Delete .ipynb_checkpoints

.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 5
-}

.ipynb_checkpoints/app-checkpoint.py

@@ -1,608 +0,0 @@
-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”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)

.ipynb_checkpoints/app_sadtalker-checkpoint.py

@@ -1,111 +0,0 @@
-import os, sys
-import gradio as gr
-from src.gradio_demo import SadTalker  
-
-
-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 sadtalker_demo(checkpoint_path='checkpoints', config_path='src/config', warpfn=None):
-
-    sad_talker = SadTalker(checkpoint_path, config_path, 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>")
-        
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                with gr.Tabs(elem_id="sadtalker_source_image"):
-                    with gr.TabItem('Upload 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('Upload OR TTS'):
-                        with gr.Column(variant='panel'):
-                            driven_audio = gr.Audio(label="Input audio", source="upload", type="filepath")
-
-                        if sys.platform != 'win32' and not in_webui: 
-                            from src.utils.text2speech import TTSTalker
-                            tts_talker = TTSTalker()
-                            with gr.Column(variant='panel'):
-                                input_text = gr.Textbox(label="Generating audio from text", lines=5, placeholder="please enter some text here, we genreate the audio from text using @Coqui.ai TTS.")
-                                tts = gr.Button('Generate audio',elem_id="sadtalker_audio_generate", variant='primary')
-                                tts.click(fn=tts_talker.test, inputs=[input_text], outputs=[driven_audio])
-                            
-            with gr.Column(variant='panel'): 
-                with gr.Tabs(elem_id="sadtalker_checkbox"):
-                    with gr.TabItem('Settings'):
-                        gr.Markdown("need help? please visit our [best practice page](https://github.com/OpenTalker/SadTalker/blob/main/docs/best_practice.md) for more detials")
-                        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
-                            pose_style = gr.Slider(minimum=0, maximum=46, step=1, label="Pose style", value=0) # 
-                            size_of_image = gr.Radio([256, 512], value=256, label='face model resolution', info="use 256/512 model?") # 
-                            preprocess_type = gr.Radio(['crop', 'resize','full', 'extcrop', 'extfull'], value='crop', label='preprocess', info="How to handle input image?")
-                            is_still_mode = gr.Checkbox(label="Still Mode (fewer hand motion, works with preprocess `full`)")
-                            batch_size = gr.Slider(label="batch size in generation", step=1, maximum=10, value=2)
-                            enhancer = gr.Checkbox(label="GFPGAN as Face enhancer")
-                            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)
-
-        if warpfn:
-            submit.click(
-                        fn=warpfn(sad_talker.test), 
-                        inputs=[source_image,
-                                driven_audio,
-                                preprocess_type,
-                                is_still_mode,
-                                enhancer,
-                                batch_size,                            
-                                size_of_image,
-                                pose_style
-                                ], 
-                        outputs=[gen_video]
-                        )
-        else:
-            submit.click(
-                        fn=sad_talker.test, 
-                        inputs=[source_image,
-                                driven_audio,
-                                preprocess_type,
-                                is_still_mode,
-                                enhancer,
-                                batch_size,                            
-                                size_of_image,
-                                pose_style
-                                ], 
-                        outputs=[gen_video]
-                        )
-
-    return sadtalker_interface
- 
-
-if __name__ == "__main__":
-
-    demo = sadtalker_demo()
-    demo.queue()
-    demo.launch(share=True)
-
-