def evaluate(self, model: ForwardTacotron,
val_set: DataLoader) -> Dict[str, float]:
model.eval()
m_val_loss = 0
dur_val_loss = 0
pitch_val_loss = 0
energy_val_loss = 0
device = next(model.parameters()).device
for i, batch in enumerate(val_set, 1):
batch = to_device(batch, device=device)
with torch.no_grad():
pred = model(batch)
m1_loss = self.l1_loss(pred['mel'], batch['mel'],
batch['mel_len'])
m2_loss = self.l1_loss(pred['mel_post'], batch['mel'],
batch['mel_len'])
dur_loss = self.l1_loss(pred['dur'].unsqueeze(1),
batch['dur'].unsqueeze(1),
batch['x_len'])
pitch_loss = self.l1_loss(pred['pitch'],
batch['pitch'].unsqueeze(1),
batch['x_len'])
energy_loss = self.l1_loss(pred['energy'],
batch['energy'].unsqueeze(1),
batch['x_len'])
pitch_val_loss += pitch_loss
energy_val_loss += energy_loss
m_val_loss += m1_loss.item() + m2_loss.item()
dur_val_loss += dur_loss.item()
return {
'mel_loss': m_val_loss / len(val_set),
'dur_loss': dur_val_loss / len(val_set),
'pitch_loss': pitch_val_loss / len(val_set),
'energy_loss': energy_val_loss / len(val_set)
}
def evaluate(self, model: ForwardTacotron, val_set: Dataset) -> Tuple[float, float,float]:
model.eval()
m_val_loss = 0
dur_val_loss = 0
pitch_val_loss = 0
device = next(model.parameters()).device
for i, (x, m, ids, x_lens, mel_lens, dur, pitch, puncts) in enumerate(
val_set, 1
):
x, m, dur, x_lens, mel_lens, pitch, puncts = (
x.to(device),
m.to(device),
dur.to(device),
x_lens.to(device),
mel_lens.to(device),
pitch.to(device),
puncts.to(device),
)
with torch.no_grad():
m1_hat, m2_hat, dur_hat, pitch_hat = model(
x, m, dur, mel_lens, pitch, puncts
)
m1_loss = self.l1_loss(m1_hat, m, mel_lens)
m2_loss = self.l1_loss(m2_hat, m, mel_lens)
dur_loss = self.l1_loss(dur_hat.unsqueeze(1), dur.unsqueeze(1), x_lens)
pitch_val_loss += self.l1_loss(pitch_hat, pitch.unsqueeze(1), x_lens)
m_val_loss += m1_loss.item() + m2_loss.item()
dur_val_loss += dur_loss.item()
m_val_loss /= len(val_set)
dur_val_loss /= len(val_set)
pitch_val_loss /= len(val_set)
return m_val_loss, dur_val_loss, pitch_val_loss
def generate_plots(self, model: ForwardTacotron,
session: ForwardSession) -> None:
model.eval()
device = next(model.parameters()).device
x, m, ids, x_lens, mel_lens, dur = session.val_sample
x, m, dur, mel_lens = x.to(device), m.to(device), dur.to(
device), mel_lens.to(device)
m1_hat, m2_hat, dur_hat = model(x, m, dur, mel_lens)
m1_hat = np_now(m1_hat)[0, :600, :]
m2_hat = np_now(m2_hat)[0, :600, :]
m = np_now(m)[0, :600, :]
m1_hat_fig = plot_mel(m1_hat)
m2_hat_fig = plot_mel(m2_hat)
m_fig = plot_mel(m)
# pitch_fig = plot_pitch(np_now(pitch[0]))
# pitch_gta_fig = plot_pitch(np_now(pitch_hat.squeeze()[0]))
# self.writer.add_figure('Pitch/target', pitch_fig, model.step)
# self.writer.add_figure('Pitch/ground_truth_aligned', pitch_gta_fig, model.step)
self.writer.add_figure('Ground_Truth_Aligned/target', m_fig,
model.step)
self.writer.add_figure('Ground_Truth_Aligned/linear', m1_hat_fig,
model.step)
self.writer.add_figure('Ground_Truth_Aligned/postnet', m2_hat_fig,
model.step)
m2_hat_wav = reconstruct_waveform(m2_hat)
