虚拟人主播的诞生之AIGC技术原理与未来前景

import open3d as o3d
import numpy as np
 
# 创建一个简单的立方体
mesh = o3d.geometry.TriangleMesh.create_box(width=1.0, height=1.0, depth=1.0)
mesh.compute_vertex_normals()
mesh.paint_uniform_color([0.1, 0.1, 0.7])
 
# 显示立方体
o3d.visualization.draw_geometries([mesh])

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from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')
 
input_text = "Hello, I am a virtual influencer."
input_ids = tokenizer.encode(input_text, return_tensors='pt')
 
output = model.generate(input_ids, max_length=50, num_return_sequences=1)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
 
print(generated_text)

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from gtts import gTTS
import os
 
text = "Hello, I am a virtual influencer."
tts = gTTS(text=text, lang='en')
tts.save("output.mp3")
 
# 播放音频
os.system("mpg321 output.mp3")

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import bpy
 
# 创建一个简单的立方体
bpy.ops.mesh.primitive_cube_add()
cube = bpy.context.object
 
# 添加关键帧动画
cube.location = (0, 0, 0)
cube.keyframe_insert(data_path="location", frame=1)
cube.location = (2, 2, 2)
cube.keyframe_insert(data_path="location", frame=50)
 
# 渲染动画
bpy.ops.render.render(animation=True)

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import torch
from torchvision import transforms
from PIL import Image
import matplotlib.pyplot as plt
from stylegan2_pytorch import model
 
# 加载预训练的StyleGAN2模型
g_ema = model.Generator(1024, 512, 8)
g_ema.load_state_dict(torch.load('path_to_pretrained_model.pt')['g_ema'])
g_ema.eval（)
 
# 生成随机潜在向量
z = torch.randn(1, 512)
 
# 生成虚拟人脸
with torch.no_grad():
    generated_image = g_ema(z, truncation=0.7, truncation_latent=None)
 
# 转换并显示生成的图像
to_pil = transforms.ToPILImage()
image = to_pil(generated_image[0].clamp_(0, 1))
plt.imshow(image)
plt.axis('off')
plt.show()

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import openai
 
 
# 定义对话上下文
prompt = "User: What is the weather like today?\nAI:"
 
# 生成响应
response = openai.Completion.create(
  engine="text-davinci-003",
  prompt=prompt,
  max_tokens=50
)
 
print(response.choices[0].text.strip())

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import torch
from transformers import Tacotron2, WaveNet, Tacotron2Processor
 
# 加载预训练模型和处理器
tacotron2 = Tacotron2.from_pretrained('tacotron2')
wavenet = WaveNet.from_pretrained('wavenet')
processor = Tacotron2Processor()
 
# 文本转换为语音
text = "Hello, I am a virtual influencer."
inputs = processor(text, return_tensors='pt')
mel_outputs, _, _ = tacotron2(inputs['input_ids'])
audio = wavenet(mel_outputs)
 
# 保存生成的语音
with open("output.wav", "wb") as f:
    f.write(audio.numpy().tobytes())

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import onnx
import onnxruntime as ort
 
# 加载ONNX模型
onnx_model_path = 'path_to_optimized_model.onnx'
onnx_model = onnx.load(onnx_model_path)
onnx.checker.check_model(onnx_model)
 
# 创建ONNX推理会话
ort_session = ort.InferenceSession(onnx_model_path)
 
# 准备输入数据
input_name = ort_session.get_inputs()[0].name
output_name = ort_session.get_outputs()[0].name
input_data = ...
 
# 进行推理
outputs = ort_session.run([output_name], {input_name: input_data})
print(outputs)

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