coqui-tts-trainer

import torch import torch.nn as nn from torch.utils.data import DataLoader, Dataset from trainer.trainer import Trainer from trainer.generic_model_config import TrainerConfig # 1. Define a simple model class SimpleModel(nn.Module): def __init__(self): super().__init__() self.linear = nn.Linear(10, 2) def forward(self, x): return self.linear(x) # 2. Define a dummy dataset class DummyDataset(Dataset): def __len__(self): return 100 def __getitem__(self, idx): return torch.randn(10), torch.randint(0, 2, ()) # 3. Create a TrainerConfig config = TrainerConfig() config.num_epochs = 2 config.output_path = "./trainer_output" config.batch_size = 4 # 4. Instantiate model, optimizer, criterion, dataloaders model = SimpleModel() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.1) criterion = nn.CrossEntropyLoss() train_dataset = DummyDataset() eval_dataset = DummyDataset() train_loader = DataLoader(train_dataset, batch_size=config.batch_size) eval_loader = DataLoader(eval_dataset, batch_size=config.batch_size) # 5. Initialize and run the Trainer trainer = Trainer( config=config, model=model, optimizer=optimizer, scheduler=scheduler, criterion=criterion, data_loader_train=train_loader, data_loader_eval=eval_loader, grad_scaler=None, # For mixed precision, can be torch.cuda.amp.GradScaler() output_path=config.output_path ) trainer.train()