数字人直播系统涉及自然语言处理、语音合成、图形渲染、直播推流等多领域技术，要实现完整功能需多模块协同。以下代码在之前基础上全面升级，使用 Python 结合多个库，涵盖语音交互、智能回复、唇形同步、表情动作模拟、直播推流（以 B 站为例）等核心功能。运行代码前，需安装SpeechRecognition、transformers、gTTS、moviepy、opencv - python、pytube、bili - live - danmaku等库，安装命令为pip install SpeechRecognition transformers gTTS moviepy opencv - python pytube bili - live - danmaku。
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
from pytube import YouTube
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，根据语音时长和音素特征调整视频帧（更复杂版本）
def lip_sync_video(video_path, audio_path):
video = VideoFileClip(video_path)
audio = AudioFileClip(audio_path)
video_duration = video.duration
audio_duration = audio.duration
# 简单的音素分析模拟（实际需要更专业的音素分析库）
# 这里假设每个单词对应一个音素变化，根据单词数量调整视频帧
words = audio.reader.get_metadata()['duration'] / 1000
num_words = len(words.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（简单缩放视频帧中嘴唇部分）
def adjust_lips(frame):
height, width, _ = frame.shape
lips_y = int(height * 0.6)
lips_height = int(height * 0.2)
lips_frame = frame[lips_y:lips_y + lips_height, :]
resized_lips = cv2.resize(lips_frame, None, fx=(1 + num_words / 10), fy=1)
frame[lips_y:lips_y + resized_lips.shape[0], :] = 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（简单模拟）
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):
# 简单旋转视频表示开心动作
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"
# 获取直播源视频（以YouTube为例）
def get_live_source_video(youtube_url):
yt = YouTube(youtube_url)
stream = yt.streams.filter(file_extension='mp4', adaptive=True).first()
stream.download(filename='live_source_video.mp4')
return 'live_source_video.mp4'
# B站直播推流函数（需自行替换房间号和直播密钥等信息）
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("这是直播内容") # 可根据需求发送弹幕
await room.send_frame(frame)
cap.release()
await room.close()
# 主函数，整合所有功能
def main():
youtube_url = "https://www.youtube.com/watch?v=xxxxxxx" # 替换为实际YouTube直播链接
live_source_video_path = get_live_source_video(youtube_url)
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)
video_path = live_source_video_path
synced_video_path = lip_sync_video(video_path, audio_path)
expression_and_action_path = simulate_digital_human_expression_and_action(synced_video_path, audio_path)
sync(bili_live_push(expression_and_action_path))
os.remove(audio_path)
os.remove(synced_video_path)
os.remove(expression_and_action_path)
user_input = recognize_speech()
if __name__ == "__main__":
main()
这段代码对唇形同步和表情动作模拟进行了更深入的处理，虽仍为简化版模拟，但相比之前更加完善。在实际应用中，如需更逼真的数字人效果，需要使用专业的图形引擎（如 Unity、Unreal Engine），结合动作捕捉、面部识别等技术进行开发。