File size: 25,097 Bytes
1cb796d
 
 
 
 
 
 
 
 
 
 
142eebc
 
 
 
 
 
1cb796d
 
883d44b
142eebc
 
 
 
 
1cb796d
 
 
 
142eebc
1cb796d
883d44b
 
 
 
 
 
 
 
 
 
 
 
450be50
1cb796d
da398a7
142eebc
 
 
 
1cb796d
 
 
142eebc
 
 
 
1cb796d
 
450be50
39a0e4e
 
 
142eebc
e964d07
142eebc
e964d07
 
 
 
 
 
 
 
142eebc
 
 
 
 
 
 
58f0474
1cb796d
 
 
 
 
883d44b
1cb796d
142eebc
1cb796d
 
 
 
883d44b
1cb796d
 
142eebc
 
 
 
 
 
1cb796d
 
142eebc
450be50
142eebc
1cb796d
 
 
960fd88
1cb796d
 
883d44b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
450be50
883d44b
 
 
142eebc
 
 
 
 
 
514a392
 
 
 
 
 
 
142eebc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1cb796d
 
 
 
 
 
 
b4f6525
 
1cb796d
 
 
 
 
142eebc
 
 
 
 
514a392
142eebc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
514a392
142eebc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
514a392
142eebc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
514a392
142eebc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1cb796d
142eebc
 
 
514a392
142eebc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1cb796d
514a392
 
 
 
 
 
1cb796d
 
883d44b
1cb796d
 
 
 
514a392
 
 
9fc4347
c0cceff
 
1cb796d
774802a
1fce9cf
 
6b034a1
1fce9cf
 
 
142eebc
1fce9cf
142eebc
 
 
 
 
 
 
 
 
 
 
 
 
883d44b
514a392
142eebc
514a392
e6b7a13
514a392
142eebc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
883d44b
 
142eebc
883d44b
142eebc
 
 
 
 
0f64309
 
142eebc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
883d44b
 
 
 
 
 
142eebc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1fce9cf
142eebc
 
 
 
 
da398a7
514a392
142eebc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7e7d175
142eebc
 
 
 
 
 
514a392
 
 
 
 
142eebc
 
 
 
514a392
 
142eebc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
514a392
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
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
import os
import glob
import json
import traceback
import logging
import gradio as gr
import numpy as np
import librosa
import torch
import asyncio
import edge_tts
import yt_dlp
import ffmpeg
import subprocess
import sys
import io
import wave
from datetime import datetime
from fairseq import checkpoint_utils
from lib.infer_pack.models import (
    SynthesizerTrnMs256NSFsid,
    SynthesizerTrnMs256NSFsid_nono,
    SynthesizerTrnMs768NSFsid,
    SynthesizerTrnMs768NSFsid_nono,
)
from vc_infer_pipeline import VC
from config import Config
config = Config()
logging.getLogger("numba").setLevel(logging.WARNING)
limitation = os.getenv("SYSTEM") == "spaces"

audio_mode = []
f0method_mode = []
f0method_info = ""
if limitation is True:
    audio_mode = ["Upload audio", "TTS Audio"]
    f0method_mode = ["pm", "harvest"]
    f0method_info = "PM is fast, Harvest is good but extremely slow. (Default: PM)"
else:
    audio_mode = ["Input path", "Upload audio", "Youtube", "TTS Audio"]
    f0method_mode = ["pm", "harvest", "crepe"]
    f0method_info = "PM is fast, Harvest is good but extremely slow, and Crepe effect is good but requires GPU (Default: PM)"

def create_vc_fn(model_name, tgt_sr, net_g, vc, if_f0, version, file_index):
    def vc_fn(
        vc_audio_mode,
        vc_input, 
        vc_upload,
        tts_text,
        tts_voice,
        f0_up_key,
        f0_method,
        index_rate,
        filter_radius,
        resample_sr,
        rms_mix_rate,
        protect,
    ):
        try:
            print(f"Converting using {model_name}...")
            if vc_audio_mode == "Input path" or "Youtube" and vc_input != "":
                audio, sr = librosa.load(vc_input, sr=16000, mono=True)
            elif vc_audio_mode == "Upload audio":
                if vc_upload is None:
                    return "You need to upload an audio", None
                sampling_rate, audio = vc_upload
                duration = audio.shape[0] / sampling_rate
                if duration > 20 and limitation:
                    return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
                audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
                if len(audio.shape) > 1:
                    audio = librosa.to_mono(audio.transpose(1, 0))
                if sampling_rate != 16000:
                    audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
            elif vc_audio_mode == "TTS Audio":
                if len(tts_text) > 100 and limitation:
                    return "Text is too long", None
                if tts_text is None or tts_voice is None:
                    return "You need to enter text and select a voice", None
                asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
                audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
                vc_input = "tts.mp3"
            times = [0, 0, 0]
            f0_up_key = int(f0_up_key)
            audio_opt = vc.pipeline(
                hubert_model,
                net_g,
                0,
                audio,
                vc_input,
                times,
                f0_up_key,
                f0_method,
                file_index,
                # file_big_npy,
                index_rate,
                if_f0,
                filter_radius,
                tgt_sr,
                resample_sr,
                rms_mix_rate,
                version,
                protect,
                f0_file=None,
            )
            info = f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
            print(f"{model_name} | {info}")
            return info, (tgt_sr, audio_opt)
        except:
            info = traceback.format_exc()
            print(info)
            return info, None
    return vc_fn

