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import os
import time
import numpy as np
import torch
import librosa
from logger.saver import Saver
from logger import utils
def test(args, model, vocoder, loader_test, saver):
print(' [*] testing...')
model.eval()
# losses
test_loss = 0.
# intialization
num_batches = len(loader_test)
rtf_all = []
# run
with torch.no_grad():
for bidx, data in enumerate(loader_test):
fn = data['name'][0]
print('--------')
print('{}/{} - {}'.format(bidx, num_batches, fn))
# unpack data
for k in data.keys():
if k != 'name':
data[k] = data[k].to(args.device)
print('>>', data['name'][0])
# forward
st_time = time.time()
mel = model(
data['units'],
data['f0'],
data['volume'],
data['spk_id'],
gt_spec=None,
infer=True,
infer_speedup=args.infer.speedup,
method=args.infer.method)
signal = vocoder.infer(mel, data['f0'])
ed_time = time.time()
# RTF
run_time = ed_time - st_time
song_time = signal.shape[-1] / args.data.sampling_rate
rtf = run_time / song_time
print('RTF: {} | {} / {}'.format(rtf, run_time, song_time))
rtf_all.append(rtf)
# loss
for i in range(args.train.batch_size):
loss = model(
data['units'],
data['f0'],
data['volume'],
data['spk_id'],
gt_spec=data['mel'],
infer=False)
test_loss += loss.item()
# log mel
saver.log_spec(data['name'][0], data['mel'], mel)
# log audio
path_audio = os.path.join(args.data.valid_path, 'audio', data['name'][0]) + '.wav'
audio, sr = librosa.load(path_audio, sr=args.data.sampling_rate)
if len(audio.shape) > 1:
audio = librosa.to_mono(audio)
audio = torch.from_numpy(audio).unsqueeze(0).to(signal)
saver.log_audio({fn+'/gt.wav': audio, fn+'/pred.wav': signal})
# report
test_loss /= args.train.batch_size
test_loss /= num_batches
# check
print(' [test_loss] test_loss:', test_loss)
print(' Real Time Factor', np.mean(rtf_all))
return test_loss
def train(args, initial_global_step, model, optimizer, scheduler, vocoder, loader_train, loader_test):
# saver
saver = Saver(args, initial_global_step=initial_global_step)
# model size
params_count = utils.get_network_paras_amount({'model': model})
saver.log_info('--- model size ---')
saver.log_info(params_count)
# run
num_batches = len(loader_train)
model.train()
saver.log_info('======= start training =======')
for epoch in range(args.train.epochs):
for batch_idx, data in enumerate(loader_train):
saver.global_step_increment()
optimizer.zero_grad()
# unpack data
for k in data.keys():
if k != 'name':
data[k] = data[k].to(args.device)
# forward
loss = model(data['units'].float(), data['f0'], data['volume'], data['spk_id'],
aug_shift = data['aug_shift'], gt_spec=data['mel'].float(), infer=False)
# handle nan loss
if torch.isnan(loss):
raise ValueError(' [x] nan loss ')
else:
# backpropagate
loss.backward()
optimizer.step()
scheduler.step()
# log loss
if saver.global_step % args.train.interval_log == 0:
current_lr = optimizer.param_groups[0]['lr']
saver.log_info(
'epoch: {} | {:3d}/{:3d} | {} | batch/s: {:.2f} | lr: {:.6} | loss: {:.3f} | time: {} | step: {}'.format(
epoch,
batch_idx,
num_batches,
args.env.expdir,
args.train.interval_log/saver.get_interval_time(),
current_lr,
loss.item(),
saver.get_total_time(),
saver.global_step
)
)
saver.log_value({
'train/loss': loss.item()
})
saver.log_value({
'train/lr': current_lr
})
# validation
if saver.global_step % args.train.interval_val == 0:
# save latest
saver.save_model(model, optimizer, postfix=f'{saver.global_step}')
last_val_step = saver.global_step - args.train.interval_val
if last_val_step % args.train.interval_force_save != 0:
saver.delete_model(postfix=f'{last_val_step}')
# run testing set
test_loss = test(args, model, vocoder, loader_test, saver)
saver.log_info(
' --- <validation> --- \nloss: {:.3f}. '.format(
test_loss,
)
)
saver.log_value({
'validation/loss': test_loss
})
model.train()
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