MIDI-86: VQ-VAE blogpost

[SamuelJanas]

MIDI-86
This is a prototype version of the blogpost. I'll be adding results and images in the TODO sections. Let me know If we would like to include anything else here. I was trying to make it a quick read that would leave you with some intuition on the topic. I also tried to share some overview on the results of the project to make it seem more appealing(?).

[SamuelJanas]

I'm also getting this error trying to run hexo server inside container:

 Nunjucks Error: _posts/Hello-MIDI.md [Line 8, Column 4] unknown block tag: algrtmImgBanner

Tried removing lines with this block tag, but it seems to be an issue with all of them. Let me know if it's something that I'm doing incorrectly or if it's reproducible.

[roszcz]

I'm also getting this error trying to run hexo server inside container:

 Nunjucks Error: _posts/Hello-MIDI.md [Line 8, Column 4] unknown block tag: algrtmImgBanner

Tried removing lines with this block tag, but it seems to be an issue with all of them. Let me know if it's something that I'm doing uncorecctly or if it's reproducible.

Sorry, I failed to include this information in the README - you have to pull the "theme" code, which is a submodule in this repository:

git submodule init
git submodule update

[roszcz]

Thanks!

I think this post would work better if it was only about ECG data - I feel like the appearance of MIDI will make it confusing to most readers. If you remove it, you got provide a bit more detail about ECG signals - I think it's worth explaining what is a multichannel 1D signal in this context :)

[roszcz]

Can you add a code snippet demonstrating this? As minimalistic as possible :)

[SamuelJanas]

What do you think about

class Encoder(nn.Module):
    def __init__(self):
        super(Encoder, self).__init__()
        self.layer = nn.Linear(10, 5)
        
    def forward(self, x):
        return self.layer(x)

class Codebook(nn.Module):
    def __init__(self, num_codes, code_dim):
        super(Codebook, self).__init__()
        self.codebook = nn.Parameter(torch.randn(num_codes, code_dim))
        
    def forward(self, z):
        distances = ((z.unsqueeze(1) - self.codebook.detach().unsqueeze(0))**2).sum(-1)
        indices = torch.argmin(distances, dim=1)
        return self.codebook[indices]

class Decoder(nn.Module):
    def __init__(self):
        super(Decoder, self).__init__()
        self.layer = nn.Linear(5, 10)
        
    def forward(self, z):
        return self.layer(z)

And training showcase as follows:

for epoch in range(1000):
    # Simulated input data
    x = torch.randn(32, 10)
    
    # Forward pass
    z_e = encoder(x)
    z_q = codebook(z_e)
    x_recon = decoder(z_q)
    
    # Reconstruction loss
    loss = ((x - x_recon)**2).mean()
    
    # Backward pass and optimization
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    if (epoch+1) % 100 == 0:
        print(f"Epoch [{epoch+1}/1000], Loss: {loss.item():.4f}")

I feel like it's an elegant way to show how the codebook works on a toy example. If you had something else in mind let me know