开源 AI 数字人系统的功能代码因不同框架和侧重点各有差异，下面以so - vad（一个基于深度学习的语音活动检测开源库）和DeepFaceLab（用于人脸交换和数字人面部合成的开源工具，此处借鉴其面部处理思路）相关思路，结合之前代码结构为你呈现示例。此示例在语音处理、面部特征与动作模拟上参考了开源库的实现理念，进一步完善数字人系统功能。运行前请确保安装SpeechRecognition、transformers、gTTS、moviepy、opencv - python、tensorflow（用于深度学习相关操作）等库，安装命令如下：

import speech_recognition as sr​

from transformers import AutoTokenizer, AutoModelForCausalLM​

from gtts import gTTS​

from moviepy.editor import VideoFileClip, AudioFileClip, CompositeVideoClip, vfx​

import os​

import numpy as np​

import cv2​

import tensorflow as tf​

​

​

# 语音识别函数​

def recognize_speech():​

r = sr.Recognizer()​

with sr.Microphone() as source:​

print("请说话...")​

audio = r.listen(source)​

try:​

text = r.recognize_google(audio)​

print(f"识别到的内容: {text}")​

return text​

except sr.UnknownValueError:​

print("无法识别语音")​

return ""​

except sr.RequestError as e:​

print(f"请求错误; {e}")​

return ""​

​

​

# 自然语言理解与回复生成函数​

def generate_response(user_input):​

tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT - medium")​

model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT - medium")​

input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')​

output = model.generate(input_ids=input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)​

response = tokenizer.decode(output[:, input_ids.shape[-1]:][0], skip_special_tokens=True)​

return response​

​

​

# 语音合成并返回音频文件路径​

def text_to_speech(text, lang='zh - CN'):​

tts = gTTS(text=text, lang=lang)​

tts.save("response.mp3")​

return "response.mp3"​

​

​

# 唇形同步模拟，根据语音时长和音素特征调整视频帧（优化版本）​

def lip_sync_video(video_path, audio_path):​

video = VideoFileClip(video_path)​

audio = AudioFileClip(audio_path)​

video_duration = video.duration​

audio_duration = audio.duration​

# 借助开源语音活动检测思路，更精准分析语音有效部分​

r = sr.Recognizer()​

audio_data = sr.AudioData(audio.tobytes(), audio.fps, audio.sample_width)​

segments = r.adjust_for_ambient_noise(audio_data)​

word_count = len(segments.split())​

if video_duration > audio_duration:​

new_fps = video.fps * (audio_duration / video_duration)​

new_video = video.set_fps(new_fps)​

new_video = new_video.set_duration(audio_duration)​

else:​

new_video = video.set_duration(audio_duration)​

# 模拟根据音素调整嘴唇动作（基于面部关键点检测，类似DeepFaceLab思路）​

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')​

def adjust_lips(frame):​

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)​

faces = face_cascade.detectMultiScale(gray, 1.3, 5)​

for (x, y, w, h) in faces:​

lips_y = y + int(h * 0.6)​

lips_height = int(h * 0.2)​

lips_frame = frame[lips_y:lips_y + lips_height, x:x + w]​

resized_lips = cv2.resize(lips_frame, None, fx=(1 + word_count / 10), fy=1)​

frame[lips_y:lips_y + resized_lips.shape[0], x:x + resized_lips.shape[1]] = resized_lips​

return frame​

new_video = new_video.fl(adjust_lips)​

new_video.write_videofile("lipsynced_video.mp4", codec='libx264')​

return "lipsynced_video.mp4"​

​

​

# 模拟数字人表情和动作，结合语音情感分析（优化模拟）​

def simulate_digital_human_expression_and_action(video_path, audio_path):​

video = VideoFileClip(video_path)​

audio = AudioFileClip(audio_path)​

audio_duration = audio.duration​

# 简单的语音情感分析模拟（实际需专业情感分析库）​

# 这里根据音频音量判断情感，音量高假设为积极情感​

audio_data = audio.to_soundarray(fps=audio.fps)​

max_volume = np.max(np.abs(audio_data))​

if max_volume > 0.5: # 假设0.5为积极情感阈值​

def happy_action(frame):​

height, width, _ = frame.shape​

M = cv2.getRotationMatrix2D((width / 2, height / 2), 10, 1.1)​

return cv2.warpAffine(frame, M, (width, height))​

new_video = video.fl(happy_action)​

else:​

new_video = video​

new_video.write_videofile("expression_and_action_simulated_video.mp4", codec='libx264')​

return "expression_and_action_simulated_video.mp4"​

​

​

# 主函数，整合所有功能​

def main():​

base_video_path = "digital_human_base_video.mp4" # 假设已有基础数字人视频​

user_input = recognize_speech()​

while user_input.lower() != "退出":​

response = generate_response(user_input)​

print(f"数字人回复: {response}")​

audio_path = text_to_speech(response)​

synced_video_path = lip_sync_video(base_video_path, audio_path)​

expression_and_action_path = simulate_digital_human_expression_and_action(synced_video_path, audio_path)​

# 这里未涉及直播推流，若需可参考之前代码添加​

os.remove(audio_path)​

os.remove(synced_video_path)​

os.remove(expression_and_action_path)​

user_input = recognize_speech()​

​

​

if __name__ == "__main__":​

main()​