def __getattr__(self, name):

try:

return super().__getattr__(name)

except AttributeError:

config = yaml.safe_load(open(config_path))

log_dir = config['log_dir']

if not osp.exists(log_dir): os.makedirs(log_dir, exist_ok=True)

shutil.copy(config_path, osp.join(log_dir, osp.basename(config_path)))

writer = SummaryWriter(log_dir + "/tensorboard")

# write logs

file_handler = logging.FileHandler(osp.join(log_dir, 'train.log'))

file_handler.setLevel(logging.DEBUG)

file_handler.setFormatter(logging.Formatter('%(levelname)s:%(asctime)s: %(message)s'))

logger.addHandler(file_handler)

batch_size = config.get('batch_size', 10)

epochs = config.get('epochs_2nd', 200)

save_freq = config.get('save_freq', 2)

log_interval = config.get('log_interval', 10)

saving_epoch = config.get('save_freq', 2)

data_params = config.get('data_params', None)

sr = config['preprocess_params'].get('sr', 24000)

train_path = data_params['train_data']

val_path = data_params['val_data']

root_path = data_params['root_path']

min_length = data_params['min_length']

OOD_data = data_params['OOD_data']

max_len = config.get('max_len', 200)

loss_params = Munch(config['loss_params'])

diff_epoch = loss_params.diff_epoch

joint_epoch = loss_params.joint_epoch

optimizer_params = Munch(config['optimizer_params'])

train_list, val_list = get_data_path_list(train_path, val_path)

device = 'cuda'

train_dataloader = build_dataloader(train_list,

root_path,

OOD_data=OOD_data,

min_length=min_length,

batch_size=batch_size,

num_workers=2,

dataset_config={},

device=device)

val_dataloader = build_dataloader(val_list,

root_path,

OOD_data=OOD_data,

min_length=min_length,

batch_size=batch_size,

validation=True,

num_workers=0,

device=device,

dataset_config={})

# load pretrained ASR model

ASR_config = config.get('ASR_config', False)

ASR_path = config.get('ASR_path', False)

text_aligner = load_ASR_models(ASR_path, ASR_config)

# load pretrained F0 model

F0_path = config.get('F0_path', False)

pitch_extractor = load_F0_models(F0_path)

# load PL-BERT model

BERT_path = config.get('PLBERT_dir', False)

plbert = load_plbert(BERT_path)

# build model

model_params = recursive_munch(config['model_params'])

multispeaker = model_params.multispeaker

model = build_model(model_params, text_aligner, pitch_extractor, plbert)

_ = [model[key].to(device) for key in model]

# DP

for key in model:

if key != "mpd" and key != "msd" and key != "wd":

model[key] = MyDataParallel(model[key])

start_epoch = 0

iters = 0

load_pretrained = config.get('pretrained_model', '') != '' and config.get('second_stage_load_pretrained', False)

if not load_pretrained:

if config.get('first_stage_path', '') != '':

first_stage_path = osp.join(log_dir, config.get('first_stage_path', 'first_stage.pth'))

print('Loading the first stage model at %s ...' % first_stage_path)

model, _, start_epoch, iters = load_checkpoint(model,

None,

first_stage_path,

load_only_params=True,

ignore_modules=['bert', 'bert_encoder', 'predictor', 'predictor_encoder', 'msd', 'mpd', 'wd', 'diffusion']) # keep starting epoch for tensorboard log

# these epochs should be counted from the start epoch

diff_epoch += start_epoch

joint_epoch += start_epoch

epochs += start_epoch

model.predictor_encoder = copy.deepcopy(model.style_encoder)

else:

raise ValueError('You need to specify the path to the first stage model.')