target_wav = reconstruct_waveform(m)
self.writer.add_audio(tag='Ground_Truth_Aligned/target_wav',
snd_tensor=target_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
self.writer.add_audio(tag='Ground_Truth_Aligned/postnet_wav',
snd_tensor=m2_hat_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
m1_hat, m2_hat, dur_hat = model.generate(x[0, :x_lens[0]].tolist())
m1_hat_fig = plot_mel(m1_hat)
m2_hat_fig = plot_mel(m2_hat)
# pitch_gen_fig = plot_pitch(np_now(pitch_hat.squeeze()))
# self.writer.add_figure('Pitch/generated', pitch_gen_fig, model.step)
self.writer.add_figure('Generated/target', m_fig, model.step)
self.writer.add_figure('Generated/linear', m1_hat_fig, model.step)
self.writer.add_figure('Generated/postnet', m2_hat_fig, model.step)
m2_hat_wav = reconstruct_waveform(m2_hat)
self.writer.add_audio(tag='Generated/target_wav',
snd_tensor=target_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
self.writer.add_audio(tag='Generated/postnet_wav',
snd_tensor=m2_hat_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
def generate_plots(self, model: ForwardTacotron,
session: TTSSession) -> None:
model.eval()
device = next(model.parameters()).device
x, m, ids, lens, dur = session.val_sample
x, m, dur = x.to(device), m.to(device), dur.to(device)
m1_hat, m2_hat, dur_hat = model(x, m, dur)
m1_hat = np_now(m1_hat)[0, :600, :]
m2_hat = np_now(m2_hat)[0, :600, :]
m = np_now(m)[0, :600, :]
m1_hat_fig = plot_mel(m1_hat)
m2_hat_fig = plot_mel(m2_hat)
m_fig = plot_mel(m)
self.writer.add_figure('Ground_Truth_Aligned/target', m_fig,
model.step)
self.writer.add_figure('Ground_Truth_Aligned/linear', m1_hat_fig,
model.step)
self.writer.add_figure('Ground_Truth_Aligned/postnet', m2_hat_fig,
model.step)
m1_hat, m2_hat, m = rescale_mel(m1_hat), rescale_mel(
m2_hat), rescale_mel(m)
m2_hat_wav = reconstruct_waveform(m2_hat)
target_wav = reconstruct_waveform(m)
self.writer.add_audio(tag='Ground_Truth_Aligned/target_wav',
snd_tensor=target_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
self.writer.add_audio(tag='Ground_Truth_Aligned/postnet_wav',
snd_tensor=m2_hat_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
m1_hat, m2_hat, dur_hat = model.generate(x[0].tolist())
m1_hat, m2_hat = rescale_mel(m1_hat), rescale_mel(m2_hat)
m1_hat_fig = plot_mel(m1_hat)
m2_hat_fig = plot_mel(m2_hat)
self.writer.add_figure('Generated/target', m_fig, model.step)
self.writer.add_figure('Generated/linear', m1_hat_fig, model.step)
self.writer.add_figure('Generated/postnet', m2_hat_fig, model.step)
m2_hat_wav = reconstruct_waveform(m2_hat)
self.writer.add_audio(tag='Generated/target_wav',
snd_tensor=target_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
self.writer.add_audio(tag='Generated/postnet_wav',
snd_tensor=m2_hat_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
def evaluate(self, model: ForwardTacotron, val_set: Dataset) -> Tuple[float, float]:
model.eval()
m_val_loss = 0
dur_val_loss = 0
device = next(model.parameters()).device
for i, (x, m, ids, lens, dur) in enumerate(val_set, 1):
x, m, dur, lens = x.to(device), m.to(device), dur.to(device), lens.to(device)
