Historical Note

This page was migrated from the original p-nand-q.com site which was last updated in 2015. The content has been preserved exactly as it was, with only formatting updated for modern browsers. Over the coming days and weeks, the content will be reviewed and may be updated for accuracy and relevance. If you find any issues, please contact me.

Text To Speech Khmer Apr 2026

# Create data loader dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

Here's an example code snippet in Python using the Tacotron 2 model and the Khmer dataset: text to speech khmer

# Load Khmer dataset dataset = KhmerDataset('path/to/khmer/dataset') text to speech khmer

# Evaluate the model model.eval() test_loss = 0 with torch.no_grad(): for batch in test_dataloader: text, audio = batch text = text.to(device) audio = audio.to(device) loss = model(text, audio) test_loss += loss.item() print(f'Test Loss: {test_loss / len(test_dataloader)}') Note that this is a highly simplified example and in practice, you will need to handle many more complexities such as data preprocessing, model customization, and hyperparameter tuning. text to speech khmer

The feature will be called "Khmer Voice Assistant" and will allow users to input Khmer text and receive an audio output of the text being read.

# Train the model for epoch in range(100): for batch in dataloader: text, audio = batch text = text.to(device) audio = audio.to(device) loss = model(text, audio) loss.backward() optimizer.step() print(f'Epoch {epoch+1}, Loss: {loss.item()}')