gl = GeneratorLoss(model.mpd, model.msd).to(device)

dl = DiscriminatorLoss(model.mpd, model.msd).to(device)

wl = WavLMLoss(model_params.slm.model,

model.wd,

sr,

model_params.slm.sr).to(device)

gl = MyDataParallel(gl)

dl = MyDataParallel(dl)

wl = MyDataParallel(wl)

sampler = DiffusionSampler(

model.diffusion.diffusion,

sampler=ADPM2Sampler(),

sigma_schedule=KarrasSchedule(sigma_min=0.0001, sigma_max=3.0, rho=9.0), # empirical parameters

clamp=False

)

scheduler_params = {

"max_lr": optimizer_params.lr,

"pct_start": float(0),

"epochs": epochs,

"steps_per_epoch": len(train_dataloader),

}

scheduler_params_dict= {key: scheduler_params.copy() for key in model}

scheduler_params_dict['bert']['max_lr'] = optimizer_params.bert_lr * 2

scheduler_params_dict['decoder']['max_lr'] = optimizer_params.ft_lr * 2

scheduler_params_dict['style_encoder']['max_lr'] = optimizer_params.ft_lr * 2

optimizer = build_optimizer({key: model[key].parameters() for key in model},

scheduler_params_dict=scheduler_params_dict, lr=optimizer_params.lr)

# adjust BERT learning rate

for g in optimizer.optimizers['bert'].param_groups:

g['betas'] = (0.9, 0.99)

g['lr'] = optimizer_params.bert_lr

g['initial_lr'] = optimizer_params.bert_lr

g['min_lr'] = 0

g['weight_decay'] = 0.01

# adjust acoustic module learning rate

for module in ["decoder", "style_encoder"]:

for g in optimizer.optimizers[module].param_groups:

g['betas'] = (0.0, 0.99)

g['lr'] = optimizer_params.ft_lr

g['initial_lr'] = optimizer_params.ft_lr

g['min_lr'] = 0

g['weight_decay'] = 1e-4

# load models if there is a model

if load_pretrained:

model, optimizer, start_epoch, iters = load_checkpoint(model, optimizer, config['pretrained_model'],

load_only_params=config.get('load_only_params', True))

n_down = model.text_aligner.n_down

best_loss = float('inf') # best test loss

loss_train_record = list([])

loss_test_record = list([])

iters = 0

criterion = nn.L1Loss() # F0 loss (regression)

torch.cuda.empty_cache()

stft_loss = MultiResolutionSTFTLoss().to(device)

print('BERT', optimizer.optimizers['bert'])

print('decoder', optimizer.optimizers['decoder'])

start_ds = False

running_std = []

slmadv_params = Munch(config['slmadv_params'])

slmadv = SLMAdversarialLoss(model, wl, sampler,

slmadv_params.min_len,

slmadv_params.max_len,

batch_percentage=slmadv_params.batch_percentage,

skip_update=slmadv_params.iter,

sig=slmadv_params.sig

)

for epoch in range(start_epoch, epochs):

running_loss = 0

start_time = time.time()

_ = [model[key].eval() for key in model]

model.predictor.train()

model.bert_encoder.train()

model.bert.train()

model.msd.train()

model.mpd.train()

if epoch >= diff_epoch:

start_ds = True

for i, batch in enumerate(train_dataloader):

waves = batch[0]

batch = [b.to(device) for b in batch[1:]]

texts, input_lengths, ref_texts, ref_lengths, mels, mel_input_length, ref_mels = batch

with torch.no_grad():

mask = length_to_mask(mel_input_length // (2 ** n_down)).to(device)

mel_mask = length_to_mask(mel_input_length).to(device)

text_mask = length_to_mask(input_lengths).to(texts.device)

try:

_, _, s2s_attn = model.text_aligner(mels, mask, texts)

s2s_attn = s2s_attn.transpose(-1, -2)

s2s_attn = s2s_attn[..., 1:]

s2s_attn = s2s_attn.transpose(-1, -2)

except:

continue

mask_ST = mask_from_lens(s2s_attn, input_lengths, mel_input_length // (2 ** n_down))

s2s_attn_mono = maximum_path(s2s_attn, mask_ST)

# encode

t_en = model.text_encoder(texts, input_lengths, text_mask)

asr = (t_en @ s2s_attn_mono)

d_gt = s2s_attn_mono.sum(axis=-1).detach()

