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app_musetalk.py
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import os
import gradio as gr
from zhconv import convert
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 LLM_response(question, voice = 'zh-CN-XiaoxiaoNeural', rate = 0, volume = 0, pitch = 0):
answer = llm.generate(question)
print(answer)
try:
os.system(f'edge-tts --text "{answer}" --voice {voice} --write-media answer.wav --write-subtitles answer.vtt')
except:
tts.predict(answer, voice, rate, volume, pitch , 'answer.wav', 'answer.vtt')
return 'answer.wav', 'answer.vtt', answer
@calculate_time
def Talker_response(text, voice, rate, volume, pitch, source_video, bbox_shift):
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 = musetalker.inference_noprepare(driven_audio,
source_video,
bbox_shift)
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('MuseV Video'):
gr.Markdown("MuseV: need help? please visit MuseVDemo to generate Video https://huggingface.co/spaces/AnchorFake/MuseVDemo")
with gr.Row():
source_video = gr.Video(label="Reference Video",sources=['upload'])
gr.Markdown("BBox_shift 推荐值下限，在生成初始结果后生成相应的 bbox 范围。如果结果不理想，可以根据该参考值进行调整。\n一般来说，在我们的实验观察中，我们发现正值（向下半部分移动）通常会增加嘴巴的张开度，而负值（向上半部分移动）通常会减少嘴巴的张开度。然而，需要注意的是，这并不是绝对的规则，用户可能需要根据他们的具体需求和期望效果来调整该参数。")
with gr.Row():
bbox_shift = gr.Number(label="BBox_shift value, px", value=0)
bbox_shift_scale = gr.Textbox(label="bbox_shift_scale",
value="",interactive=False)
source_video.change(fn=musetalker.prepare_material, inputs=[source_video, bbox_shift], outputs=[source_video, bbox_shift_scale])
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():
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.TabItem("MuseTalk Video"):
gen_video = gr.Video(label="Generated video", format="mp4")
submit = gr.Button('Generate', elem_id="sadtalker_generate", variant='primary')
examples = [os.path.join('Musetalk/data/video', video) for video in os.listdir("Musetalk/data/video")]
# ['Musetalk/data/video/yongen_musev.mp4', 'Musetalk/data/video/musk_musev.mp4', 'Musetalk/data/video/monalisa_musev.mp4', 'Musetalk/data/video/sun_musev.mp4', 'Musetalk/data/video/seaside4_musev.mp4', 'Musetalk/data/video/sit_musev.mp4', 'Musetalk/data/video/man_musev.mp4']
gr.Markdown("## MuseV Video Examples")
gr.Examples(
examples=[
['Musetalk/data/video/yongen_musev.mp4', 5],
['Musetalk/data/video/musk_musev.mp4', 5],
['Musetalk/data/video/monalisa_musev.mp4', 5],
['Musetalk/data/video/sun_musev.mp4', 5],
['Musetalk/data/video/seaside4_musev.mp4', 5],
['Musetalk/data/video/sit_musev.mp4', 5],
['Musetalk/data/video/man_musev.mp4', 5]
],
inputs =[source_video, bbox_shift],
)
submit.click(
fn=Talker_response,
inputs=[input_text,
voice, rate, volume, pitch,
source_video, bbox_shift],
outputs=[gen_video]
)
return inference
def success_print(text):
print(f"\033[1;31;42m{text}\033[0m")
def error_print(text):
print(f"\033[1;37;41m{text}\033[0m")
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')
try:
from LLM import LLM
llm = LLM(mode=mode).init_model('Qwen', 'Qwen/Qwen-1_8B-Chat')
except Exception as e:
error_print(f"LLM is not ready, error: {e}")
error_print("如果使用LLM，请先下载有关的LLM模型")
try:
from TTS import EdgeTTS
tts = EdgeTTS()
except Exception as e:
error_print(f"EdgeTTS Error: {e}")
error_print("如果使用EdgeTTS，请先下载EdgeTTS库，测试EdgeTTS是否可用")
try:
from ASR import WhisperASR
asr = WhisperASR('base')
except Exception as e:
error_print(f"ASR Error: {e}")
error_print("如果使用ASR，请先下载ASR相关模型，如Whisper")
try:
from TFG import MuseTalk_RealTime
musetalker = MuseTalk_RealTime()
except Exception as e:
error_print(f"MuseTalk Error: {e}")
error_print("如果使用MuseTalk，请先下载MuseTalk相关模型")
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)