wav2vec2 trains now

.gitignore

@@ -1,3 +1,9 @@
+transformers/
+results/
+data/
+logs/
+*.lock
+*.toml
 *.eeg
 *.vhdr
 *.vmrk

.python-version

@@ -0,0 +1 @@
+3.12.5

CNN_check.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torchvision
+import torchvision.transforms as transforms
+
+# Define the CNN architecture
+class SimpleCNN(nn.Module):
+    def __init__(self):
+        super(SimpleCNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)  # Input channel = 1 (grayscale), Output channels = 32
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1) # Input channel = 32, Output channels = 64
+        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1) # Input channel = 64, Output channels = 128
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
+        self.fc1 = nn.Linear(128 * 4 * 4, 512)  # Assuming input image size is 28x28
+        self.fc2 = nn.Linear(512, 10)  # 10 classes for MNIST
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = self.pool(F.relu(self.conv3(x)))
+        x = x.view(-1, 128 * 4 * 4)  # Flatten the tensor
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+# Set device
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Prepare data loaders for MNIST dataset
+transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
+
+trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
+trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
+
+testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)
+testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)
+
+# Initialize network, criterion, and optimizer
+net = SimpleCNN().to(device)
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(net.parameters(), lr=0.001)
+
+# Training loop
+epochs = 5000
+for epoch in range(epochs):
+    running_loss = 0.0
+    for images, labels in trainloader:
+        images, labels = images.to(device), labels.to(device)
+
+        # Zero the parameter gradients
+        optimizer.zero_grad()
+
+        # Forward pass
+        outputs = net(images)
+        loss = criterion(outputs, labels[:outputs.shape[0]])
+
+        # Backward pass and optimize
+        loss.backward()
+        optimizer.step()
+
+        running_loss += loss.item()
+    print(f'Epoch {epoch + 1}, Loss: {running_loss / len(trainloader)}')
+
+print('Finished Training')
+
+# Testing loop
+correct = 0
+total = 0
+with torch.no_grad():
+    for images, labels in testloader:
+        images, labels = images.to(device), labels.to(device)
+
+        # Ensure the batch sizes are consistent
+        if images.size(0) != labels.size(0):
+            raise ValueError(f"Batch size mismatch: images batch size = {images.size(0)}, labels batch size = {labels.size(0)}")
+
+        outputs = net(images)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+print(f'Accuracy of the network on the 10000 test images: {100 * correct // total}%')