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app_img.py
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app_img.py
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import os
import gradio as gr
from zhconv import convert
from LLM import LLM
from ASR import WhisperASR
from TFG import SadTalker
from TTS import EdgeTTS
from src.cost_time import calculate_time
from configs import *
description = """<p style="text-align: center; font-weight: bold;">
<span style="font-size: 28px;">Linly 智能对话系统 (Linly-Talker)</span>
<br>
<span style="font-size: 18px;" id="paper-info">
[<a href="https://zhuanlan.zhihu.com/p/671006998" target="_blank">知乎</a>]
[<a href="https://www.bilibili.com/video/BV1rN4y1a76x/" target="_blank">bilibili</a>]
[<a href="https://github.com/Kedreamix/Linly-Talker" target="_blank">GitHub</a>]
[<a herf="https://kedreamix.github.io/" target="_blank">个人主页</a>]
</span>
<br>
<span>Linly-Talker 是一款智能 AI 对话系统，结合了大型语言模型 (LLMs) 与视觉模型，是一种新颖的人工智能交互方式。</span>
</p>
"""
use_ref_video = False
ref_video = None
ref_info = 'pose'
use_idle_mode = False
length_of_audio = 5
@calculate_time
def Asr(audio):
try:
question = asr.transcribe(audio)
question = convert(question, 'zh-cn')
except Exception as e:
print("ASR Error: ", e)
question = 'Gradio存在一些bug，麦克风模式有时候可能音频还未传入，请重新点击一下语音识别即可'
gr.Warning(question)
return question
@calculate_time
def TTS_response(text,
voice, rate, volume, pitch,):
try:
tts.predict(text, voice, rate, volume, pitch , 'answer.wav', 'answer.vtt')
except:
os.system(f'edge-tts --text "{text}" --voice {voice} --write-media answer.wav')
return 'answer.wav', 'answer.vtt'
@calculate_time
def LLM_response(question, voice = 'zh-CN-XiaoxiaoNeural', rate = 0, volume = 0, pitch = 0):
answer = llm.generate(question)
print(answer)
TTS_response(answer, voice, rate, volume, pitch)
return 'answer.wav', 'answer.vtt', answer
@calculate_time
def Talker_response(text, voice, rate, volume, pitch, source_image,
preprocess_type,
is_still_mode,
enhancer,
batch_size,
size_of_image,
pose_style,
facerender,
exp_weight,
blink_every,
fps):
voice = 'zh-CN-XiaoxiaoNeural' if voice not in tts.SUPPORTED_VOICE else voice
driven_audio, driven_vtt, _ = LLM_response(text, voice, rate, volume, pitch)
video = talker.test2(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,
fps=fps)
if driven_vtt:
return video, driven_vtt
else:
return video
def main():
with gr.Blocks(analytics_enabled=False, title = 'Linly-Talker') as inference:
gr.HTML(description)
with gr.Row(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", type="filepath", elem_id="img2img_image", width=512)
with gr.Tabs(elem_id="question_audio"):
with gr.TabItem('对话'):
with gr.Column(variant='panel'):
question_audio = gr.Audio(sources=['microphone','upload'], type="filepath", label = '语音对话')
input_text = gr.Textbox(label="Input Text", lines=3, info = '文字对话')
with gr.Accordion("Advanced Settings",
open=False,
visible=True) as parameter_article:
voice = gr.Dropdown(tts.SUPPORTED_VOICE,
value='zh-CN-XiaoxiaoNeural',
label="Voice")
rate = gr.Slider(minimum=-100,
maximum=100,
value=0,
step=1.0,
label='Rate')
volume = gr.Slider(minimum=0,
maximum=100,
value=100,
step=1,
label='Volume')
pitch = gr.Slider(minimum=-100,
maximum=100,
value=0,
step=1,
label='Pitch')
asr_text = gr.Button('语音识别（语音对话后点击）')
asr_text.click(fn=Asr,inputs=[question_audio],outputs=[input_text])
# with gr.Tabs(elem_id="response_audio"):
# with gr.TabItem("语音选择"):
# with gr.Column(variant='panel'):
# voice = gr.Dropdown(VOICES, values='zh-CN-XiaoxiaoNeural')
with gr.Tabs(elem_id="text_examples"):
