人工智能-音乐创作（变分自编码器（VAE）、生成对抗网络（GAN）和Transformer架构）

以下分别为你提供使用变分自编码器（VAE）、生成对抗网络（GAN）和Transformer架构进行音乐创作的代码示例。这些示例基于PyTorch框架，并使用了一些简单的音乐表示方法，实际应用中可能需要根据具体的音乐数据和任务进行调整。

变分自编码器（VAE）实现音乐创作

变分自编码器常用于生成连续分布的数据，适用于学习音乐的潜在表示并生成新的音乐。

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
import numpy as np
import matplotlib.pyplot as plt# 假设音乐数据被编码为长度为100的向量
# 这里简单生成一些随机数据作为示例，实际应用中应替换为真实音乐数据
num_samples = 1000
data = np.random.randn(num_samples, 100)
data = torch.FloatTensor(data)
dataset = TensorDataset(data)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)class VAE(nn.Module):def __init__(self, input_dim, hidden_dim, latent_dim):super(VAE, self).__init__()self.encoder = nn.Sequential(nn.Linear(input_dim, hidden_dim),nn.ReLU(True),nn.Linear(hidden_dim, 2 * latent_dim))self.decoder = nn.Sequential(nn.Linear(latent_dim, hidden_dim),nn.ReLU(True),nn.Linear(hidden_dim, input_dim),nn.Tanh())def reparameterize(self, mu, logvar):std = torch.exp(0.5 * logvar)eps = torch.randn_like(std)return mu + eps * stddef forward(self, x):h = self.encoder(x)mu, logvar = torch.chunk(h, 2, dim=1)z = self.reparameterize(mu, logvar)recon_x = self.decoder(z)return recon_x, mu, logvarinput_dim = 100
hidden_dim = 256
latent_dim = 10
vae = VAE(input_dim, hidden_dim, latent_dim)
optimizer = optim.Adam(vae.parameters(), lr=1e-3)
criterion = nn.MSELoss()for epoch in range(100):for batch in dataloader:x = batch[0]optimizer.zero_grad()recon_x, mu, logvar = vae(x)recon_loss = criterion(recon_x, x)kl_loss = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp()).sum(dim=1).mean()loss = recon_loss + kl_lossloss.backward()optimizer.step()print(f'Epoch {epoch + 1}, Loss: {loss.item()}')# 生成新音乐
with torch.no_grad():z = torch.randn(1, latent_dim)new_music = vae.decoder(z)print("生成的新音乐向量:", new_music)

生成对抗网络（GAN）实现音乐创作

生成对抗网络通过生成器和判别器的对抗训练来生成新的数据。

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
import numpy as np# 假设音乐数据被编码为长度为100的向量
# 这里简单生成一些随机数据作为示例，实际应用中应替换为真实音乐数据
num_samples = 1000
data = np.random.randn(num_samples, 100)
data = torch.FloatTensor(data)
dataset = TensorDataset(data)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)class Generator(nn.Module):def __init__(self, input_dim, hidden_dim, output_dim):super(Generator, self).__init__()self.model = nn.Sequential(nn.Linear(input_dim, hidden_dim),nn.ReLU(True),nn.Linear(hidden_dim, hidden_dim),nn.ReLU(True),nn.Linear(hidden_dim, output_dim),nn.Tanh())def forward(self, z):return self.model(z)class Discriminator(nn.Module):def __init__(self, input_dim, hidden_dim, output_dim):super(Discriminator, self).__init__()self.model = nn.Sequential(nn.Linear(input_dim, hidden_dim),nn.LeakyReLU(0.2, inplace=True),nn.Linear(hidden_dim, hidden_dim),nn.LeakyReLU(0.2, inplace=True),nn.Linear(hidden_dim, output_dim),nn.Sigmoid())def forward(self, x):return self.model(x)input_dim = 10
hidden_dim = 256
output_dim = 100
generator = Generator(input_dim, hidden_dim, output_dim)
discriminator = Discriminator(output_dim, hidden_dim, 1)optimizer_G = optim.Adam(generator.parameters(), lr=1e-3)
optimizer_D = optim.Adam(discriminator.parameters(), lr=1e-3)criterion = nn.BCELoss()for epoch in range(100):for i, batch in enumerate(dataloader):real_music = batch[0]batch_size = real_music.size(0)# 训练判别器optimizer_D.zero_grad()z = torch.randn(batch_size, input_dim)fake_music = generator(z)real_labels = torch.ones(batch_size, 1)fake_labels = torch.zeros(batch_size, 1)real_output = discriminator(real_music)real_loss = criterion(real_output, real_labels)fake_output = discriminator(fake_music.detach())fake_loss = criterion(fake_output, fake_labels)d_loss = real_loss + fake_lossd_loss.backward()optimizer_D.step()# 训练生成器optimizer_G.zero_grad()fake_output = discriminator(fake_music)g_loss = criterion(fake_output, real_labels)g_loss.backward()optimizer_G.step()print(f'Epoch {epoch + 1}, D Loss: {d_loss.item()}, G Loss: {g_loss.item()}')# 生成新音乐
with torch.no_grad():z = torch.randn(1, input_dim)new_music = generator(z)print("生成的新音乐向量:", new_music)

