AI 虚拟主播系统功能繁多，需整合语音识别、自然语言处理、语音合成、视频处理及直播推流等多方面技术。以下代码以 Python 为基础，结合多个常用库，实现了从用户语音输入到虚拟主播回复并推流直播的基本功能。运行代码前，请确保安装SpeechRecognition、transformers、gTTS、moviepy、opencv - python、bili - live - danmaku（用于 B 站直播推流，可按需替换为其他平台相关库）等库，安装命令示例：

import speech_recognition as sr​

from transformers import AutoTokenizer, AutoModelForCausalLM​

from gtts import gTTS​

from moviepy.editor import VideoFileClip, AudioFileClip, CompositeVideoClip​

import os​

import numpy as np​

import cv2​

from bilibili_api import live, sync​

​

​

# 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，根据语音时长调整视频帧​

def lip_sync_video(video_path, audio_path):​

video = VideoFileClip(video_path)​

audio = AudioFileClip(audio_path)​

video_duration = video.duration​

audio_duration = audio.duration​

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)​

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

return "lipsynced_video.mp4"​

​

​

# 简单的表情和动作模拟，根据语音情感调整视频（假设音量高为积极情感）​

def simulate_expression_and_action(video_path, audio_path):​

video = VideoFileClip(video_path)​

audio = AudioFileClip(audio_path)​

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("action_simulated_video.mp4", codec='libx264')​

return "action_simulated_video.mp4"​

​

​

# B站直播推流函数（需自行替换房间号等信息）​

async def bili_live_push(video_path):​

room = live.LiveDanmaku(room_id=123456) # 替换为实际房间号​

await room.connect()​

cap = cv2.VideoCapture(video_path)​

while cap.isOpened():​

ret, frame = cap.read()​

if not ret:​

break​

frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)​

await room.send_danmaku("虚拟主播直播中") # 可自定义弹幕内容​

await room.send_frame(frame)​

cap.release()​

await room.close()​

​

​

# 主函数，整合所有功能​

def main():​

base_video_path = "virtual_anchor_base_video.mp4" # 假设已有基础虚拟主播视频​

user_input = recognize_speech()​

while user_input.lower() != "退出":​

response = generate_response(user_input)​

print(f"虚拟主播回复: {response}")​

audio_path = text_to_speech(response)​

synced_video_path = lip_sync_video(base_video_path, audio_path)​

action_simulated_path = simulate_expression_and_action(synced_video_path, audio_path)​

sync(bili_live_push(action_simulated_path))​

os.remove(audio_path)​

os.remove(synced_video_path)​

os.remove(action_simulated_path)​

user_input = recognize_speech()​

​

​

if __name__ == "__main__":​

main()​

这段代码中，recognize_speech函数实现语音识别，generate_response进行自然语言处理生成回复，text_to_speech完成语音合成，lip_sync_video和simulate_expression_and_action分别实现唇形同步与表情动作模拟，bili_live_push用于 B 站直播推流。实际应用中，若想实现更逼真的虚拟主播效果，需借助专业图形引擎（如 Unity、Unreal Engine）以及更复杂的动作捕捉、面部表情生成技术。