with torch.no_grad():
m1_hat, m2_hat, dur_hat = model(x, m, dur)
m1_loss = self.l1_loss(m1_hat, m, lens)
m2_loss = self.l1_loss(m2_hat, m, lens)
dur_loss = F.l1_loss(dur_hat, dur)
m_val_loss += m1_loss.item() + m2_loss.item()
dur_val_loss += dur_loss.item()
return m_val_loss / len(val_set), dur_val_loss / len(val_set)
durpred_dropout=hp.forward_durpred_dropout,
pitch_rnn_dims=hp.forward_pitch_rnn_dims,
pitch_conv_dims=hp.forward_pitch_conv_dims,
pitch_dropout=hp.forward_pitch_dropout,
pitch_emb_dims=hp.forward_pitch_emb_dims,
pitch_proj_dropout=hp.forward_pitch_proj_dropout,
rnn_dim=hp.forward_rnn_dims,
postnet_k=hp.forward_postnet_K,
postnet_dims=hp.forward_postnet_dims,
prenet_k=hp.forward_prenet_K,
prenet_dims=hp.forward_prenet_dims,
highways=hp.forward_num_highways,
dropout=hp.forward_dropout,
n_mels=hp.num_mels).to(device)
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
print(f'num params {params}')
optimizer = optim.Adam(model.parameters())
restore_checkpoint('forward',
paths,
model,
optimizer,
create_if_missing=True)
if force_gta:
print('Creating Ground Truth Aligned Dataset...\n')
train_set, val_set = get_tts_datasets(paths.data,
8,
r=1,
def train_session(self, model: ForwardTacotron,
optimizer: Optimizer, session: TTSSession) -> None:
current_step = model.get_step()
training_steps = session.max_step - current_step
total_iters = len(session.train_set)
epochs = training_steps // total_iters + 1
simple_table([(f'Steps', str(training_steps // 1000) + 'k Steps'),
('Batch Size', session.bs),
('Learning Rate', session.lr)])
for g in optimizer.param_groups:
g['lr'] = session.lr
m_loss_avg = Averager()
dur_loss_avg = Averager()
duration_avg = Averager()
pitch_loss_avg = Averager()
device = next(model.parameters()).device # use same device as model parameters
for e in range(1, epochs + 1):
for i, (x, m, ids, x_lens, mel_lens, dur, pitch, puncts) in enumerate(
session.train_set, 1
):
start = time.time()
model.train()
x, m, dur, x_lens, mel_lens, pitch, puncts = (
x.to(device),
m.to(device),
dur.to(device),
x_lens.to(device),
mel_lens.to(device),
pitch.to(device),
puncts.to(device),
)
# print("*" * 20)
# print(x)
# print("*" * 20)
m1_hat, m2_hat, dur_hat, pitch_hat = model(
x, m, dur, mel_lens, pitch, puncts
)
m1_loss = self.l1_loss(m1_hat, m, mel_lens)
m2_loss = self.l1_loss(m2_hat, m, mel_lens)
dur_loss = self.l1_loss(dur_hat.unsqueeze(1), dur.unsqueeze(1), x_lens)
pitch_loss = self.l1_loss(pitch_hat, pitch.unsqueeze(1), x_lens)
loss = m1_loss + m2_loss + 0.3 * dur_loss + 0.1 * pitch_loss
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), hp.tts_clip_grad_norm)
optimizer.step()
m_loss_avg.add(m1_loss.item() + m2_loss.item())
dur_loss_avg.add(dur_loss.item())
step = model.get_step()
k = step // 1000
duration_avg.add(time.time() - start)
pitch_loss_avg.add(pitch_loss.item())
speed = 1. / duration_avg.get()
msg = f'| Epoch: {e}/{epochs} ({i}/{total_iters}) | Mel Loss: {m_loss_avg.get():#.4} ' \
f'| Dur Loss: {dur_loss_avg.get():#.4} | Pitch Loss: {pitch_loss_avg.get():#.4} ' \
f'| {speed:#.2} steps/s | Step: {k}k | '
if step % hp.forward_checkpoint_every == 0:
