mirror of
https://github.com/xiph/opus.git
synced 2025-05-15 16:08:30 +00:00
145 lines
5.3 KiB
Python
145 lines
5.3 KiB
Python
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 plc
|
|
from plc_dataset import PLCDataset
|
|
|
|
parser = argparse.ArgumentParser()
|
|
|
|
parser.add_argument('features', type=str, help='path to feature file in .f32 format')
|
|
parser.add_argument('loss', type=str, help='path to signal file in .s8 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: 128", default=128)
|
|
model_group.add_argument('--gru-size', type=int, help="GRU size, default: 128", default=128)
|
|
|
|
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
|
|
loss_file = args.loss
|
|
|
|
# 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, 'gru_size': args.gru_size}
|
|
print(checkpoint['model_kwargs'])
|
|
model = plc.PLC(*checkpoint['model_args'], **checkpoint['model_kwargs'])
|
|
|
|
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 = PLCDataset(features_file, loss_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
|
|
|
|
plc_loss = plc.plc_loss(18, device=device)
|
|
if __name__ == '__main__':
|
|
model.to(device)
|
|
|
|
for epoch in range(1, epochs + 1):
|
|
|
|
running_loss = 0
|
|
running_l1_loss = 0
|
|
running_ceps_loss = 0
|
|
running_band_loss = 0
|
|
running_pitch_loss = 0
|
|
|
|
print(f"training epoch {epoch}...")
|
|
with tqdm.tqdm(dataloader, unit='batch') as tepoch:
|
|
for i, (features, lost, target) in enumerate(tepoch):
|
|
optimizer.zero_grad()
|
|
features = features.to(device)
|
|
lost = lost.to(device)
|
|
target = target.to(device)
|
|
|
|
out, states = model(features, lost)
|
|
|
|
loss, l1_loss, ceps_loss, band_loss, pitch_loss = plc_loss(target, out)
|
|
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
#model.clip_weights()
|
|
|
|
scheduler.step()
|
|
|
|
running_loss += loss.detach().cpu().item()
|
|
running_l1_loss += l1_loss.detach().cpu().item()
|
|
running_ceps_loss += ceps_loss.detach().cpu().item()
|
|
running_band_loss += band_loss.detach().cpu().item()
|
|
running_pitch_loss += pitch_loss.detach().cpu().item()
|
|
tepoch.set_postfix(loss=f"{running_loss/(i+1):8.5f}",
|
|
l1_loss=f"{running_l1_loss/(i+1):8.5f}",
|
|
ceps_loss=f"{running_ceps_loss/(i+1):8.5f}",
|
|
band_loss=f"{running_band_loss/(i+1):8.5f}",
|
|
pitch_loss=f"{running_pitch_loss/(i+1):8.5f}",
|
|
)
|
|
|
|
# save checkpoint
|
|
checkpoint_path = os.path.join(checkpoint_dir, f'plc{args.suffix}_{epoch}.pth')
|
|
checkpoint['state_dict'] = model.state_dict()
|
|
checkpoint['loss'] = running_loss / len(dataloader)
|
|
checkpoint['epoch'] = epoch
|
|
torch.save(checkpoint, checkpoint_path)
|