# compute reference styles

if multispeaker and epoch >= diff_epoch:

ref_ss = model.style_encoder(ref_mels.unsqueeze(1))

ref_sp = model.predictor_encoder(ref_mels.unsqueeze(1))

ref = torch.cat([ref_ss, ref_sp], dim=1)

# compute the style of the entire utterance

# this operation cannot be done in batch because of the avgpool layer (may need to work on masked avgpool)

ss = []

gs = []

for bib in range(len(mel_input_length)):

mel_length = int(mel_input_length[bib].item())

mel = mels[bib, :, :mel_input_length[bib]]

s = model.predictor_encoder(mel.unsqueeze(0).unsqueeze(1))

ss.append(s)

s = model.style_encoder(mel.unsqueeze(0).unsqueeze(1))

gs.append(s)

s_dur = torch.stack(ss).squeeze() # global prosodic styles

gs = torch.stack(gs).squeeze() # global acoustic styles

s_trg = torch.cat([gs, s_dur], dim=-1).detach() # ground truth for denoiser

bert_dur = model.bert(texts, attention_mask=(~text_mask).int())

d_en = model.bert_encoder(bert_dur).transpose(-1, -2)

# denoiser training

if epoch >= diff_epoch:

num_steps = np.random.randint(3, 5)

if model_params.diffusion.dist.estimate_sigma_data:

model.diffusion.module.diffusion.sigma_data = s_trg.std(axis=-1).mean().item() # batch-wise std estimation

running_std.append(model.diffusion.module.diffusion.sigma_data)

if multispeaker:

s_preds = sampler(noise = torch.randn_like(s_trg).unsqueeze(1).to(device),

embedding=bert_dur,

embedding_scale=1,

features=ref, # reference from the same speaker as the embedding

embedding_mask_proba=0.1,

num_steps=num_steps).squeeze(1)

loss_diff = model.diffusion(s_trg.unsqueeze(1), embedding=bert_dur, features=ref).mean() # EDM loss

loss_sty = F.l1_loss(s_preds, s_trg.detach()) # style reconstruction loss

else:

s_preds = sampler(noise = torch.randn_like(s_trg).unsqueeze(1).to(device),

embedding=bert_dur,

embedding_scale=1,

embedding_mask_proba=0.1,

num_steps=num_steps).squeeze(1)

loss_diff = model.diffusion.module.diffusion(s_trg.unsqueeze(1), embedding=bert_dur).mean() # EDM loss

loss_sty = F.l1_loss(s_preds, s_trg.detach()) # style reconstruction loss

else:

loss_sty = 0

loss_diff = 0

d, p = model.predictor(d_en, s_dur,

input_lengths,

s2s_attn_mono,

text_mask)

mel_len = min(int(mel_input_length.min().item() / 2 - 1), max_len // 2)

mel_len_st = int(mel_input_length.min().item() / 2 - 1)

en = []

gt = []

st = []

p_en = []

wav = []

for bib in range(len(mel_input_length)):

mel_length = int(mel_input_length[bib].item() / 2)

random_start = np.random.randint(0, mel_length - mel_len)

en.append(asr[bib, :, random_start:random_start+mel_len])

p_en.append(p[bib, :, random_start:random_start+mel_len])

gt.append(mels[bib, :, (random_start * 2):((random_start+mel_len) * 2)])

y = waves[bib][(random_start * 2) * 300:((random_start+mel_len) * 2) * 300]

wav.append(torch.from_numpy(y).to(device))

# style reference (better to be different from the GT)

random_start = np.random.randint(0, mel_length - mel_len_st)

st.append(mels[bib, :, (random_start * 2):((random_start+mel_len_st) * 2)])

wav = torch.stack(wav).float().detach()

en = torch.stack(en)

p_en = torch.stack(p_en)

gt = torch.stack(gt).detach()

st = torch.stack(st).detach()

if gt.size(-1) < 80:

continue

s_dur = model.predictor_encoder(st.unsqueeze(1) if multispeaker else gt.unsqueeze(1))

s = model.style_encoder(st.unsqueeze(1) if multispeaker else gt.unsqueeze(1))

with torch.no_grad():

