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gradio_app.py
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gradio_app.py
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import os, sys
import gradio as gr
# import tempfile
# from huggingface_hub import snapshot_download
# from modelscope.outputs import OutputKeys
# from modelscope.pipelines import pipeline
# from modelscope.utils.constant import Tasks
from src.gradio_demo import SadTalker
from src.utils.text2speech import text_to_speech_edge, tts_speakers_map
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)
gr.Markdown("""该标签页的功能基于[SadTalker](https://sadtalker.github.io)实现，要使用该标签页，请按照README安装相关依赖。\n
The function of this tab is implemented based on [SadTalker](https://sadtalker.github.io), to use this tab, you should follow the installation guide in README. """)
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> \
<a style='font-size:18px;color: #efefef' href='https://sadtalker.github.io'>Homepage</a> \
<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'):
source_image = gr.Image(label="源图片(source image)", source="upload", type="filepath")
driven_audio = gr.Audio(label="驱动音频(driven audio)", source="upload", type="filepath")
input_text = gr.Textbox(label="用文本生成音频(Generating audio from text)", lines=1, value="大家好，欢迎使用阿里达摩院开源的face chain项目！")
speaker = gr.Dropdown(choices=list(tts_speakers_map.keys()), value="普通话(中国大陆)-Xiaoxiao-女", label="请根据输入文本选择对应的语言和说话人(Select speaker according the language of input text)")
tts = gr.Button('生成音频(Generate audio)')
tts.click(fn=text_to_speech_edge, inputs=[input_text, speaker], outputs=[driven_audio])
# if sys.platform == 'linux':
# model_id = 'damo/speech_sambert-hifigan_tts_zh-cn_16k'
# tts_talker = pipeline(task=Tasks.text_to_speech, model=model_id)
# 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.")
# d = {"男生": "zhiyan_emo", "女生": "zhitian_emo"}
# speaker = gr.Dropdown(d.keys(), value="女生", label="Select speaker")
# tts = gr.Button('Generate audio',elem_id="sadtalker_audio_generate", variant='primary')
# lambda_fn = lambda input_text, speaker: tts_talker(input_text, voice=d[speaker])[OutputKeys.OUTPUT_WAV]
# tts.click(fn=lambda_fn, inputs=[input_text, speaker], outputs=[driven_audio])
with gr.Column(variant='panel'):
with gr.Box():
gr.Markdown("设置(Settings)")
with gr.Column(variant='panel'):
# with gr.Accordion("高级选项(Advanced Options)", open=False):
pose_style = gr.Slider(minimum=0, maximum=45, step=1, label="头部姿态(Pose style)", info="模型自主学习到的头部姿态(the head pose style that model learn)", value=0)
exp_weight = gr.Slider(minimum=0.5, maximum=2, step=0.1, label="表情系数(expression scale)", info="数值越大，表情越夸张(the higher, the more exaggerated)", value=1)
with gr.Row():
size_of_image = gr.Radio([256, 512], value=256, label='人脸模型分辨率(face model resolution)', info="使用哪种输入分辨率的模型(use which model with this input size)")
preprocess_type = gr.Radio(['crop', 'resize','full'], value='full', label='预处理(preprocess)', info="如果源图片是全身像，`crop`会裁剪到只剩人脸区域")
is_still_mode = gr.Checkbox(value=True, label="静止模式(Still Mode)", info="更少的头部运动(fewer head motion)")
enhancer = gr.Checkbox(label="使用GFPGAN增强人脸清晰度(GFPGAN as Face enhancer)")
batch_size = gr.Slider(label="批次大小(batch size)", step=1, maximum=10, value=1, info="当处理长视频，可以分成多段并行合成(when systhesizing long video, this will process it in parallel)")
submit = gr.Button('生成(Generate)', variant='primary')
with gr.Box():
infer_progress = gr.Textbox(value="当前无任务(No task currently)", show_label=False, interactive=False)
gen_video = gr.Video(label="Generated video", format="mp4", 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,
exp_weight
],
outputs=[gen_video]
)
with gr.Row():
examples = [
[
'examples/source_image/man.png',
'examples/driven_audio/chinese_poem1.wav',
'full',
True,
False
],
[
'examples/source_image/women.png',
'examples/driven_audio/chinese_poem2.wav',
'full',
False,
False
],
]
gr.Examples(examples=examples,
inputs=[
source_image,
driven_audio,
preprocess_type,
is_still_mode,
enhancer],
outputs=[gen_video],
fn=sad_talker.test,
cache_examples=os.getenv('SYSTEM') == 'spaces')
return sadtalker_interface
if __name__ == "__main__":
demo = sadtalker_demo()
demo.queue()
demo.launch()