import os import argparse import random import numpy as np import torch from torch import nn import torch.nn.functional as F import tqdm import fargan from dataset import FARGANDataset from stft_loss import * parser = argparse.ArgumentParser() parser.add_argument('features', type=str, help='path to feature file in .f32 format') parser.add_argument('signal', type=str, help='path to signal file in .s16 format') parser.add_argument('output', type=str, help='path to output folder') parser.add_argument('--suffix', type=str, help="model name suffix", default="") parser.add_argument('--cuda-visible-devices', type=str, help="comma separates list of cuda visible device indices, default: CUDA_VISIBLE_DEVICES", default=None) model_group = parser.add_argument_group(title="model parameters") model_group.add_argument('--cond-size', type=int, help="first conditioning size, default: 256", default=256) model_group.add_argument('--gamma', type=float, help="Use A(z/gamma), default: 0.9", default=0.9) training_group = parser.add_argument_group(title="training parameters") training_group.add_argument('--batch-size', type=int, help="batch size, default: 512", default=512) training_group.add_argument('--lr', type=float, help='learning rate, default: 1e-3', default=1e-3) training_group.add_argument('--epochs', type=int, help='number of training epochs, default: 20', default=20) training_group.add_argument('--sequence-length', type=int, help='sequence length, default: 15', default=15) training_group.add_argument('--lr-decay', type=float, help='learning rate decay factor, default: 1e-4', default=1e-4) training_group.add_argument('--initial-checkpoint', type=str, help='initial checkpoint to start training from, default: None', default=None) args = parser.parse_args() if args.cuda_visible_devices != None: os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_visible_devices # checkpoints checkpoint_dir = os.path.join(args.output, 'checkpoints') checkpoint = dict() os.makedirs(checkpoint_dir, exist_ok=True) # training parameters batch_size = args.batch_size lr = args.lr epochs = args.epochs sequence_length = args.sequence_length lr_decay = args.lr_decay adam_betas = [0.8, 0.95] adam_eps = 1e-8 features_file = args.features signal_file = args.signal # model parameters cond_size = args.cond_size checkpoint['batch_size'] = batch_size checkpoint['lr'] = lr checkpoint['lr_decay'] = lr_decay checkpoint['epochs'] = epochs checkpoint['sequence_length'] = sequence_length checkpoint['adam_betas'] = adam_betas device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") checkpoint['model_args'] = () checkpoint['model_kwargs'] = {'cond_size': cond_size, 'gamma': args.gamma} print(checkpoint['model_kwargs']) model = fargan.FARGAN(*checkpoint['model_args'], **checkpoint['model_kwargs']) #model = fargan.FARGAN() #model = nn.DataParallel(model) if type(args.initial_checkpoint) != type(None): checkpoint = torch.load(args.initial_checkpoint, map_location='cpu') model.load_state_dict(checkpoint['state_dict'], strict=False) checkpoint['state_dict'] = model.state_dict() dataset = FARGANDataset(features_file, signal_file, sequence_length=sequence_length) dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4) optimizer = torch.optim.AdamW(model.parameters(), lr=lr, betas=adam_betas, eps=adam_eps) # learning rate scheduler scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay * x)) states = None spect_loss = MultiResolutionSTFTLoss(device).to(device) if __name__ == '__main__': model.to(device) for epoch in range(1, epochs + 1): running_specc = 0 running_cont_loss = 0 running_loss = 0 print(f"training epoch {epoch}...") with tqdm.tqdm(dataloader, unit='batch') as tepoch: for i, (features, periods, target, lpc) in enumerate(tepoch): optimizer.zero_grad() features = features.to(device) #lpc = torch.tensor(fargan.interp_lpc(lpc.numpy(), 4)) #print("interp size", lpc.shape) #lpc = lpc.to(device) #lpc = lpc*(args.gamma**torch.arange(1,17, device=device)) #lpc = fargan.interp_lpc(lpc, 4) periods = periods.to(device) if (np.random.rand() > 0.1): target = target[:, :sequence_length*160] #lpc = lpc[:,:sequence_length*4,:] features = features[:,:sequence_length+4,:] periods = periods[:,:sequence_length+4] else: target=target[::2, :] #lpc=lpc[::2,:] features=features[::2,:] periods=periods[::2,:] target = target.to(device) #print(target.shape, lpc.shape) #target = fargan.analysis_filter(target, lpc[:,:,:], nb_subframes=1, gamma=args.gamma) #nb_pre = random.randrange(1, 6) nb_pre = 2 pre = target[:, :nb_pre*160] sig, states = model(features, periods, target.size(1)//160 - nb_pre, pre=pre, states=None) sig = torch.cat([pre, sig], -1) cont_loss = fargan.sig_loss(target[:, nb_pre*160:nb_pre*160+160], sig[:, nb_pre*160:nb_pre*160+160]) specc_loss = spect_loss(sig, target.detach()) loss = .03*cont_loss + specc_loss loss.backward() optimizer.step() #model.clip_weights() scheduler.step() running_specc += specc_loss.detach().cpu().item() running_cont_loss += cont_loss.detach().cpu().item() running_loss += loss.detach().cpu().item() tepoch.set_postfix(loss=f"{running_loss/(i+1):8.5f}", cont_loss=f"{running_cont_loss/(i+1):8.5f}", specc=f"{running_specc/(i+1):8.5f}", ) # save checkpoint checkpoint_path = os.path.join(checkpoint_dir, f'fargan{args.suffix}_{epoch}.pth') checkpoint['state_dict'] = model.state_dict() checkpoint['loss'] = running_loss / len(dataloader) checkpoint['epoch'] = epoch torch.save(checkpoint, checkpoint_path)