F0_real, _, F0 = model.pitch_extractor(gt.unsqueeze(1))

F0 = F0.reshape(F0.shape[0], F0.shape[1] * 2, F0.shape[2], 1).squeeze()

asr_real = model.text_aligner.get_feature(gt)

N_real = log_norm(gt.unsqueeze(1)).squeeze(1)

y_rec_gt = wav.unsqueeze(1)

y_rec_gt_pred = model.decoder(en, F0_real, N_real, s)

if epoch >= joint_epoch:

# ground truth from recording

wav = y_rec_gt # use recording since decoder is tuned

else:

# ground truth from reconstruction

wav = y_rec_gt_pred # use reconstruction since decoder is fixed

F0_fake, N_fake = model.predictor.F0Ntrain(p_en, s_dur)

y_rec = model.decoder(en, F0_fake, N_fake, s)

loss_F0_rec = (F.smooth_l1_loss(F0_real, F0_fake)) / 10

loss_norm_rec = F.smooth_l1_loss(N_real, N_fake)

if start_ds:

optimizer.zero_grad()

d_loss = dl(wav.detach(), y_rec.detach()).mean()

d_loss.backward()

optimizer.step('msd')

optimizer.step('mpd')

else:

d_loss = 0

# generator loss

optimizer.zero_grad()

loss_mel = stft_loss(y_rec, wav)

if start_ds:

loss_gen_all = gl(wav, y_rec).mean()

else:

loss_gen_all = 0

loss_lm = wl(wav.detach().squeeze(), y_rec.squeeze()).mean()

loss_ce = 0

loss_dur = 0

for _s2s_pred, _text_input, _text_length in zip(d, (d_gt), input_lengths):

_s2s_pred = _s2s_pred[:_text_length, :]

_text_input = _text_input[:_text_length].long()

_s2s_trg = torch.zeros_like(_s2s_pred)

for p in range(_s2s_trg.shape[0]):

_s2s_trg[p, :_text_input[p]] = 1

_dur_pred = torch.sigmoid(_s2s_pred).sum(axis=1)

loss_dur += F.l1_loss(_dur_pred[1:_text_length-1],

_text_input[1:_text_length-1])

loss_ce += F.binary_cross_entropy_with_logits(_s2s_pred.flatten(), _s2s_trg.flatten())

loss_ce /= texts.size(0)

loss_dur /= texts.size(0)

g_loss = loss_params.lambda_mel * loss_mel + \

loss_params.lambda_F0 * loss_F0_rec + \

loss_params.lambda_ce * loss_ce + \

loss_params.lambda_norm * loss_norm_rec + \

loss_params.lambda_dur * loss_dur + \

loss_params.lambda_gen * loss_gen_all + \

loss_params.lambda_slm * loss_lm + \

loss_params.lambda_sty * loss_sty + \

loss_params.lambda_diff * loss_diff

running_loss += loss_mel.item()

g_loss.backward()

if torch.isnan(g_loss):

from IPython.core.debugger import set_trace

set_trace()

optimizer.step('bert_encoder')

optimizer.step('bert')

optimizer.step('predictor')

optimizer.step('predictor_encoder')

if epoch >= diff_epoch:

optimizer.step('diffusion')

if epoch >= joint_epoch:

optimizer.step('style_encoder')

optimizer.step('decoder')

# randomly pick whether to use in-distribution text

if np.random.rand() < 0.5:

use_ind = True

else:

use_ind = False

if use_ind:

ref_lengths = input_lengths

ref_texts = texts

slm_out = slmadv(i,

y_rec_gt,

y_rec_gt_pred,

waves,

mel_input_length,

ref_texts,

ref_lengths, use_ind, s_trg.detach(), ref if multispeaker else None)

if slm_out is None:

continue

d_loss_slm, loss_gen_lm, y_pred = slm_out

# SLM generator loss

optimizer.zero_grad()

loss_gen_lm.backward()