ckpt_name = f'forward_step{k}K'
save_checkpoint('forward', self.paths, model, optimizer,
name=ckpt_name, is_silent=True)
if step % hp.forward_plot_every == 0:
self.generate_plots(model, session)
self.writer.add_scalar('Mel_Loss/train', m1_loss + m2_loss, model.get_step())
self.writer.add_scalar('Pitch_Loss/train', pitch_loss, model.get_step())
self.writer.add_scalar('Duration_Loss/train', dur_loss, model.get_step())
self.writer.add_scalar('Params/batch_size', session.bs, model.get_step())
self.writer.add_scalar('Params/learning_rate', session.lr, model.get_step())
stream(msg)
m_val_loss, dur_val_loss, pitch_val_loss = self.evaluate(model, session.val_set)
self.writer.add_scalar('Mel_Loss/val', m_val_loss, model.get_step())
self.writer.add_scalar('Duration_Loss/val', dur_val_loss, model.get_step())
self.writer.add_scalar('Pitch_Loss/val', pitch_val_loss, model.get_step())
save_checkpoint('forward', self.paths, model, optimizer, is_silent=True)
m_loss_avg.reset()
duration_avg.reset()
pitch_loss_avg.reset()
print(' ')
def train_session(self, model_tts: ForwardTacotron,
model_asr: Wav2Vec2ForCTC, optimizer_tts: Optimizer,
tts_session: ForwardSession, asr_session: ASRSession,
asr_trainer, optimizer_asr) -> None:
# print(tts_session.path)
# exit()
asr_trainer_state = {'logs': []}
current_step = model_tts.get_step()
tts_training_steps = tts_session.max_step - current_step
try:
_, asr_current_step = get_last_checkpoint(
'./checkpoints/sme_speech_tts.asr_forward/', 'model_at')
asr_training_steps = tts_session.max_step - asr_current_step
except:
asr_current_step = 0
asr_training_steps = tts_training_steps
total_iters = len(tts_session.train_set)
epochs = tts_training_steps // total_iters + 1
simple_table([
('TTS Steps', str(tts_training_steps // 1000) + 'k Steps'),
('ASR Steps', str(asr_training_steps // 1000) + 'k Steps'),
('Batch Size TTS', tts_session.bs),
('Learning Rate', tts_session.lr)
])
for g in optimizer_tts.param_groups:
g['lr'] = tts_session.lr
m_loss_avg = Averager()
dur_loss_avg = Averager()
duration_avg = Averager()
device = next(model_tts.parameters()
).device # use same device as model parameters
warnings.filterwarnings('ignore', category=UserWarning)
for e in range(1, epochs + 1):
#tts train loop for epoch
for i, (x, m, ids, x_lens, mel_lens,
dur) in enumerate(tts_session.train_set, 1):
start = time.time()
model_tts.train()
x, m, dur, x_lens, mel_lens = x.to(device), m.to(device), dur.to(device),\
x_lens.to(device), mel_lens.to(device)
m1_hat, m2_hat, dur_hat = model_tts(x, m, dur, mel_lens)
m1_loss = self.l1_loss(m1_hat, m, mel_lens)
m2_loss = self.l1_loss(m2_hat, m, mel_lens)
dur_loss = self.l1_loss(dur_hat.unsqueeze(1), dur.unsqueeze(1),
x_lens)
tts_s_loss = m1_loss + m2_loss + 0.1 * dur_loss
optimizer_tts.zero_grad()
# tts_s_loss.backward()
torch.nn.utils.clip_grad_norm_(model_tts.parameters(),
hp.tts_clip_grad_norm)
# optimizer_tts.step()
m_loss_avg.add(m1_loss.item() + m2_loss.item())
dur_loss_avg.add(dur_loss.item())
step = model_tts.get_step()
k = step // 1000
duration_avg.add(time.time() - start)
