多模态AI系统：结合视觉、语言和声音的智能

from transformers import CLIPProcessor, CLIPModel
from PIL import Image
import torch

# 加载CLIP模型和处理器
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16")
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch16")

# 加载图片
image = Image.open("example_image.jpg")

# 定义描述文本
texts = ["a photo of a cat", "a photo of a dog", "a person riding a bike"]

# 预处理输入数据
inputs = processor(text=texts, images=image, return_tensors="pt", padding=True)

# 进行模型推理
outputs = model(**inputs)

# 计算文本与图像的相似度
logits_per_image = outputs.logits_per_image # 图像与文本的匹配分数
probs = logits_per_image.softmax(dim=1) # 转换为概率

# 输出最相关的文本
best_match = texts[torch.argmax(probs)]
print(f"最相关的描述是: {best_match}")

from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import torch
import soundfile as sf

# 加载Wav2Vec2模型和处理器
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h")

# 读取音频文件
speech, samplerate = sf.read("example_audio.wav")

# 预处理音频数据
input_values = processor(speech, return_tensors="pt").input_values

# 进行语音识别
with torch.no_grad():
    logits = model(input_values).logits

# 获取识别结果
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.decode(predicted_ids[0])
print(f"识别结果: {transcription}")

import cv2
from transformers import pipeline
import soundfile as sf

# 加载情感分析模型
sentiment_analysis = pipeline("sentiment-analysis")

# 使用OpenCV检测面部表情
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
image = cv2.imread("face_image.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.1, 4)

# 假设我们只分析第一张检测到的人脸
for (x, y, w, h) in faces:
    face = image[y:y+h, x:x+w]
    # 在此处添加面部表情分析的代码（例如使用深度学习模型）

# 读取语音文件并进行情感分析
speech, samplerate = sf.read("example_audio.wav")
result = sentiment_analysis(" ".join([str(word) for word in speech]))
print(f"语音情感分析结果: {result}")

from fastdtw import fastdtw
from scipy.spatial.distance import euclidean

# 模拟两个模态的时间序列数据
audio_features = [1.0, 1.5, 2.0, 2.5, 3.0]
motion_features = [1.1, 1.4, 2.1, 2.4, 3.1]

# 使用动态时间规整进行对齐
distance, path = fastdtw(audio_features, motion_features, dist=euclidean)

print(f"最小对齐距离: {distance}")
print(f"对齐路径: {path}")

import torch
import torch.nn as nn

# 模拟视觉和语言特征
visual_features = torch.randn(32, 128)  # batch_size=32, feature_dim=128
language_features = torch.randn(32, 128)

# 定义多头注意力机制
class CrossModalAttention(nn.Module):
    def __init__(self, feature_dim, num_heads):
        super().__init__()
        self.attention = nn.MultiheadAttention(embed_dim=feature_dim, num_heads=num_heads)

    def forward(self, query, key, value):
        return self.attention(query, key, value)[0]

# 初始化注意力层
attention_layer = CrossModalAttention(feature_dim=128, num_heads=4)

# 计算视觉和语言模态的交互
fused_features, _ = attention_layer(visual_features, language_features, language_features)

print(f"融合后的特征维度: {fused_features.shape}")

from diffusers import StableDiffusionPipeline

# 加载预训练的Stable Diffusion模型
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
pipe = pipe.to("cuda")

# 输入文本描述
text_prompt = "A beautiful sunset over the ocean with palm trees"

# 生成图像
generated_image = pipe(text_prompt).images[0]

# 显示生成的图像
generated_image.show()

from torchvision import transforms
from PIL import Image

# 定义数据增强操作
transform = transforms.Compose([
    transforms.RandomResizedCrop(224),
    transforms.RandomHorizontalFlip(),
    transforms.ColorJitter(brightness=0.5, contrast=0.5),
    transforms.ToTensor()
])

# 加载图像并进行数据增强
image = Image.open("example_image.jpg")
augmented_image = transform(image)

print(f"增强后的图像尺寸: {augmented_image.shape}")

from transformers import VisionTextDualEncoderModel, VisionTextDualEncoderProcessor

# 加载预训练的多模态模型和处理器
model = VisionTextDualEncoderModel.from_pretrained("clip-vit-base-patch16")
processor = VisionTextDualEncoderProcessor.from_pretrained("clip-vit-base-patch16")

# 模拟数据集
images = [Image.open("image1.jpg"), Image.open("image2.jpg")]
texts = ["This is an image of a cat.", "This is an image of a dog."]

# 数据预处理
inputs = processor(images=images, text=texts, return_tensors="pt", padding=True)

# 微调模型
labels = torch.tensor([0, 1])  # 示例标签
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)

model.train()
for epoch in range(5):
    outputs = model(**inputs)
    logits = outputs.logits_per_image
    loss = loss_fn(logits, labels)
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()
    print(f"Epoch {epoch+1}, Loss: {loss.item()}")