# compute the gradient norm

total_norm = {}

for key in model.keys():

total_norm[key] = 0

parameters = [p for p in model[key].parameters() if p.grad is not None and p.requires_grad]

for p in parameters:

param_norm = p.grad.detach().data.norm(2)

total_norm[key] += param_norm.item() ** 2

total_norm[key] = total_norm[key] ** 0.5

# gradient scaling

if total_norm['predictor'] > slmadv_params.thresh:

for key in model.keys():

for p in model[key].parameters():

if p.grad is not None:

p.grad *= (1 / total_norm['predictor'])

for p in model.predictor.duration_proj.parameters():

if p.grad is not None:

p.grad *= slmadv_params.scale

for p in model.predictor.lstm.parameters():

if p.grad is not None:

p.grad *= slmadv_params.scale

for p in model.diffusion.parameters():

if p.grad is not None:

p.grad *= slmadv_params.scale

optimizer.step('bert_encoder')

optimizer.step('bert')

optimizer.step('predictor')

optimizer.step('diffusion')

# SLM discriminator loss

if d_loss_slm != 0:

optimizer.zero_grad()

d_loss_slm.backward(retain_graph=True)

optimizer.step('wd')

else:

d_loss_slm, loss_gen_lm = 0, 0

iters = iters + 1

if (i+1)%log_interval == 0:

logger.info ('Epoch [%d/%d], Step [%d/%d], Loss: %.5f, Disc Loss: %.5f, Dur Loss: %.5f, CE Loss: %.5f, Norm Loss: %.5f, F0 Loss: %.5f, LM Loss: %.5f, Gen Loss: %.5f, Sty Loss: %.5f, Diff Loss: %.5f, DiscLM Loss: %.5f, GenLM Loss: %.5f'

%(epoch+1, epochs, i+1, len(train_list)//batch_size, running_loss / log_interval, d_loss, loss_dur, loss_ce, loss_norm_rec, loss_F0_rec, loss_lm, loss_gen_all, loss_sty, loss_diff, d_loss_slm, loss_gen_lm))

writer.add_scalar('train/mel_loss', running_loss / log_interval, iters)

writer.add_scalar('train/gen_loss', loss_gen_all, iters)

writer.add_scalar('train/d_loss', d_loss, iters)

writer.add_scalar('train/ce_loss', loss_ce, iters)

writer.add_scalar('train/dur_loss', loss_dur, iters)

writer.add_scalar('train/slm_loss', loss_lm, iters)

writer.add_scalar('train/norm_loss', loss_norm_rec, iters)

writer.add_scalar('train/F0_loss', loss_F0_rec, iters)

writer.add_scalar('train/sty_loss', loss_sty, iters)

writer.add_scalar('train/diff_loss', loss_diff, iters)

writer.add_scalar('train/d_loss_slm', d_loss_slm, iters)

writer.add_scalar('train/gen_loss_slm', loss_gen_lm, iters)

running_loss = 0

print('Time elasped:', time.time()-start_time)

loss_test = 0

loss_align = 0

loss_f = 0

_ = [model[key].eval() for key in model]

with torch.no_grad():

iters_test = 0

for batch_idx, batch in enumerate(val_dataloader):

optimizer.zero_grad()

try:

waves = batch[0]

batch = [b.to(device) for b in batch[1:]]

texts, input_lengths, ref_texts, ref_lengths, mels, mel_input_length, ref_mels = batch

with torch.no_grad():

mask = length_to_mask(mel_input_length // (2 ** n_down)).to('cuda')

text_mask = length_to_mask(input_lengths).to(texts.device)

_, _, s2s_attn = model.text_aligner(mels, mask, texts)

s2s_attn = s2s_attn.transpose(-1, -2)

s2s_attn = s2s_attn[..., 1:]

s2s_attn = s2s_attn.transpose(-1, -2)

mask_ST = mask_from_lens(s2s_attn, input_lengths, mel_input_length // (2 ** n_down))

s2s_attn_mono = maximum_path(s2s_attn, mask_ST)