# pitch_loss_avg.add(pitch_loss.item())
speed = 1. / duration_avg.get()
msg_tts = f'| TTS MODEL (supervised training ): '\
f'| Epoch: {e}/{epochs} ({i}/{total_iters}) | Mel Loss: {m_loss_avg.get():#.4} ' \
f'| Dur Loss: {dur_loss_avg.get():#.4} ' \
f'| {speed:#.2} steps/s | Step: {k}k | '
if step % hp.forward_checkpoint_every == 0:
ckpt_name = f'forward_step{k}K'
save_checkpoint('forward',
self.paths,
model_tts,
optimizer_tts,
name=ckpt_name,
is_silent=True)
if step % hp.forward_plot_every == 0:
self.generate_plots(model_tts, tts_session)
self.writer.add_scalar('Mel_Loss/train', m1_loss + m2_loss,
model_tts.get_step())
self.writer.add_scalar('Duration_Loss/train', dur_loss,
model_tts.get_step())
self.writer.add_scalar('Params/batch_size', tts_session.bs,
model_tts.get_step())
self.writer.add_scalar('Params/learning_rate', tts_session.lr,
model_tts.get_step())
stream(msg_tts)
# print(msg_tts)
# print(torch.cuda.memory_allocated(device=device))
# model_tts = model_tts.to('cpu')
for step, inputs in enumerate(asr_session.train_set):
optimizer_asr.zero_grad()
model_asr.to(device)
for k, v in inputs.items():
if isinstance(v, torch.Tensor):
inputs[k] = v.to(device)
model_asr.train()
outputs = model_asr(**inputs)
asr_s_loss = outputs["loss"] if isinstance(
outputs, dict) else outputs[0]
# asr_s_loss = asr_s_loss.mean()
msg_asr = f'| ASR MODEL (supervised training) : '\
f'| Epoch: {e}/{epochs} ({step}/{len(asr_session.train_set)}) | Loss ASR: {asr_s_loss:#.4} '\
f' ||||||||||||||||||||||'
stream(msg_asr)
# # model_asr.to('cuda')
m_val_loss, dur_val_loss = self.evaluate(model_tts,
tts_session.val_set)
eval_tts_msg = f'| TTS MODEL (supervised eval ): '\
f'| Epoch: {e}/{epochs} | Val Loss: {m_val_loss:#.4} ' \
f'| Dur Val Loss: {dur_val_loss:#.4} ' \
stream(eval_tts_msg)
tts_eval_loss = m_val_loss + dur_val_loss
# print(eval_tts_msg)
# ASR eval supervised
print('\nEvaluating ASR model ...')
# model_asr.to('cpu')
asr_eval_loss = 0
eval_wer = 0
for step, inputs in enumerate(asr_session.test_set):
asr_eval_loss_i, logits_a, labels_a = asr_trainer.prediction_step(
model_asr, inputs, False)
asr_eval_loss += asr_eval_loss_i
logits_a.to('cpu')
eval_wer_i = asr_trainer.compute_metrics(
EvalPrediction(predictions=logits_a, label_ids=labels_a))
eval_wer += eval_wer_i['wer']
# print(eval_wer)
eval_wer = eval_wer / step
asr_eval_loss = asr_eval_loss / step
msg_asr_eval = f'| ASR MODEL (supervised eval) : Epoch {e}/{epochs} | Loss ASR: {asr_eval_loss:#.4} | WER: {eval_wer} |||||||||||||||||||||||||||||||||||||||||||||||||||||'
stream(msg_asr_eval)
# dual transformation loop
# tts_s_loss = 3
# asr_s_loss = 1
tts_u_loss, asr_u_loss = self.dual_transform(
model_tts, model_asr, optimizer_tts, optimizer_asr,
asr_session.test_set, m_loss_avg, dur_loss_avg, device,
asr_current_step, e, epochs, duration_avg, total_iters,
tts_s_loss, asr_s_loss, tts_session.lr, tts_session.path)
step += 1
asr_path = f'checkpoint-27364'
modelasr_folder = './checkpoints/sme_speech_tts.asr_forward/'
new_check = modelasr_folder + asr_path
os.makedirs(new_check, exist_ok=True)
