```
import torch
import torch.nn as nn
import torch.optim as optim

class ModulationNN(nn.Module):
    def __init__(self, num_symbols=256, embedding_dim=64, hidden_dim=128, num_subcarriers=64):
        super(ModulationNN, self).__init__()
        self.num_symbols = num_symbols
        self.num_subcarriers = num_subcarriers

        # Encoder
        self.embedding = nn.Embedding(num_symbols, embedding_dim)
        self.encoder_fc = nn.Sequential(
            nn.Linear(embedding_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, 2 * num_subcarriers)  # 2 for real and imaginary parts
        )

        # Decoder
        self.decoder_fc = nn.Sequential(
            nn.Linear(2 * num_subcarriers, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, num_symbols)
        )

    def encoder(self, x):
        x = self.embedding(x)
        x = self.encoder_fc(x)
        x = x.view(-1, self.num_subcarriers, 2)
        x = torch.complex(x[:, :, 0], x[:, :, 1])
        return x

    def apply_ofdm(self, x):
        return torch.fft.ifft(x, dim=1)

    def remove_ofdm(self, x):
        return torch.fft.fft(x, dim=1)

    def channel_simulator(self, x, snr_db=20):
        # AWGN channel
        signal_power = torch.mean(torch.abs(x) ** 2)
        noise_power = signal_power / (10 ** (snr_db / 10))
        noise = torch.sqrt(noise_power / 2) * (torch.randn_like(x) + 1j * torch.randn_like(x))
        return x + noise

    def decoder(self, x):
        x = torch.cat([x.real, x.imag], dim=-1)
        x = self.decoder_fc(x)
        return x

    def forward(self, x):
        x = self.encoder(x)
        x = self.apply_ofdm(x)
        x = self.channel_simulator(x)
        x = self.remove_ofdm(x)
        x = self.decoder(x)
        return x

# Training function
def train(model, num_epochs=100, batch_size=64):
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters())

    for epoch in range(num_epochs):
        total_loss = 0
        for _ in range(100):  # 100 batches per epoch
            inputs = torch.randint(0, model.num_symbols, (batch_size,))
            outputs = model(inputs)
            loss = criterion(outputs, inputs)

            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            total_loss += loss.item()

        print(f"Epoch {epoch + 1}/{num_epochs}, Loss: {total_loss / 100:.4f}")

# Example usage
model = ModulationNN()
train(model)

# Test the model
test_input = torch.randint(0, model.num_symbols, (10,))
with torch.no_grad():
    output = model(test_input)
    predicted = torch.argmax(output, dim=1)
print("Input:", test_input)
print("Predicted:", predicted)
```

Now, I need to revise my requirements:

* The model should be able to modulate data on top of an existing 8 kHz bandwidth audio signal
* The audio signal should not be noticeably distorted.
* This will probably require the use of some sort of perceptual loss.
* Save an audio sample or two after each epoch for monitoring.
* Aim for a symbol rate of 1000 symbols/sec (or 8 samples/symbol, considering the bandwidth)
* No separate decoding network should be needed to restore the audio

(Allow providing a folder full of audio files of various types for training : "./audio", on top of the random symbols. Use `librosa.load(..., sr=...)` to correct sample rate, and note the audio files will not be pre-sliced, so please do that.)