gr.Markdown("## Text Examples")
examples = [
['应对压力最有效的方法是什么？'],
['如何进行时间管理？'],
['为什么有些人选择使用纸质地图或寻求方向，而不是依赖GPS设备或智能手机应用程序？'],
['近日，苹果公司起诉高通公司，状告其未按照相关合约进行合作，高通方面尚未回应。这句话中“其”指的是谁？'],
['三年级同学种树80颗，四、五年级种的棵树比三年级种的2倍多14棵，三个年级共种树多少棵?'],
['撰写一篇交响乐音乐会评论，讨论乐团的表演和观众的整体体验。'],
['翻译成中文：Luck is a dividend of sweat. The more you sweat, the luckier you get.'],
]
gr.Examples(
examples = examples,
inputs = [input_text],
)
# driven_audio = 'answer.wav'
with gr.Column(variant='panel'):
with gr.Tabs(elem_id="sadtalker_checkbox"):
with gr.TabItem('Settings'):
with gr.Accordion("Advanced Settings",
open=False):
gr.Markdown("SadTalker: 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
with gr.Row():
pose_style = gr.Slider(minimum=0, maximum=45, step=1, label="Pose style", value=0) #
exp_weight = gr.Slider(minimum=0, maximum=3, step=0.1, label="expression scale", value=1) #
blink_every = gr.Checkbox(label="use eye blink", value=True)
with gr.Row():
size_of_image = gr.Radio([256, 512], value=256, label='face model resolution', info="use 256/512 model? 256 is faster") #
preprocess_type = gr.Radio(['crop', 'resize','full', 'extcrop', 'extfull'], value='crop', label='preprocess', info="How to handle input image?")
with gr.Row():
is_still_mode = gr.Checkbox(label="Still Mode (fewer head motion, works with preprocess `full`)")
facerender = gr.Radio(['facevid2vid', 'PIRender'], value='facevid2vid', label='facerender', info="which face render?")
with gr.Row():
batch_size = gr.Slider(label="batch size in generation", step=1, maximum=10, value=1)
fps = gr.Slider(label='fps in generation', step=1, maximum=30, value =20)
enhancer = gr.Checkbox(label="GFPGAN as Face enhancer(slow)")
with gr.Tabs(elem_id="sadtalker_genearted"):
gen_video = gr.Video(label="Generated video", format="mp4",scale=0.8)
submit = gr.Button('Generate', elem_id="sadtalker_generate", variant='primary')
submit.click(
fn=Talker_response,
inputs=[input_text,
voice, rate, volume, pitch,
source_image,
preprocess_type,
is_still_mode,
enhancer,
batch_size,
size_of_image,
pose_style,
facerender,
exp_weight,
blink_every,
fps],
outputs=[gen_video]
)
with gr.Row():
examples = [
[
'examples/source_image/full_body_2.png',
'crop',
False,
False
],
[
'examples/source_image/full_body_1.png',
'crop',
False,
False
],
[
'examples/source_image/full3.png',
'crop',
False,
False
],
[
'examples/source_image/full4.jpeg',
'crop',
False,
False
],
[
'examples/source_image/art_13.png',
'crop',
False,
False
],
[
'examples/source_image/art_5.png',
'crop',
False,
False
],
]
gr.Examples(examples=examples,
fn=Talker_response,
inputs=[
source_image,
preprocess_type,
is_still_mode,
enhancer],
outputs=[gen_video],
# cache_examples=True,
)
return inference
if __name__ == "__main__":
# llm = LLM(mode='offline').init_model('Linly', 'Linly-AI/Chinese-LLaMA-2-7B-hf')
# llm = LLM(mode='offline').init_model('Gemini', 'gemini-pro', api_key = "your api key")
# llm = LLM(mode='offline').init_model('Qwen', 'Qwen/Qwen-1_8B-Chat')
llm = LLM(mode=mode).init_model('Qwen', 'Qwen/Qwen-1_8B-Chat')
talker = SadTalker(lazy_load=True)
asr = WhisperASR('base')
tts = EdgeTTS()
gr.close_all()
demo = main()
demo.queue()
# demo.launch()
demo.launch(server_name=ip, # 本地端口localhost:127.0.0.1 全局端口转发:"0.0.0.0"
server_port=port,
# 似乎在Gradio4.0以上版本可以不使用证书也可以进行麦克风对话
ssl_certfile=ssl_certfile,
ssl_keyfile=ssl_keyfile,
ssl_verify=False,
debug=True)