# asr_path, asr_step = get_last_checkpoint(modelasr_folder, modelasr_name)
save_checkpoint('forward',
self.paths,
model_tts,
optimizer_tts,
is_silent=True)
# asr_u_loss = 2
if "logs" not in asr_trainer_state:
asr_trainer_state['logs'] = []
asr_trainer_state['logs'].append({
'step':
step,
'epoch':
e,
'asr_s_loss':
int(asr_s_loss),
'asr_u_loss':
int(asr_u_loss),
'tts_s_loss':
int(tts_s_loss),
'tts_u_loss':
int(tts_u_loss),
'tts_eval_loss':
int(tts_eval_loss),
'asr_eval_loss':
int(asr_eval_loss),
'eval_wer':
eval_wer
})
with open(f'{modelasr_folder+ asr_path}/dt_trainer_state.json',
'w') as f:
json.dump(asr_trainer_state, f)
model_asr.save_pretrained(f'{new_check}')
torch.save(optimizer_asr.state_dict(), f'{new_check}/optimizer.pt')
print("Exiting due to cuda OOM!")
exit(11)
def train_session(self, model: ForwardTacotron, optimizer: Optimizer,
session: TTSSession) -> None:
current_step = model.get_step()
training_steps = session.max_step - current_step
total_iters = len(session.train_set)
epochs = training_steps // total_iters + 1
simple_table([(f'Steps', str(training_steps // 1000) + 'k Steps'),
('Batch Size', session.bs),
('Learning Rate', session.lr)])
for g in optimizer.param_groups:
g['lr'] = session.lr
m_loss_avg = Averager()
dur_loss_avg = Averager()
duration_avg = Averager()
pitch_loss_avg = Averager()
device = next(
model.parameters()).device # use same device as model parameters
for e in range(1, epochs + 1):
for i, batch in enumerate(session.train_set, 1):
batch = to_device(batch, device=device)
start = time.time()
model.train()
pitch_zoneout_mask = torch.rand(
batch['x'].size()) > self.train_cfg['pitch_zoneout']
energy_zoneout_mask = torch.rand(
batch['x'].size()) > self.train_cfg['energy_zoneout']
pitch_target = batch['pitch'].detach().clone()
energy_target = batch['energy'].detach().clone()
batch['pitch'] = batch['pitch'] * pitch_zoneout_mask.to(
device).float()
batch['energy'] = batch['energy'] * energy_zoneout_mask.to(
device).float()
pred = model(batch)
m1_loss = self.l1_loss(pred['mel'], batch['mel'],
batch['mel_len'])
m2_loss = self.l1_loss(pred['mel_post'], batch['mel'],
batch['mel_len'])
dur_loss = self.l1_loss(pred['dur'].unsqueeze(1),
batch['dur'].unsqueeze(1),
batch['x_len'])
pitch_loss = self.l1_loss(pred['pitch'],
pitch_target.unsqueeze(1),
batch['x_len'])
energy_loss = self.l1_loss(pred['energy'],
energy_target.unsqueeze(1),
batch['x_len'])
loss = m1_loss + m2_loss \
+ self.train_cfg['dur_loss_factor'] * dur_loss \
+ self.train_cfg['pitch_loss_factor'] * pitch_loss \
+ self.train_cfg['energy_loss_factor'] * energy_loss
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(
model.parameters(), self.train_cfg['clip_grad_norm'])
optimizer.step()
m_loss_avg.add(m1_loss.item() + m2_loss.item())
dur_loss_avg.add(dur_loss.item())
step = model.get_step()
k = step // 1000
duration_avg.add(time.time() - start)
pitch_loss_avg.add(pitch_loss.item())
speed = 1. / duration_avg.get()
msg = f'| Epoch: {e}/{epochs} ({i}/{total_iters}) | Mel Loss: {m_loss_avg.get():#.4} ' \
f'| Dur Loss: {dur_loss_avg.get():#.4} | Pitch Loss: {pitch_loss_avg.get():#.4} ' \
f'| {speed:#.2} steps/s | Step: {k}k | '
