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：开源AI数字人系统，AI数字人系统开发
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