Transformer架构实现音乐创作

Transformer架构擅长处理序列数据，在音乐创作中可以用于学习音乐序列的模式。

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
import numpy as np# 假设音乐数据被编码为长度为100的序列，每个元素是一个独热编码的音符表示
# 这里简单生成一些随机数据作为示例，实际应用中应替换为真实音乐数据
num_samples = 1000
num_notes = 128
sequence_length = 100
data = np.random.randint(0, num_notes, size=(num_samples, sequence_length))
data = torch.LongTensor(data)
dataset = TensorDataset(data)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)class TransformerModel(nn.Module):def __init__(self, num_tokens, d_model, nhead, num_layers, dim_feedforward):super(TransformerModel, self).__init__()self.embedding = nn.Embedding(num_tokens, d_model)self.position_encoding = nn.Parameter(torch.zeros(1, sequence_length, d_model))self.transformer = nn.TransformerEncoder(nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward),num_layers)self.fc = nn.Linear(d_model, num_tokens)def forward(self, x):x = self.embedding(x)x = x + self.position_encoding[:, :x.size(1), :]x = self.transformer(x)x = self.fc(x)return xnum_tokens = num_notes
d_model = 128
nhead = 4
num_layers = 3
dim_feedforward = 512
model = TransformerModel(num_tokens, d_model, nhead, num_layers, dim_feedforward)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
criterion = nn.CrossEntropyLoss()for epoch in range(100):for batch in dataloader:x = batch[0]optimizer.zero_grad()output = model(x)loss = criterion(output.view(-1, num_tokens), x.view(-1))loss.backward()optimizer.step()print(f'Epoch {epoch + 1}, Loss: {loss.item()}')# 生成新音乐
with torch.no_grad():start_sequence = torch.randint(0, num_notes, (1, 1))generated_sequence = start_sequencefor _ in range(sequence_length - 1):output = model(generated_sequence)next_note = torch.argmax(output[:, -1, :], dim=1).unsqueeze(1)generated_sequence = torch.cat((generated_sequence, next_note), dim=1)print("生成的新音乐序列:", generated_sequence)

代码解释

VAE示例：
- 定义了编码器和解码器网络结构，通过重参数化技巧从潜在空间采样生成新数据。
- 使用均方误差损失（MSELoss）作为重建损失，KL散度作为正则化项，优化模型以最小化总体损失。
GAN示例：
- 分别定义了生成器和判别器网络，生成器从随机噪声生成音乐数据，判别器区分真实和生成的数据。
- 使用二元交叉熵损失（BCELoss），在训练过程中交替优化生成器和判别器，使它们相互对抗。
Transformer示例：
- 构建了基于Transformer的模型，包括嵌入层、位置编码、Transformer编码器和全连接层。
- 使用交叉熵损失（CrossEntropyLoss）训练模型，以预测音乐序列中的下一个音符，生成新的音乐序列。