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trainer.py
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188 lines (167 loc) · 7.56 KB
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import pathlib
import torch
import tqdm
from utils import Trainer
class UniWaveNetTrainer(Trainer):
def __init__(
self, train_data_loader, valid_data_loader, train_writer,
valid_writer, valid_iteration, save_iteration, device, encoder,
wavenet, optimizer, loss_weights, scale, loss_threshold, sr,
output_dir, gradient_threshold):
super(UniWaveNetTrainer, self).__init__(
train_data_loader, valid_data_loader, train_writer, valid_writer,
valid_iteration, save_iteration, device)
self.encoder = encoder
self.wavenet = wavenet
self.optimizer = optimizer
self.loss_weights = loss_weights
self.scale = scale
self.loss_threshold = loss_threshold
self.sr = sr
self.output_dir = output_dir
self.gradient_threshold = gradient_threshold
def _train_one_iteration(self, iteration):
batch = next(iter(self.train_data_loader))
t, spectrogram = batch[0].to(self.device), batch[1].to(self.device)
conditions = self.encoder(spectrogram)
xs = self.wavenet(conditions, return_all=True)
magnitude_loss = 0
power_loss = 0
log_loss = 0
for x in xs:
magnitude_loss += calc_spectrogram_loss(
x, t, 'magnitude', self.loss_weights, self.loss_threshold)
power_loss += calc_spectrogram_loss(
x, t, 'power', self.loss_weights, self.loss_threshold)
log_loss += calc_spectrogram_loss(
x, t, 'log', self.loss_weights, self.loss_threshold)
if self.scale == 'magnitude':
loss = magnitude_loss
elif self.scale == 'power':
loss = power_loss
else:
loss = log_loss
self.train_writer.add_audio(
'audio/generated', torch.clamp(xs[-1][0], -1, 1), iteration,
sample_rate=self.sr)
self.train_writer.add_audio(
'audio/grandtruth', torch.clamp(t[0], -1, 1), iteration,
sample_rate=self.sr)
self.optimizer.zero_grad()
loss.backward()
if self.gradient_threshold is not None:
torch.nn.utils.clip_grad_norm_(
self.wavenet.parameters(), self.gradient_threshold)
self.optimizer.step()
self.train_writer.add_scalar(
'magnitude_loss', magnitude_loss.item(), iteration)
self.train_writer.add_scalar(
'power_loss', power_loss.item(), iteration)
self.train_writer.add_scalar(
'log_loss', log_loss.item(), iteration)
self.train_writer.add_scalar(
'loss', loss.item(), iteration)
def _valid(self, iteration):
avg_magnitude_loss = 0
avg_power_loss = 0
avg_log_loss = 0
avg_loss = 0
with torch.no_grad():
for batch in tqdm.tqdm(self.valid_data_loader):
t, spectrogram = \
batch[0].to(self.device), batch[1].to(self.device)
conditions = self.encoder(spectrogram)
xs = self.wavenet(conditions, return_all=True)
magnitude_loss = 0
power_loss = 0
log_loss = 0
for x in xs:
magnitude_loss += calc_spectrogram_loss(
x, t, 'magnitude', self.loss_weights,
self.loss_threshold)
power_loss += calc_spectrogram_loss(
x, t, 'power', self.loss_weights,
self.loss_threshold)
log_loss += calc_spectrogram_loss(
x, t, 'log', self.loss_weights,
self.loss_threshold)
if self.scale == 'magnitude':
loss = magnitude_loss
elif self.scale == 'power':
loss = power_loss
else:
loss = log_loss
avg_magnitude_loss += magnitude_loss.item()
avg_power_loss += power_loss.item()
avg_log_loss += log_loss.item()
avg_loss += loss.item()
self.valid_writer.add_audio(
'audio/generated', torch.clamp(xs[-1][0], -1, 1),
iteration, sample_rate=self.sr)
self.valid_writer.add_audio(
'audio/grandtruth', torch.clamp(t[0], -1, 1),
iteration, sample_rate=self.sr)
self.valid_writer.add_scalar(
'magnitude_loss', avg_magnitude_loss / len(self.valid_data_loader),
iteration)
self.valid_writer.add_scalar(
'power_loss', avg_power_loss / len(self.valid_data_loader),
iteration)
self.valid_writer.add_scalar(
'log_loss', avg_log_loss / len(self.valid_data_loader), iteration)
self.valid_writer.add_scalar(
'loss', avg_loss / len(self.valid_data_loader), iteration)
def _save_checkpoints(self, iteration):
def _save_checkpoint(model, model_name):
model_out_path = '{}_iteration_{}.pth'.format(
model_name, iteration)
torch.save(model.state_dict(), model_out_path)
print('Checkpoint is saved to {}'.format(model_out_path))
_save_checkpoint(
self.wavenet, str(pathlib.Path(self.output_dir, 'wavenet')))
_save_checkpoint(
self.encoder, str(pathlib.Path(self.output_dir, 'encoder')))
_save_checkpoint(
self.optimizer, str(pathlib.Path(self.output_dir, 'optimizer')))
def load_trained_encoder(self, model_path):
if model_path is not None:
self.encoder.load_state_dict(torch.load(model_path))
def load_trained_wavenet(self, model_path):
if model_path is not None:
self.wavenet.load_state_dict(torch.load(model_path))
def load_optimizer_state(self, optimizer_path):
if optimizer_path is not None:
self.optimizer.load_state_dict(torch.load(optimizer_path))
def calc_spectrograms(signal, scale):
signal = torch.squeeze(signal, dim=1)
spectrograms = []
for n_fft in [32, 64, 128, 256, 512, 1024, 2048, 4096]:
hop_length = n_fft // 4
win_length = n_fft // 2
if torch.cuda.is_available():
window = torch.hann_window(win_length).cuda()
else:
window = torch.hann_window(win_length)
complex_spectrogram = torch.stft(
signal, n_fft, hop_length, win_length, window)
# [minibatch, frame, frequency, real/imaginary]
power_spectrogram = (
complex_spectrogram[:, :, :, 0] * complex_spectrogram[:, :, :, 0] +
complex_spectrogram[:, :, :, 1] * complex_spectrogram[:, :, :, 1])
if scale == 'power':
spectrograms.append(power_spectrogram)
elif scale == 'magnitude':
spectrograms.append(torch.sqrt(power_spectrogram + 1e-10))
elif scale == 'log':
spectrograms.append(torch.log(power_spectrogram + 1e-10))
else:
print('error')
return spectrograms
def calc_spectrogram_loss(x, t, scale, weights, loss_threshold=100):
x_specs = calc_spectrograms(x, scale)
t_specs = calc_spectrograms(t, scale)
loss = 0
for x_spec, t_spec, weight in zip(x_specs, t_specs, weights):
loss += weight * torch.mean(
torch.clamp(torch.abs(x_spec - t_spec), max=loss_threshold))
return loss