if step % self.train_cfg['checkpoint_every'] == 0:
save_checkpoint(model=model,
optim=optimizer,
config=self.config,
path=self.paths.forward_checkpoints /
f'forward_step{k}k.pt')
if step % self.train_cfg['plot_every'] == 0:
self.generate_plots(model, session)
self.writer.add_scalar('Mel_Loss/train', m1_loss + m2_loss,
model.get_step())
self.writer.add_scalar('Pitch_Loss/train', pitch_loss,
model.get_step())
self.writer.add_scalar('Energy_Loss/train', energy_loss,
model.get_step())
self.writer.add_scalar('Duration_Loss/train', dur_loss,
model.get_step())
self.writer.add_scalar('Params/batch_size', session.bs,
model.get_step())
self.writer.add_scalar('Params/learning_rate', session.lr,
model.get_step())
stream(msg)
val_out = self.evaluate(model, session.val_set)
self.writer.add_scalar('Mel_Loss/val', val_out['mel_loss'],
model.get_step())
self.writer.add_scalar('Duration_Loss/val', val_out['dur_loss'],
model.get_step())
self.writer.add_scalar('Pitch_Loss/val', val_out['pitch_loss'],
model.get_step())
self.writer.add_scalar('Energy_Loss/val', val_out['energy_loss'],
model.get_step())
save_checkpoint(model=model,
optim=optimizer,
config=self.config,
path=self.paths.forward_checkpoints /
'latest_model.pt')
m_loss_avg.reset()
duration_avg.reset()
pitch_loss_avg.reset()
print(' ')
def generate_plots(self, model: ForwardTacotron,
session: TTSSession) -> None:
model.eval()
device = next(model.parameters()).device
batch = session.val_sample
batch = to_device(batch, device=device)
pred = model(batch)
m1_hat = np_now(pred['mel'])[0, :600, :]
m2_hat = np_now(pred['mel_post'])[0, :600, :]
m_target = np_now(batch['mel'])[0, :600, :]
m1_hat_fig = plot_mel(m1_hat)
m2_hat_fig = plot_mel(m2_hat)
m_target_fig = plot_mel(m_target)
pitch_fig = plot_pitch(np_now(batch['pitch'][0]))
pitch_gta_fig = plot_pitch(np_now(pred['pitch'].squeeze()[0]))
energy_fig = plot_pitch(np_now(batch['energy'][0]))
energy_gta_fig = plot_pitch(np_now(pred['energy'].squeeze()[0]))
self.writer.add_figure('Pitch/target', pitch_fig, model.step)
self.writer.add_figure('Pitch/ground_truth_aligned', pitch_gta_fig,
model.step)
self.writer.add_figure('Energy/target', energy_fig, model.step)
self.writer.add_figure('Energy/ground_truth_aligned', energy_gta_fig,
model.step)
self.writer.add_figure('Ground_Truth_Aligned/target', m_target_fig,
model.step)
self.writer.add_figure('Ground_Truth_Aligned/linear', m1_hat_fig,
model.step)
self.writer.add_figure('Ground_Truth_Aligned/postnet', m2_hat_fig,
model.step)
m2_hat_wav = self.dsp.griffinlim(m2_hat)
target_wav = self.dsp.griffinlim(m_target)
self.writer.add_audio(tag='Ground_Truth_Aligned/target_wav',
snd_tensor=target_wav,
global_step=model.step,
sample_rate=self.dsp.sample_rate)
self.writer.add_audio(tag='Ground_Truth_Aligned/postnet_wav',
snd_tensor=m2_hat_wav,
global_step=model.step,
sample_rate=self.dsp.sample_rate)
gen = model.generate(batch['x'][0:1, :batch['x_len'][0]])
m1_hat_fig = plot_mel(np_now(gen['mel']))
m2_hat_fig = plot_mel(np_now(gen['mel_post']))
pitch_gen_fig = plot_pitch(np_now(gen['pitch'].squeeze()))