def load_model():
    categories = []
    with open("weights/folder_info.json", "r", encoding="utf-8") as f:
        folder_info = json.load(f)
    for category_name, category_info in folder_info.items():
        if not category_info['enable']:
            continue
        category_title = category_info['title']
        category_folder = category_info['folder_path']
        description = category_info['description']
        models = []
        with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
            models_info = json.load(f)
        for character_name, info in models_info.items():
            if not info['enable']:
                continue
            model_title = info['title']
            model_name = info['model_path']
            model_author = info.get("author", None)
            model_cover = f"weights/{category_folder}/{character_name}/{info['cover']}"
            model_index = f"weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
            cpt = torch.load(f"weights/{category_folder}/{character_name}/{model_name}", map_location="cpu")
            tgt_sr = cpt["config"][-1]
            cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
            if_f0 = cpt.get("f0", 1)
            version = cpt.get("version", "v1")
            if version == "v1":
                if if_f0 == 1:
                    net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
                else:
                    net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
                model_version = "V1"
            elif version == "v2":
                if if_f0 == 1:
                    net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
                else:
                    net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
                model_version = "V2"
            del net_g.enc_q
            print(net_g.load_state_dict(cpt["weight"], strict=False))
            net_g.eval().to(config.device)
            if config.is_half:
                net_g = net_g.half()
            else:
                net_g = net_g.float()
            vc = VC(tgt_sr, config)
            print(f"Model loaded: {character_name} / {info['feature_retrieval_library']} | ({model_version})")
            models.append((character_name, model_title, model_author, model_cover, model_version, create_vc_fn(model_name, tgt_sr, net_g, vc, if_f0, version, model_index)))
        categories.append([category_title, category_folder, description, models])
    return categories

def cut_vocal_and_inst(url, audio_provider, split_model):
    if url != "":
        if not os.path.exists("dl_audio"):
            os.mkdir("dl_audio")
        if audio_provider == "Youtube":
            ydl_opts = {
                'noplaylist': True,
                'format': 'bestaudio/best',
                'postprocessors': [{
                    'key': 'FFmpegExtractAudio',
                    'preferredcodec': 'wav',
                }],
                "outtmpl": 'dl_audio/youtube_audio',
            }
            with yt_dlp.YoutubeDL(ydl_opts) as ydl:
                ydl.download([url])
            audio_path = "dl_audio/youtube_audio.wav"
        if split_model == "htdemucs":
            command = f"demucs --two-stems=vocals {audio_path} -o output"
            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
            print(result.stdout.decode())
            return "output/htdemucs/youtube_audio/vocals.wav", "output/htdemucs/youtube_audio/no_vocals.wav", audio_path, "output/htdemucs/youtube_audio/vocals.wav"
        else:
            command = f"demucs --two-stems=vocals -n mdx_extra_q {audio_path} -o output"
            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
            print(result.stdout.decode())
            return "output/mdx_extra_q/youtube_audio/vocals.wav", "output/mdx_extra_q/youtube_audio/no_vocals.wav", audio_path, "output/mdx_extra_q/youtube_audio/vocals.wav"
    else:
        raise gr.Error("URL Required!")
        return None, None, None, None

def combine_vocal_and_inst(audio_data, audio_volume, split_model):
    if not os.path.exists("output/result"):
        os.mkdir("output/result")
    vocal_path = "output/result/output.wav"
    output_path = "output/result/combine.mp3"
    if split_model == "htdemucs":
        inst_path = "output/htdemucs/youtube_audio/no_vocals.wav"
    else:
        inst_path = "output/mdx_extra_q/youtube_audio/no_vocals.wav"
    with wave.open(vocal_path, "w") as wave_file:
        wave_file.setnchannels(1) 
        wave_file.setsampwidth(2)
        wave_file.setframerate(audio_data[0])
        wave_file.writeframes(audio_data[1].tobytes())
    command =  f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [1:a]volume={audio_volume}dB[v];[0:a][v]amix=inputs=2:duration=longest -b:a 320k -c:a libmp3lame {output_path}'
    result = subprocess.run(command.split(), stdout=subprocess.PIPE)
    print(result.stdout.decode())
    return output_path

def load_hubert():
    global hubert_model
    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
        ["hubert_base.pt"],
        suffix="",
    )
    hubert_model = models[0]
    hubert_model = hubert_model.to(config.device)
    if config.is_half:
        hubert_model = hubert_model.half()
    else:
        hubert_model = hubert_model.float()
    hubert_model.eval()

def change_audio_mode(vc_audio_mode):
    if vc_audio_mode == "Input path":
        return (
            # Input & Upload
            gr.Textbox.update(visible=True),
            gr.Checkbox.update(visible=False),
            gr.Audio.update(visible=False),
            # Youtube
            gr.Dropdown.update(visible=False),
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False),
            gr.Button.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Slider.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Button.update(visible=False),
            # TTS
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False)
        )
    elif vc_audio_mode == "Upload audio":
        return (
            # Input & Upload
            gr.Textbox.update(visible=False),
            gr.Checkbox.update(visible=True),
            gr.Audio.update(visible=True),
            # Youtube
            gr.Dropdown.update(visible=False),
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False),
            gr.Button.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Slider.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Button.update(visible=False),
            # TTS
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False)
        )
    elif vc_audio_mode == "Youtube":
        return (
            # Input & Upload
            gr.Textbox.update(visible=False),
            gr.Checkbox.update(visible=False),
            gr.Audio.update(visible=False),
            # Youtube
            gr.Dropdown.update(visible=True),
            gr.Textbox.update(visible=True),
            gr.Dropdown.update(visible=True),
            gr.Button.update(visible=True),
            gr.Audio.update(visible=True),
            gr.Audio.update(visible=True),
            gr.Audio.update(visible=True),
            gr.Slider.update(visible=True),
            gr.Audio.update(visible=True),
            gr.Button.update(visible=True),
            # TTS
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False)
        )
    elif vc_audio_mode == "TTS Audio":
        return (
            # Input & Upload
            gr.Textbox.update(visible=False),
            gr.Checkbox.update(visible=False),
            gr.Audio.update(visible=False),
            # Youtube
            gr.Dropdown.update(visible=False),
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False),
            gr.Button.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Slider.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Button.update(visible=False),
            # TTS
            gr.Textbox.update(visible=True),
            gr.Dropdown.update(visible=True)
        )
    else:
        return (
            # Input & Upload
            gr.Textbox.update(visible=False),
            gr.Checkbox.update(visible=True),
            gr.Audio.update(visible=True),
            # Youtube
            gr.Dropdown.update(visible=False),
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False),
            gr.Button.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Slider.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Button.update(visible=False),
            # TTS
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False)
        )

def use_microphone(microphone):
    if microphone == True:
        return gr.Audio.update(source="microphone")
    else:
        return gr.Audio.update(source="upload")

if __name__ == '__main__':
    load_hubert()
    categories = load_model()
    tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
    voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
    with gr.Blocks() as app:
        gr.Markdown(
            "<div align='center'>\n\n"+
            "# RVC Genshin Impact\n\n"+
            "### Recommended to use Google Colab to use other character and feature.\n\n"+
            "[![Colab](https://img.shields.io/badge/Colab-RVC%20Genshin%20Impact-blue?style=for-the-badge&logo=googlecolab)](https://colab.research.google.com/drive/110kiMZTdP6Ri1lY9-NbQf17GVPPhHyeT?usp=sharing)\n\n"+
            "</div>\n\n"+
            "[![Repository](https://img.shields.io/badge/Github-Multi%20Model%20RVC%20Inference-blue?style=for-the-badge&logo=github)](https://github.com/ArkanDash/Multi-Model-RVC-Inference)"
        )
        for (folder_title, folder, description, models) in categories:
            with gr.TabItem(folder_title):
                if description:
                    gr.Markdown(f"### <center> {description}")
                with gr.Tabs():
                    if not models:
                        gr.Markdown("# <center> No Model Loaded.")
                        gr.Markdown("## <center> Please add model or fix your model path.")
                        continue
                    for (name, title, author, cover, model_version, vc_fn) in models:
                        with gr.TabItem(name):
                            with gr.Row():
                                gr.Markdown(
                                    '<div align="center">'
                                    f'<div>{title}</div>\n'+
                                    f'<div>RVC {model_version} Model</div>\n'+
                                    (f'<div>Model author: {author}</div>' if author else "")+
                                    (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
                                    '</div>'
                                )
                            with gr.Row():
                                with gr.Column():
                                    vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Upload audio")
                                    # Input
                                    vc_input = gr.Textbox(label="Input audio path", visible=False)
                                    # Upload
                                    vc_microphone_mode = gr.Checkbox(label="Use Microphone", value=False, visible=True, interactive=True)
                                    vc_upload = gr.Audio(label="Upload audio file", source="upload", visible=True, interactive=True)
                                    # Youtube
                                    vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
                                    vc_link = gr.Textbox(label="Youtube URL", visible=False, info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
                                    vc_split_model = gr.Dropdown(label="Splitter Model", choices=["htdemucs", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
                                    vc_split = gr.Button("Split Audio", variant="primary", visible=False)
                                    vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False)
                                    vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False)
                                    vc_audio_preview = gr.Audio(label="Audio Preview", visible=False)
                                    # TTS
                                    tts_text = gr.Textbox(visible=False, label="TTS text", info="Text to speech input")
                                    tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
                                with gr.Column():
                                    vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
                                    f0method0 = gr.Radio(
                                        label="Pitch extraction algorithm",
                                        info=f0method_info,
                                        choices=f0method_mode,
                                        value="pm",
                                        interactive=True
                                    )
                                    index_rate1 = gr.Slider(
                                        minimum=0,
                                        maximum=1,
                                        label="Retrieval feature ratio",
                                        info="(Default: 0.7)",
                                        value=0.7,
                                        interactive=True,
                                    )
                                    filter_radius0 = gr.Slider(
                                        minimum=0,
                                        maximum=7,
                                        label="Apply Median Filtering",
                                        info="The value represents the filter radius and can reduce breathiness.",
                                        value=3,
                                        step=1,
                                        interactive=True,
                                    )
                                    resample_sr0 = gr.Slider(
                                        minimum=0,
                                        maximum=48000,
                                        label="Resample the output audio",
                                        info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling",
                                        value=0,
                                        step=1,
                                        interactive=True,
                                    )
                                    rms_mix_rate0 = gr.Slider(
                                        minimum=0,
                                        maximum=1,
                                        label="Volume Envelope",
                                        info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
                                        value=1,
                                        interactive=True,
                                    )
                                    protect0 = gr.Slider(
                                        minimum=0,
                                        maximum=0.5,
                                        label="Voice Protection",
                                        info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
                                        value=0.5,
                                        step=0.01,
                                        interactive=True,
                                    )
                                with gr.Column():
                                    vc_log = gr.Textbox(label="Output Information", interactive=False)
                                    vc_output = gr.Audio(label="Output Audio", interactive=False)
                                    vc_convert = gr.Button("Convert", variant="primary")
                                    vc_volume = gr.Slider(
                                        minimum=0,
                                        maximum=10,
                                        label="Vocal volume",
                                        value=4,
                                        interactive=True,
                                        step=1,
                                        info="Adjust vocal volume (Default: 4}",
                                        visible=False
                                    )
                                    vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False)
                                    vc_combine =  gr.Button("Combine",variant="primary", visible=False)
                        vc_convert.click(
                            fn=vc_fn, 
                            inputs=[
                                vc_audio_mode,
                                vc_input,
                                vc_upload,
                                tts_text,
                                tts_voice,
                                vc_transform0,
                                f0method0,
                                index_rate1,
                                filter_radius0,
                                resample_sr0,
                                rms_mix_rate0,
                                protect0,
                            ], 
                            outputs=[vc_log ,vc_output]
                        )
                        vc_split.click(
                            fn=cut_vocal_and_inst, 
                            inputs=[vc_link, vc_download_audio, vc_split_model], 
                            outputs=[vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input]
                        )
                        vc_combine.click(
                            fn=combine_vocal_and_inst,
                            inputs=[vc_output, vc_volume, vc_split_model],
                            outputs=[vc_combined_output]
                        )
                        vc_microphone_mode.change(
                            fn=use_microphone,
                            inputs=vc_microphone_mode,
                            outputs=vc_upload
                        )
                        vc_audio_mode.change(
                            fn=change_audio_mode,
                            inputs=[vc_audio_mode],
                            outputs=[
                                vc_input,
                                vc_microphone_mode,
                                vc_upload,
                                vc_download_audio,
                                vc_link,
                                vc_split_model,
                                vc_split,
                                vc_vocal_preview,
                                vc_inst_preview,
                                vc_audio_preview,
                                vc_volume,
                                vc_combined_output,
                                vc_combine,
                                tts_text,
                                tts_voice
                            ]
                        )
        app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab)