('audios')) > 0 else {"value":"","__type__":"update"}, #TODO check if properly returns a sorted list of audio files in the 'audios' folder that have the extensions '.wav', '.mp3', '.ogg', or '.flac'
                        inputs=[],
                        outputs = [input_audio0],   
                    )
            with gr.Row():
                f0_file = gr.File(label="F0 Path", visible=False)
            with gr.Row():
                vc_output1 = gr.Textbox(label="Information", placeholder="Welcome!",visible=False)
                but0.click(
                    vc.vc_single,  
                    [
                        spk_item,
                        input_audio0,
                        vc_transform0,
                        f0_file,
                        f0method0,
                        file_index1,
                        file_index2,
                        index_rate1,
                        filter_radius0,
                        resample_sr0,
                        rms_mix_rate0,
                        protect0,
                    ],
                    [vc_output1, vc_output2],
                    api_name="infer_convert",
                )  
                voice_model.change(
                    fn=vc.get_vc,
                    inputs=[voice_model, protect0, protect0],
                    outputs=[spk_item, protect0, protect0, file_index2, file_index2],
                    api_name="infer_change_voice",
                )
        with gr.TabItem("Download Models"):
            with gr.Row():
                url_input = gr.Textbox(label="URL to model", value="",placeholder="https://...", scale=6)
                name_output = gr.Textbox(label="Save as", value="",placeholder="MyModel",scale=2)
                url_download = gr.Button(value="Download Model",scale=2)
                url_download.click(
                    inputs=[url_input,name_output],
                    outputs=[url_input],
                    fn=download_from_url,
                )
            with gr.Row():
                model_browser = gr.Dropdown(choices=list(model_library.models.keys()),label="OR Search Models (Quality UNKNOWN)",scale=5)
                download_from_browser = gr.Button(value="Get",scale=2)
                download_from_browser.click(
                    inputs=[model_browser],
                    outputs=[model_browser],
                    fn=lambda model: download_from_url(model_library.models[model],model),
                )
        

    if config.iscolab:
        app.queue(concurrency_count=511, max_size=1022).launch(share=True)
    else:
        app.queue(concurrency_count=511, max_size=1022).launch(
            server_name="0.0.0.0",
            inbrowser=not config.noautoopen,
            server_port=config.listen_port,
            quiet=True,
        )