app.py

import os
import random 
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 *
os.environ["GRADIO_TEMP_DIR"]= './temp'

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>
"""

# 设定默认参数值，可修改
source_image = r'example.png'
blink_every = True
size_of_image = 256
preprocess_type = 'crop'
facerender = 'facevid2vid'
enhancer = False
is_still_mode = False
pic_path = "./inputs/girl.png"
crop_pic_path = "./inputs/first_frame_dir_girl/girl.png"
first_coeff_path = "./inputs/first_frame_dir_girl/girl.mat"
crop_info = ((403, 403), (19, 30, 502, 513), [40.05956541381802, 40.17324339233366, 443.7892505041507, 443.9029284826663])

exp_weight = 1

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:
        tts.predict(answer, voice, rate, volume, pitch , 'answer.wav', 'answer.vtt')
    except:
        os.system(f'edge-tts --text "{answer}" --voice {voice} --write-media answer.wav')
    return 'answer.wav', 'answer.vtt', answer

@calculate_time
def Talker_response(text, voice = 'zh-CN-XiaoxiaoNeural', rate = 0, volume = 100, pitch = 0, batch_size = 2):
    voice = 'zh-CN-XiaoxiaoNeural' if voice not in tts.SUPPORTED_VOICE else voice
    # print(voice , rate , volume , pitch)
    driven_audio, driven_vtt, _ = LLM_response(text, voice, rate, volume, pitch)
    pose_style = random.randint(0, 45)
    video = talker.test(pic_path,
                        crop_pic_path,
                        first_coeff_path,
                        crop_info,
                        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=20)
    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="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)
                            
                            with gr.Accordion("Advanced Settings(高级设置语音参数) ",
                                        open=False):
                                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')
                                batch_size = gr.Slider(minimum=1,
                                                    maximum=10,
                                                    value=2,
                                                    step=1,
                                                    label='Talker Batch size')
                            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.Column(variant='panel'):
                        #     input_text = gr.Textbox(label="Input Text", lines=3)
                        #     text_button = gr.Button("文字对话", variant='primary')
                        
                
            with gr.Column(variant='panel'): 
                with gr.Tabs():
                    with gr.TabItem('数字人问答'):
                        gen_video = gr.Video(label="Generated video", format="mp4", scale=1, autoplay=True)
                video_button = gr.Button("提交", variant='primary')
            video_button.click(fn=Talker_response,inputs=[input_text,voice, rate, volume, pitch, batch_size],outputs=[gen_video])

        with gr.Row():
            with gr.Column(variant='panel'):
                    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,
                        fn = Talker_response,
                        inputs = [input_text],
                        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='offline').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)