energy_gen_fig = plot_pitch(np_now(gen['energy'].squeeze()))
self.writer.add_figure('Pitch/generated', pitch_gen_fig, model.step)
self.writer.add_figure('Energy/generated', energy_gen_fig, model.step)
self.writer.add_figure('Generated/target', m_target_fig, model.step)
self.writer.add_figure('Generated/linear', m1_hat_fig, model.step)
self.writer.add_figure('Generated/postnet', m2_hat_fig, model.step)
m2_hat_wav = self.dsp.griffinlim(m2_hat)
self.writer.add_audio(tag='Generated/target_wav',
snd_tensor=target_wav,
global_step=model.step,
sample_rate=self.dsp.sample_rate)
self.writer.add_audio(tag='Generated/postnet_wav',
snd_tensor=m2_hat_wav,
global_step=model.step,
sample_rate=self.dsp.sample_rate)
def main():
# Parse Arguments
parser = argparse.ArgumentParser(description='Train Tacotron TTS')
parser.add_argument('--force_train',
'-f',
action='store_true',
help='Forces the model to train past total steps')
parser.add_argument('--force_gta',
'-g',
action='store_true',
help='Force the model to create GTA features')
parser.add_argument(
'--force_cpu',
'-c',
action='store_true',
help='Forces CPU-only training, even when in CUDA capable environment')
parser.add_argument('--hp_file',
metavar='FILE',
default='hparams.py',
help='The file to use for the hyperparameters')
args = parser.parse_args()
hp.configure(args.hp_file) # Load hparams from file
paths = Paths(hp.data_path, hp.voc_model_id, hp.tts_model_id)
force_gta = args.force_gta
if not args.force_cpu and torch.cuda.is_available():
device = torch.device('cuda')
for session in hp.forward_schedule:
_, _, batch_size = session
if batch_size % torch.cuda.device_count() != 0:
raise ValueError(
'`batch_size` must be evenly divisible by n_gpus!')
else:
device = torch.device('cpu')
print('Using device:', device)
# Instantiate Forward TTS Model
print('\nInitialising Forward TTS Model...\n')
model = ForwardTacotron(embed_dims=hp.forward_embed_dims,
num_chars=len(symbols),
durpred_rnn_dims=hp.forward_durpred_rnn_dims,
durpred_conv_dims=hp.forward_durpred_conv_dims,
rnn_dim=hp.forward_rnn_dims,
postnet_k=hp.forward_postnet_K,
postnet_dims=hp.forward_postnet_dims,
prenet_k=hp.forward_prenet_K,
prenet_dims=hp.forward_prenet_dims,
highways=hp.forward_num_highways,
dropout=hp.forward_dropout,
n_mels=hp.num_mels).to(device)
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
print(f'num params {params}')
optimizer = optim.Adam(model.parameters())
restore_checkpoint('forward',
paths,
model,
optimizer,
create_if_missing=True)
if not force_gta:
for i, session in enumerate(hp.forward_schedule):
current_step = model.get_step()
lr, max_step, batch_size = session
training_steps = max_step - current_step
simple_table([(f'Steps', str(training_steps // 1000) + 'k Steps'),
('Batch Size', batch_size), ('Learning Rate', lr)])
train_set, mel_example = get_tts_datasets(paths.data,
batch_size,
1,
alignments=True)
train_loop(paths, model, optimizer, train_set, lr, training_steps,
mel_example)
train_set, mel_example = get_tts_datasets(paths.data,
8,
1,
alignments=True)
create_gta_features(model, train_set, paths.gta)
print('Training Complete.')