# load packages
import random
import yaml
import time
from munch import Munch
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import torchaudio
import librosa
import click
import shutil
import traceback
import warnings
warnings.simplefilter('ignore')
from torch.utils.tensorboard import SummaryWriter
from meldataset import build_dataloader
from Utils.ASR.models import ASRCNN
from Utils.JDC.model import JDCNet
from Utils.PLBERT.util import load_plbert
from models import *
from losses import *
from utils import *
from Modules.slmadv import SLMAdversarialLoss
from Modules.diffusion.sampler import DiffusionSampler, ADPM2Sampler, KarrasSchedule
from optimizers import build_optimizer
# simple fix for dataparallel that allows access to class attributes
class MyDataParallel(torch.nn.DataParallel):
def __getattr__(self, name):
try:
return super().__getattr__(name)
except AttributeError:
return getattr(self.module, name)
import logging
from logging import StreamHandler
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
handler = StreamHandler()
handler.setLevel(logging.DEBUG)
logger.addHandler(handler)
@click.command()
@click.option('-p', '--config_path', default='Configs/config.yml', type=str)
def main(config_path):
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 Exception as e:
print(f"run into exception", e)
traceback.print_exc()
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_align / 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:
yaml.dump(config, outfile, default_flow_style=True)
if __name__=="__main__":
main()