File size: 24,178 Bytes
9074b85
 
 
 
 
 
 
68fbfa8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9074b85
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
68fbfa8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0be75d5
68fbfa8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ab6656a
68fbfa8
9cc183a
68fbfa8
9cc183a
68fbfa8
000290c
68fbfa8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7544d0c
9074b85
0691758
68fbfa8
 
 
99b8193
68fbfa8
9074b85
0691758
68fbfa8
 
 
 
 
 
 
9074b85
68fbfa8
 
 
 
 
 
 
 
 
 
 
7544d0c
68fbfa8
 
 
 
 
 
 
 
 
 
99b8193
68fbfa8
 
0691758
68fbfa8
 
 
0691758
68fbfa8
 
0691758
68fbfa8
 
0691758
68fbfa8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e7b3887
68fbfa8
 
 
 
 
 
 
 
4c0f4f7
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
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
import os, sys
import tempfile
import gradio as gr
from src.gradio_demo import SadTalker  
# from src.utils.text2speech import TTSTalker
from huggingface_hub import snapshot_download

import torch
import librosa
from scipy.io.wavfile import write
from transformers import WavLMModel

import utils
from models import SynthesizerTrn
from mel_processing import mel_spectrogram_torch
from speaker_encoder.voice_encoder import SpeakerEncoder

import time
from textwrap import dedent

import mdtex2html
from loguru import logger
from transformers import AutoModel, AutoTokenizer

from tts_voice import tts_order_voice
import edge_tts
import tempfile
import anyio


def get_source_image(image):   
        return image

try:
    import webui  # in webui
    in_webui = True
except:
    in_webui = False


def toggle_audio_file(choice):
    if choice == False:
        return gr.update(visible=True), gr.update(visible=False)
    else:
        return gr.update(visible=False), gr.update(visible=True)
    
def ref_video_fn(path_of_ref_video):
    if path_of_ref_video is not None:
        return gr.update(value=True)
    else:
        return gr.update(value=False)
    
def download_model():
    REPO_ID = 'vinthony/SadTalker-V002rc'
    snapshot_download(repo_id=REPO_ID, local_dir='./checkpoints', local_dir_use_symlinks=True)

def sadtalker_demo():

    download_model()

    sad_talker = SadTalker(lazy_load=True)
    # tts_talker = TTSTalker()

download_model()
sad_talker = SadTalker(lazy_load=True)


# ChatGLM2 & FreeVC

'''
def get_wavlm():
    os.system('gdown https://drive.google.com/uc?id=12-cB34qCTvByWT-QtOcZaqwwO21FLSqU')
    shutil.move('WavLM-Large.pt', 'wavlm')
'''

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

smodel = SpeakerEncoder('speaker_encoder/ckpt/pretrained_bak_5805000.pt')

print("Loading FreeVC(24k)...")
hps = utils.get_hparams_from_file("configs/freevc-24.json")
freevc_24 = SynthesizerTrn(
    hps.data.filter_length // 2 + 1,
    hps.train.segment_size // hps.data.hop_length,
    **hps.model).to(device)
_ = freevc_24.eval()
_ = utils.load_checkpoint("checkpoint/freevc-24.pth", freevc_24, None)

print("Loading WavLM for content...")
cmodel = WavLMModel.from_pretrained("microsoft/wavlm-large").to(device)
 
def convert(model, src, tgt):
    with torch.no_grad():
        # tgt
        wav_tgt, _ = librosa.load(tgt, sr=hps.data.sampling_rate)
        wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20)
        if model == "FreeVC" or model == "FreeVC (24kHz)":
            g_tgt = smodel.embed_utterance(wav_tgt)
            g_tgt = torch.from_numpy(g_tgt).unsqueeze(0).to(device)
        else:
            wav_tgt = torch.from_numpy(wav_tgt).unsqueeze(0).to(device)
            mel_tgt = mel_spectrogram_torch(
                wav_tgt, 
                hps.data.filter_length,
                hps.data.n_mel_channels,
                hps.data.sampling_rate,
                hps.data.hop_length,
                hps.data.win_length,
                hps.data.mel_fmin,
                hps.data.mel_fmax
            )
        # src
        wav_src, _ = librosa.load(src, sr=hps.data.sampling_rate)
        wav_src = torch.from_numpy(wav_src).unsqueeze(0).to(device)
        c = cmodel(wav_src).last_hidden_state.transpose(1, 2).to(device)
        # infer
        if model == "FreeVC":
            audio = freevc.infer(c, g=g_tgt)
        elif model == "FreeVC-s":
            audio = freevc_s.infer(c, mel=mel_tgt)
        else:
            audio = freevc_24.infer(c, g=g_tgt)
        audio = audio[0][0].data.cpu().float().numpy()
        if model == "FreeVC" or model == "FreeVC-s":
            write("out.wav", hps.data.sampling_rate, audio)
        else:
            write("out.wav", 24000, audio)
    out = "out.wav"
    return out

# GLM2

language_dict = tts_order_voice

# fix timezone in Linux
os.environ["TZ"] = "Asia/Shanghai"
try:
    time.tzset()  # type: ignore # pylint: disable=no-member
except Exception:
    # Windows
    logger.warning("Windows, cant run time.tzset()")

# model_name = "THUDM/chatglm2-6b"
model_name = "THUDM/chatglm2-6b-int4"

RETRY_FLAG = False

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda()

# 4/8 bit
# model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).quantize(4).cuda()

has_cuda = torch.cuda.is_available()

# has_cuda = False  # force cpu

if has_cuda:
    model_glm = (
        AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda().half()
    )  # 3.92G
else:
    model_glm = AutoModel.from_pretrained(
        model_name, trust_remote_code=True
    ).float()  # .float() .half().float()

model_glm = model_glm.eval()

_ = """Override Chatbot.postprocess"""


def postprocess(self, y):
    if y is None:
        return []
    for i, (message, response) in enumerate(y):
        y[i] = (
            None if message is None else mdtex2html.convert((message)),
            None if response is None else mdtex2html.convert(response),
        )
    return y


gr.Chatbot.postprocess = postprocess


def parse_text(text):
    """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
    lines = text.split("\n")
    lines = [line for line in lines if line != ""]
    count = 0
    for i, line in enumerate(lines):
        if "```" in line:
            count += 1
            items = line.split("`")
            if count % 2 == 1:
                lines[i] = f'<pre><code class="language-{items[-1]}">'
            else:
                lines[i] = "<br></code></pre>"
        else:
            if i > 0:
                if count % 2 == 1:
                    line = line.replace("`", r"\`")
                    line = line.replace("<", "&lt;")
                    line = line.replace(">", "&gt;")
                    line = line.replace(" ", "&nbsp;")
                    line = line.replace("*", "&ast;")
                    line = line.replace("_", "&lowbar;")
                    line = line.replace("-", "&#45;")
                    line = line.replace(".", "&#46;")
                    line = line.replace("!", "&#33;")
                    line = line.replace("(", "&#40;")
                    line = line.replace(")", "&#41;")
                    line = line.replace("$", "&#36;")
                lines[i] = "<br>" + line
    text = "".join(lines)
    return text


def predict(
    RETRY_FLAG, input, chatbot, max_length, top_p, temperature, history, past_key_values
):
    try:
        chatbot.append((parse_text(input), ""))
    except Exception as exc:
        logger.error(exc)
        logger.debug(f"{chatbot=}")
        _ = """
        if chatbot:
            chatbot[-1] = (parse_text(input), str(exc))
            yield chatbot, history, past_key_values
        # """
        yield chatbot, history, past_key_values

    for response, history, past_key_values in model_glm.stream_chat(
        tokenizer,
        input,
        history,
        past_key_values=past_key_values,
        return_past_key_values=True,
        max_length=max_length,
        top_p=top_p,
        temperature=temperature,
    ):
        chatbot[-1] = (parse_text(input), parse_text(response))
        # chatbot[-1][-1] = parse_text(response)

        yield chatbot, history, past_key_values, parse_text(response)


def trans_api(input, max_length=4096, top_p=0.8, temperature=0.2):
    if max_length < 10:
        max_length = 4096
    if top_p < 0.1 or top_p > 1:
        top_p = 0.85
    if temperature <= 0 or temperature > 1:
        temperature = 0.01
    try:
        res, _ = model_glm.chat(
            tokenizer,
            input,
            history=[],
            past_key_values=None,
            max_length=max_length,
            top_p=top_p,
            temperature=temperature,
        )
        # logger.debug(f"{res=} \n{_=}")
    except Exception as exc:
        logger.error(f"{exc=}")
        res = str(exc)

    return res


def reset_user_input():
    return gr.update(value="")


def reset_state():
    return [], [], None, ""


# Delete last turn
def delete_last_turn(chat, history):
    if chat and history:
        chat.pop(-1)
        history.pop(-1)
    return chat, history


# Regenerate response
def retry_last_answer(
    user_input, chatbot, max_length, top_p, temperature, history, past_key_values
):
    if chatbot and history:
        # Removing the previous conversation from chat
        chatbot.pop(-1)
        # Setting up a flag to capture a retry
        RETRY_FLAG = True
        # Getting last message from user
        user_input = history[-1][0]
        # Removing bot response from the history
        history.pop(-1)

    yield from predict(
        RETRY_FLAG,  # type: ignore
        user_input,
        chatbot,
        max_length,
        top_p,
        temperature,
        history,
        past_key_values,
    )

# print

def print(text):
    return text

# TTS

async def text_to_speech_edge(text, language_code):
    voice = language_dict[language_code]
    communicate = edge_tts.Communicate(text, voice)
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
        tmp_path = tmp_file.name

    await communicate.save(tmp_path)

    return tmp_path


with gr.Blocks(title="ChatGLM2-6B-int4", theme=gr.themes.Soft(text_size="sm"), analytics_enabled=False) as demo:
    gr.HTML("<center>"
            "<h1>📺💕🎶 - ChatGLM2+声音克隆+视频对话:和喜欢的角色畅所欲言吧!</h1>"
            "</center>")
    gr.Markdown("## <center>🥳 - ChatGLM2+FreeVC+SadTalker,为您打造沉浸式的视频对话体验,支持中英双语</center>")
    gr.Markdown("## <center>🌊 - 更多精彩应用,尽在[滔滔AI](http://www.talktalkai.com);滔滔AI,为爱滔滔!💕</center>")
    gr.Markdown("### <center>⭐ - 如果您喜欢这个程序,欢迎给我的[GitHub项目](https://github.com/KevinWang676/ChatGLM2-Voice-Cloning)点赞支持!</center>")

    with gr.Tab("🍻 - ChatGLM2聊天区"):
        with gr.Accordion("📒 相关信息", open=False):
            _ = f""" ChatGLM2的可选参数信息:
                * Low temperature: responses will be more deterministic and focused; High temperature: responses more creative.
                * Suggested temperatures -- translation: up to 0.3; chatting: > 0.4
                * Top P controls dynamic vocabulary selection based on context.\n
                如果您想让ChatGLM2进行角色扮演并与之对话,请先输入恰当的提示词,如“请你扮演成动漫角色蜡笔小新并和我进行对话”;您也可以为ChatGLM2提供自定义的角色设定\n
                当您使用声音克隆功能时,请先在此程序的对应位置上传一段您喜欢的音频
                """
            gr.Markdown(dedent(_))
        chatbot = gr.Chatbot(height=300)
        with gr.Row():
            with gr.Column(scale=4):
                with gr.Column(scale=12):
                    user_input = gr.Textbox(
                        label="请在此处和GLM2聊天 (按回车键即可发送)",
                        placeholder="聊点什么吧",
                    )
                    RETRY_FLAG = gr.Checkbox(value=False, visible=False)
        with gr.Column(min_width=32, scale=1):
            with gr.Row():
                submitBtn = gr.Button("开始和GLM2交流吧", variant="primary")
                deleteBtn = gr.Button("删除最新一轮对话", variant="secondary")
                retryBtn = gr.Button("重新生成最新一轮对话", variant="secondary")
                    
        with gr.Accordion("🔧 更多设置", open=False):
            with gr.Row():
                emptyBtn = gr.Button("清空所有聊天记录")
                max_length = gr.Slider(
                    0,
                    32768,
                    value=8192,
                    step=1.0,
                    label="Maximum length",
                    interactive=True,
                )
                top_p = gr.Slider(
                    0, 1, value=0.85, step=0.01, label="Top P", interactive=True
                )
                temperature = gr.Slider(
                    0.01, 1, value=0.95, step=0.01, label="Temperature", interactive=True
                )
    
    
        with gr.Row():
            test1 = gr.Textbox(label="GLM2的最新回答 (可编辑)", lines = 3)
            with gr.Column():
                language = gr.Dropdown(choices=list(language_dict.keys()), value="普通话 (中国大陆)-Xiaoxiao-女", label="请选择文本对应的语言及您喜欢的说话人")
                tts_btn = gr.Button("生成对应的音频吧", variant="primary")
            output_audio = gr.Audio(type="filepath", label="为您生成的音频", interactive=False)
    
        tts_btn.click(text_to_speech_edge, inputs=[test1, language], outputs=[output_audio])
    
        with gr.Row():
            model_choice = gr.Dropdown(choices=["FreeVC", "FreeVC-s", "FreeVC (24kHz)"], value="FreeVC (24kHz)", label="Model", visible=False) 
            audio1 = output_audio
            audio2 = gr.Audio(label="请上传您喜欢的声音进行声音克隆", type='filepath')
            clone_btn = gr.Button("开始AI声音克隆吧", variant="primary")
            audio_cloned =  gr.Audio(label="为您生成的专属声音克隆音频", type='filepath')
    
        clone_btn.click(convert, inputs=[model_choice, audio1, audio2], outputs=[audio_cloned])
            
        history = gr.State([])
        past_key_values = gr.State(None)
    
        user_input.submit(
            predict,
            [
                RETRY_FLAG,
                user_input,
                chatbot,
                max_length,
                top_p,
                temperature,
                history,
                past_key_values,
            ],
            [chatbot, history, past_key_values, test1],
            show_progress="full",
        )
        submitBtn.click(
            predict,
            [
                RETRY_FLAG,
                user_input,
                chatbot,
                max_length,
                top_p,
                temperature,
                history,
                past_key_values,
            ],
            [chatbot, history, past_key_values, test1],
            show_progress="full",
            api_name="predict",
        )
        submitBtn.click(reset_user_input, [], [user_input])
    
        emptyBtn.click(
            reset_state, outputs=[chatbot, history, past_key_values, test1], show_progress="full"
        )
    
        retryBtn.click(
            retry_last_answer,
            inputs=[
                user_input,
                chatbot,
                max_length,
                top_p,
                temperature,
                history,
                past_key_values,
            ],
            # outputs = [chatbot, history, last_user_message, user_message]
            outputs=[chatbot, history, past_key_values, test1],
        )
        deleteBtn.click(delete_last_turn, [chatbot, history], [chatbot, history])
    
        with gr.Accordion("📔 提示词示例", open=False):
            etext = """In America, where cars are an important part of the national psyche, a decade ago people had suddenly started to drive less, which had not happened since the oil shocks of the 1970s. """
            examples = gr.Examples(
                examples=[
                    ["Explain the plot of Cinderella in a sentence."],
                    [
                        "How long does it take to become proficient in French, and what are the best methods for retaining information?"
                    ],
                    ["What are some common mistakes to avoid when writing code?"],
                    ["Build a prompt to generate a beautiful portrait of a horse"],
                    ["Suggest four metaphors to describe the benefits of AI"],
                    ["Write a pop song about leaving home for the sandy beaches."],
                    ["Write a summary demonstrating my ability to tame lions"],
                    ["鲁迅和周树人什么关系"],
                    ["从前有一头牛,这头牛后面有什么?"],
                    ["正无穷大加一大于正无穷大吗?"],
                    ["正无穷大加正无穷大大于正无穷大吗?"],
                    ["-2的平方根等于什么"],
                    ["树上有5只鸟,猎人开枪打死了一只。树上还有几只鸟?"],
                    ["树上有11只鸟,猎人开枪打死了一只。树上还有几只鸟?提示:需考虑鸟可能受惊吓飞走。"],
                    ["鲁迅和周树人什么关系 用英文回答"],
                    ["以红楼梦的行文风格写一张委婉的请假条。不少于320字。"],
                    [f"{etext} 翻成中文,列出3个版本"],
                    [f"{etext} \n 翻成中文,保留原意,但使用文学性的语言。不要写解释。列出3个版本"],
                    ["js 判断一个数是不是质数"],
                    ["js 实现python 的 range(10)"],
                    ["js 实现python 的 [*(range(10)]"],
                    ["假定 1 + 2 = 4, 试求 7 + 8"],
                    ["Erkläre die Handlung von Cinderella in einem Satz."],
                    ["Erkläre die Handlung von Cinderella in einem Satz. Auf Deutsch"],
                ],
                inputs=[user_input],
                examples_per_page=30,
            )
    
        with gr.Accordion("For Chat/Translation API", open=False, visible=False):
            input_text = gr.Text()
            tr_btn = gr.Button("Go", variant="primary")
            out_text = gr.Text()
        tr_btn.click(
            trans_api,
            [input_text, max_length, top_p, temperature],
            out_text,
            # show_progress="full",
            api_name="tr",
        )
        _ = """
        input_text.submit(
            trans_api,
            [input_text, max_length, top_p, temperature],
            out_text,
            show_progress="full",
            api_name="tr1",
        )
        # """
    with gr.Tab("📺 - 视频聊天区"):
        with gr.Row().style(equal_height=False):
            with gr.Column(variant='panel'):
                with gr.Tabs(elem_id="sadtalker_source_image"):
                    with gr.TabItem('图片上传'):
                        with gr.Row():
                            source_image = gr.Image(label="请上传一张您喜欢角色的图片", source="upload", type="filepath", elem_id="img2img_image").style(width=512)
    
    
                with gr.Tabs(elem_id="sadtalker_driven_audio"):
                    with gr.TabItem('💡您还可以将视频下载到本地'):
    
                        with gr.Row():
                            driven_audio = audio_cloned
                            driven_audio_no = gr.Audio(label="Use IDLE mode, no audio is required", source="upload", type="filepath", visible=False)
    
                            with gr.Column():
                                use_idle_mode = gr.Checkbox(label="Use Idle Animation", visible=False)
                                length_of_audio = gr.Number(value=5, label="The length(seconds) of the generated video.", visible=False)
                                use_idle_mode.change(toggle_audio_file, inputs=use_idle_mode, outputs=[driven_audio, driven_audio_no]) # todo
    
                        with gr.Row():
                            ref_video = gr.Video(label="Reference Video", source="upload", type="filepath", elem_id="vidref", visible=False).style(width=512)
    
                            with gr.Column():
                                use_ref_video = gr.Checkbox(label="Use Reference Video", visible=False)
                                ref_info = gr.Radio(['pose', 'blink','pose+blink', 'all'], value='pose', label='Reference Video',info="How to borrow from reference Video?((fully transfer, aka, video driving mode))", visible=False)
    
                            ref_video.change(ref_video_fn, inputs=ref_video, outputs=[use_ref_video]) # todo
    
    
            with gr.Column(variant='panel'):
                with gr.Tabs(elem_id="sadtalker_checkbox"):
                    with gr.TabItem('视频设置'):
                        with gr.Column(variant='panel'):
                            # width = gr.Slider(minimum=64, elem_id="img2img_width", maximum=2048, step=8, label="Manually Crop Width", value=512) # img2img_width
                            # height = gr.Slider(minimum=64, elem_id="img2img_height", maximum=2048, step=8, label="Manually Crop Height", value=512) # img2img_width
                            with gr.Row():
                                pose_style = gr.Slider(minimum=0, maximum=45, step=1, label="Pose style", value=0, visible=False) #
                                exp_weight = gr.Slider(minimum=0, maximum=3, step=0.1, label="expression scale", value=1, visible=False) #
                                blink_every = gr.Checkbox(label="use eye blink", value=True, visible=False)
    
                            with gr.Row():
                                size_of_image = gr.Radio([256, 512], value=256, label='face model resolution', info="use 256/512 model?", visible=False) #
                                preprocess_type = gr.Radio(['crop', 'full'], value='crop', label='是否聚焦角色面部', info="crop:视频会聚焦角色面部;full:视频会显示图片全貌")
    
                            with gr.Row():
                                is_still_mode = gr.Checkbox(label="静态模式 (开启静态模式,角色的面部动作会减少;默认开启)", value=True)
                                facerender = gr.Radio(['facevid2vid','pirender'], value='facevid2vid', label='facerender', info="which face render?", visible=False)
    
                            with gr.Row():
                                batch_size = gr.Slider(label="Batch size (数值越大,生成速度越快;若显卡性能好,可增大数值)", step=1, maximum=32, value=2)
                                enhancer = gr.Checkbox(label="GFPGAN as Face enhancer", value=True, visible=False)
    
                            submit = gr.Button('开始视频聊天吧', elem_id="sadtalker_generate", variant='primary')
    
                with gr.Tabs(elem_id="sadtalker_genearted"):
                        gen_video = gr.Video(label="为您生成的专属视频", format="mp4").style(width=256)
    
    
    
        submit.click(
                fn=sad_talker.test,
                inputs=[source_image,
                        driven_audio,
                        preprocess_type,
                        is_still_mode,
                        enhancer,
                        batch_size,
                        size_of_image,
                        pose_style,
                        facerender,
                        exp_weight,
                        use_ref_video,
                        ref_video,
                        ref_info,
                        use_idle_mode,
                        length_of_audio,
                        blink_every
                        ],
                outputs=[gen_video]
                )    
    gr.Markdown("### <center>注意❗:请不要生成会对个人以及组织造成侵害的内容,此程序仅供科研、学习及个人娱乐使用。</center>")
    gr.Markdown("<center>💡- 如何使用此程序:输入您对ChatGLM的提问后,依次点击“开始和GLM2交流吧”、“生成对应的音频吧”、“开始AI声音克隆吧”、“开始视频聊天吧”四个按键即可;使用声音克隆功能时,请先上传一段您喜欢的音频</center>")
    gr.HTML('''
        <div class="footer">
                    <p>🌊🏞️🎶 - 江水东流急,滔滔无尽声。 明·顾璘
                    </p>
        </div>
    ''')


demo.queue().launch(show_error=True, debug=True)