# encode

t_en = model.text_encoder(texts, input_lengths, text_mask)

asr = (t_en @ s2s_attn_mono)

d_gt = s2s_attn_mono.sum(axis=-1).detach()

ss = []

gs = []

for bib in range(len(mel_input_length)):

mel_length = int(mel_input_length[bib].item())

mel = mels[bib, :, :mel_input_length[bib]]

s = model.predictor_encoder(mel.unsqueeze(0).unsqueeze(1))

ss.append(s)

s = model.style_encoder(mel.unsqueeze(0).unsqueeze(1))

gs.append(s)

s = torch.stack(ss).squeeze()

gs = torch.stack(gs).squeeze()

s_trg = torch.cat([s, gs], dim=-1).detach()

bert_dur = model.bert(texts, attention_mask=(~text_mask).int())

d_en = model.bert_encoder(bert_dur).transpose(-1, -2)

d, p = model.predictor(d_en, s,

input_lengths,

s2s_attn_mono,

text_mask)

# get clips

mel_len = int(mel_input_length.min().item() / 2 - 1)

en = []

gt = []

p_en = []

wav = []

for bib in range(len(mel_input_length)):

mel_length = int(mel_input_length[bib].item() / 2)

random_start = np.random.randint(0, mel_length - mel_len)

en.append(asr[bib, :, random_start:random_start+mel_len])

p_en.append(p[bib, :, random_start:random_start+mel_len])

gt.append(mels[bib, :, (random_start * 2):((random_start+mel_len) * 2)])

y = waves[bib][(random_start * 2) * 300:((random_start+mel_len) * 2) * 300]

wav.append(torch.from_numpy(y).to(device))

wav = torch.stack(wav).float().detach()

en = torch.stack(en)

p_en = torch.stack(p_en)

gt = torch.stack(gt).detach()

s = model.predictor_encoder(gt.unsqueeze(1))

F0_fake, N_fake = model.predictor.F0Ntrain(p_en, s)

loss_dur = 0

for _s2s_pred, _text_input, _text_length in zip(d, (d_gt), input_lengths):

_s2s_pred = _s2s_pred[:_text_length, :]

_text_input = _text_input[:_text_length].long()

_s2s_trg = torch.zeros_like(_s2s_pred)

for bib in range(_s2s_trg.shape[0]):

_s2s_trg[bib, :_text_input[bib]] = 1

_dur_pred = torch.sigmoid(_s2s_pred).sum(axis=1)

loss_dur += F.l1_loss(_dur_pred[1:_text_length-1],

_text_input[1:_text_length-1])

loss_dur /= texts.size(0)

s = model.style_encoder(gt.unsqueeze(1))

y_rec = model.decoder(en, F0_fake, N_fake, s)

loss_mel = stft_loss(y_rec.squeeze(), wav.detach())

F0_real, _, F0 = model.pitch_extractor(gt.unsqueeze(1))

loss_F0 = F.l1_loss(F0_real, F0_fake) / 10

loss_test += (loss_mel).mean()

loss_align += (loss_dur).mean()

loss_f += (loss_F0).mean()

iters_test += 1

except:

continue

print('Epochs:', epoch + 1)

logger.info('Validation loss: %.3f, Dur loss: %.3f, F0 loss: %.3f' % (loss_test / iters_test, loss_align / iters_test, loss_f / iters_test) + '\n\n\n')

print('\n\n\n')

writer.add_scalar('eval/mel_loss', loss_test / iters_test, epoch + 1)

writer.add_scalar('eval/dur_loss', loss_test / iters_test, epoch + 1)

writer.add_scalar('eval/F0_loss', loss_f / iters_test, epoch + 1)

if epoch < joint_epoch:

# generating reconstruction examples with GT duration

with torch.no_grad():

for bib in range(len(asr)):

mel_length = int(mel_input_length[bib].item())

gt = mels[bib, :, :mel_length].unsqueeze(0)

en = asr[bib, :, :mel_length // 2].unsqueeze(0)

F0_real, _, _ = model.pitch_extractor(gt.unsqueeze(1))

F0_real = F0_real.unsqueeze(0)

s = model.style_encoder(gt.unsqueeze(1))

real_norm = log_norm(gt.unsqueeze(1)).squeeze(1)

y_rec = model.decoder(en, F0_real, real_norm, s)

writer.add_audio('eval/y' + str(bib), y_rec.cpu().numpy().squeeze(), epoch, sample_rate=sr)

s_dur = model.predictor_encoder(gt.unsqueeze(1))

p_en = p[bib, :, :mel_length // 2].unsqueeze(0)

F0_fake, N_fake = model.predictor.F0Ntrain(p_en, s_dur)

y_pred = model.decoder(en, F0_fake, N_fake, s)

writer.add_audio('pred/y' + str(bib), y_pred.cpu().numpy().squeeze(), epoch, sample_rate=sr)

if epoch == 0:

writer.add_audio('gt/y' + str(bib), waves[bib].squeeze(), epoch, sample_rate=sr)

if bib >= 5:

break

else:

# generating sampled speech from text directly

with torch.no_grad():

# compute reference styles

if multispeaker and epoch >= diff_epoch:

ref_ss = model.style_encoder(ref_mels.unsqueeze(1))

ref_sp = model.predictor_encoder(ref_mels.unsqueeze(1))

ref_s = torch.cat([ref_ss, ref_sp], dim=1)

for bib in range(len(d_en)):

if multispeaker:

s_pred = sampler(noise = torch.randn((1, 256)).unsqueeze(1).to(texts.device),

embedding=bert_dur[bib].unsqueeze(0),

embedding_scale=1,

features=ref_s[bib].unsqueeze(0), # reference from the same speaker as the embedding

num_steps=5).squeeze(1)

else:

s_pred = sampler(noise = torch.randn((1, 256)).unsqueeze(1).to(texts.device),

embedding=bert_dur[bib].unsqueeze(0),

embedding_scale=1,

num_steps=5).squeeze(1)

s = s_pred[:, 128:]

ref = s_pred[:, :128]

d = model.predictor.text_encoder(d_en[bib, :, :input_lengths[bib]].unsqueeze(0),

s, input_lengths[bib, ...].unsqueeze(0), text_mask[bib, :input_lengths[bib]].unsqueeze(0))

x, _ = model.predictor.lstm(d)

duration = model.predictor.duration_proj(x)

duration = torch.sigmoid(duration).sum(axis=-1)

pred_dur = torch.round(duration.squeeze()).clamp(min=1)

pred_dur[-1] += 5

pred_aln_trg = torch.zeros(input_lengths[bib], int(pred_dur.sum().data))

c_frame = 0

for i in range(pred_aln_trg.size(0)):

pred_aln_trg[i, c_frame:c_frame + int(pred_dur[i].data)] = 1

c_frame += int(pred_dur[i].data)

# encode prosody

en = (d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(texts.device))

F0_pred, N_pred = model.predictor.F0Ntrain(en, s)

out = model.decoder((t_en[bib, :, :input_lengths[bib]].unsqueeze(0) @ pred_aln_trg.unsqueeze(0).to(texts.device)),

F0_pred, N_pred, ref.squeeze().unsqueeze(0))

writer.add_audio('pred/y' + str(bib), out.cpu().numpy().squeeze(), epoch, sample_rate=sr)

if bib >= 5:

break

if epoch % saving_epoch == 0:

if (loss_test / iters_test) < best_loss:

best_loss = loss_test / iters_test

print('Saving..')

state = {

'net': {key: model[key].state_dict() for key in model},

'optimizer': optimizer.state_dict(),

'iters': iters,

'val_loss': loss_test / iters_test,

'epoch': epoch,

}

save_path = osp.join(log_dir, 'epoch_2nd_%05d.pth' % epoch)

torch.save(state, save_path)

# if estimate sigma, save the estimated simga

if model_params.diffusion.dist.estimate_sigma_data:

config['model_params']['diffusion']['dist']['sigma_data'] = float(np.mean(running_std))

with open(osp.join(log_dir, osp.basename(config_path)), 'w') as outfile: