Update app.py

app.py

@@ -5,6 +5,29 @@ 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
 
@@ -42,101 +65,545 @@ download_model()
 sad_talker = SadTalker(lazy_load=True)
 
 
-with gr.Blocks(analytics_enabled=False) as sadtalker_interface:
-    gr.Markdown("<div align='center'> <h2> 😭 SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation (CVPR 2023) </span> </h2> \
-                <a style='font-size:18px;color: #efefef' href='https://arxiv.org/abs/2211.12194'>Arxiv</a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; \
-                <a style='font-size:18px;color: #efefef' href='https://sadtalker.github.io'>Homepage</a>  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; \
-                  <a style='font-size:18px;color: #efefef' href='https://github.com/Winfredy/SadTalker'> Github </div>")
-
-
-    gr.Markdown("""
-    <b>You may duplicate the space and upgrade to GPU in settings for better performance and faster inference without waiting in the queue. <a style='display:inline-block' href="https://huggingface.co/spaces/vinthony/SadTalker?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a></b> \
-    <br/><b>Alternatively, try our GitHub <a href=https://github.com/Winfredy/SadTalker> code </a> on your own GPU. </b> <a style='display:inline-block' href="https://github.com/Winfredy/SadTalker"><img src="https://img.shields.io/github/stars/Winfredy/SadTalker?style=social"/></a> \
-    """)
-
-    with gr.Row().style(equal_height=False):
-        with gr.Column(variant='panel'):
-            with gr.Tabs(elem_id="sadtalker_source_image"):
-                with gr.TabItem('Source image'):
-                    with gr.Row():
-                        source_image = gr.Image(label="Source image", source="upload", type="filepath", elem_id="img2img_image").style(width=512)
-
-
-            with gr.Tabs(elem_id="sadtalker_driven_audio"):
-                with gr.TabItem('Driving Methods'):
-
-                    with gr.Row():
-                        driven_audio = gr.Audio(label="Input audio", source="upload", type="filepath")
-                        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('Settings'):
-                    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)
-
+# 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("checkpoints/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")) as demo:
+    gr.HTML("<center>"
+            "<h1>🥳💕🎶 - ChatGLM2 + 声音克隆：和你喜欢的角色畅所欲言吧！</h1>"
+            "</center>")
+    gr.Markdown("## <center>💡 - 第二代ChatGLM大语言模型 + FreeVC变声，为您打造独一无二的沉浸式对话体验，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���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('Source image'):
                         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', 'resize','full', 'extcrop', 'extfull'], value='crop', label='preprocess', info="How to handle input image?", visible=False)
-
+                            source_image = gr.Image(label="Source image", source="upload", type="filepath", elem_id="img2img_image").style(width=512)
+    
+    
+                with gr.Tabs(elem_id="sadtalker_driven_audio"):
+                    with gr.TabItem('Driving Methods'):
+    
                         with gr.Row():
-                            is_still_mode = gr.Checkbox(label="Still Mode (fewer head motion, works with preprocess `full`)", value=True)
-                            facerender = gr.Radio(['facevid2vid','pirender'], value='facevid2vid', label='facerender', info="which face render?", visible=False)
-
+                            driven_audio = gr.Audio(label="Input audio", source="upload", type="filepath")
+                            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():
-                            batch_size = gr.Slider(label="batch size in generation", step=1, maximum=10, value=2)
-                            enhancer = gr.Checkbox(label="GFPGAN as Face enhancer", value=True, visible=False)
-
-                        submit = gr.Button('Generate', elem_id="sadtalker_generate", variant='primary')
-
-            with gr.Tabs(elem_id="sadtalker_genearted"):
-                    gen_video = gr.Video(label="Generated video", 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]
-            )
-
-
-sadtalker_interface.queue().launch(debug=True)
-
+                            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('Settings'):
+                        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', 'resize','full', 'extcrop', 'extfull'], value='crop', label='preprocess', info="How to handle input image?", visible=False)
+    
+                            with gr.Row():
+                                is_still_mode = gr.Checkbox(label="Still Mode (fewer head motion, works with preprocess `full`)", 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 in generation", step=1, maximum=10, value=2)
+                                enhancer = gr.Checkbox(label="GFPGAN as Face enhancer", value=True, visible=False)
+    
+                            submit = gr.Button('Generate', elem_id="sadtalker_generate", variant='primary')
+    
+                with gr.Tabs(elem_id="sadtalker_genearted"):
+                        gen_video = gr.Video(label="Generated video", 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”三个按键即可；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)