def gen_tacotron(self, 華, inputs):
for i, x in enumerate(inputs, 1):
print(f'\n| Generating {i}/{len(inputs)}')
_, m, attention = self.tts_model.generate(x)
# Fix mel spectrogram scaling to be from 0 to 1
m = (m + 4) / 8
np.clip(m, 0, 1, out=m)
if self.args.vocoder == 'griffinlim':
v_type = self.args.vocoder
elif self.args.vocoder == 'wavernn' and self.args.batched:
v_type = 'wavernn_batched'
else:
v_type = 'wavernn_unbatched'
# == define output name == #
if len(華) == 0:
output_name = re.split(r'\,|\.|\!|\?| ', input_text)[0]
elif 1 <= len(華) <= 9:
output_name = 華[:-1]
elif 9 < len(華):
output_name = 華[:8]
print(output_name)
save_path = "output/{}.wav".format(output_name)
##
if self.args.vocoder == 'wavernn':
m = torch.tensor(m).unsqueeze(0)
self.voc_model.generate(m, save_path, self.args.batched,
hp.voc_target, hp.voc_overlap,
hp.mu_law)
elif self.args.vocoder == 'griffinlim':
wav = reconstruct_waveform(m, n_iter=self.args.iters)
save_wav(wav, save_path)
def tsau(input_text, save_path):
if input_text:
inputs = [text_to_sequence(input_text.strip(), hp.tts_cleaner_names)]
else:
with open('sentences.txt') as f:
inputs = [
text_to_sequence(l.strip(), hp.tts_cleaner_names) for l in f
]
if args.vocoder == 'wavernn':
voc_k = voc_model.get_step() // 1000
tts_k = tts_model.get_step() // 1000
simple_table([
('Tacotron', str(tts_k) + 'k'), ('r', tts_model.r),
('Vocoder Type', 'WaveRNN'), ('WaveRNN', str(voc_k) + 'k'),
('Generation Mode', 'Batched' if batched else 'Unbatched'),
('Target Samples', target if batched else 'N/A'),
('Overlap Samples', overlap if batched else 'N/A')
])
elif args.vocoder == 'griffinlim':
tts_k = tts_model.get_step() // 1000
simple_table([('Tacotron', str(tts_k) + 'k'), ('r', tts_model.r),
('Vocoder Type', 'Griffin-Lim'),
('GL Iters', args.iters)])
for i, x in enumerate(inputs, 1):
print(f'\n| Generating {i}/{len(inputs)}')
_, m, attention = tts_model.generate(x)
# Fix mel spectrogram scaling to be from 0 to 1
m = (m + 4) / 8
np.clip(m, 0, 1, out=m)
if args.vocoder == 'griffinlim':
v_type = args.vocoder
elif args.vocoder == 'wavernn' and args.batched:
v_type = 'wavernn_batched'
else:
v_type = 'wavernn_unbatched'
if save_attn: save_attention(attention, save_path)
if args.vocoder == 'wavernn':
m = torch.tensor(m).unsqueeze(0)
voc_model.generate(m, save_path, batched, target, overlap,
hp.mu_law)
elif args.vocoder == 'griffinlim':
wav = reconstruct_waveform(m, n_iter=args.iters)
save_wav(wav, save_path)
print('\n\nDone.\n')
def gen_tacotron2(self, 華, inputs):
for i, x in enumerate(inputs, 1):
print(f'\n| Generating {i}/{len(inputs)}')
print(x)
x = np.array(x)[None, :]
x = torch.autograd.Variable(torch.from_numpy(x)).cuda().long()
self.tts_model.eval()
mel_outputs, mel_outputs_postnet, _, alignments = self.tts_model.inference(
x)
if self.args.vocoder == 'griffinlim':
v_type = self.args.vocoder
elif self.args.vocoder == 'wavernn' and self.args.batched:
v_type = 'wavernn_batched'
else:
v_type = 'wavernn_unbatched'
# == define output name == #
if len(華) == 0:
output_name = re.split(r'\,|\.|\!|\?| ', input_text)[0]
elif 1 <= len(華) <= 9:
output_name = 華[:-1]
elif 9 < len(華):
output_name = 華[:8]
print(output_name)
save_path = "output/{}.wav".format(output_name)
##
if self.args.vocoder == 'wavernn':
m = mel_outputs_postnet
self.voc_model.generate(m, save_path, self.args.batched,
hp.voc_target, hp.voc_overlap,
hp.mu_law)
elif self.args.vocoder == 'griffinlim':
m = torch.squeeze(mel_outputs_postnet).detach().cpu().numpy()
wav = reconstruct_waveform(m, n_iter=self.args.iters)
save_wav(wav, save_path)
_, m, dur, pitch = tts_model.generate(x,
alpha=args.alpha,
pitch_function=pitch_function)
if args.vocoder == 'griffinlim':
v_type = args.vocoder
elif args.vocoder == 'wavernn' and args.batched:
v_type = 'wavernn_batched'
else:
v_type = 'wavernn_unbatched'
if input_text:
save_path = paths.forward_output / f'{input_text[:10]}_{args.alpha}_{v_type}_{tts_k}k_amp{args.amp}.wav'
else:
save_path = paths.forward_output / f'{i}_{v_type}_{tts_k}k_alpha{args.alpha}_amp{args.amp}.wav'
if args.vocoder == 'wavernn':
m = torch.tensor(m).unsqueeze(0)
voc_model.generate(m, save_path, batched, hp.voc_target,
hp.voc_overlap, hp.mu_law)
if args.vocoder == 'melgan':
m = torch.tensor(m).unsqueeze(0)
torch.save(
m, paths.forward_output /
f'{i}_{tts_k}_alpha{args.alpha}_amp{args.amp}.mel')
elif args.vocoder == 'griffinlim':
wav = reconstruct_waveform(m, n_iter=args.iters)
save_wav(wav, save_path)
print('\n\nDone.\n')
def dual_transform(self, model_tts, model_asr, optimizer_tts,
optimizer_asr, asr_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_lr,
tts_dt_path):
print('\n\nStarting DualTransformation loop...\n')
# exit()
tmp_dir = './checkpoints/sme_speech_tts.asr_forward/dual_transform_tmp'
os.makedirs(tmp_dir, exist_ok=True)
# generate tmp ASR training data
asr_train_data = []
input_set = get_unpaired_txt(35)
# print(input_set)
text = [clean_text(v) for v in input_set]
inputs = [text_to_sequence(t) for t in text]
# generate unpaired data for ASR from TTS
for i, x in enumerate(inputs, 1):
_, m, dur = model_tts.generate(x, alpha=1.)
wav = reconstruct_waveform(m, n_iter=32)
wav_path = os.path.join(tmp_dir, f'{i}.wav')
save_wav(wav, wav_path)
asr_train_data.append((wav_path, text[i - 1]))
# print(asr_train_data)
dt_asr_data = load_dt_data(asr_train_data)
# reinit trainer with only tmp train data
asr_trainer_dt = init_trainer(dt_asr_data, None)
dt_train = asr_trainer_dt.get_train_dataloader()
# unsuper train loop for ASR
for step, inputs in enumerate(dt_train, 1):
# model_asr.cpu()
model_asr.train()
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_u_loss = outputs["loss"] if isinstance(outputs,
dict) else outputs[0]
# asr_u_loss.detach()
# asr_u_loss = asr_s_loss.mean()
# model_name = step + asr_current_step
msg_asr = f'| ASR MODEL (unsupervised training) : '\
f'| Epoch: {e}/{epochs} ({step}/{len(dt_train)}) | Loss ASR: {asr_u_loss:#.4} '\
f' ||||||||||||||||||||||||||||||||||||||||||||||||'
stream(msg_asr)
# for f in os.listdir(tmp_dir):
# file_path = os.path.join(tmp_dir, f)
# if f.endswith('.wav'):
# os.unlink(file_path)
# generate tmp TTS data from ASR
# model_asr.to(device)
asr_predict_for_dt(model_asr)
subprocess.check_output(
'python preprocess.py -p "./data/speech-sme-tts" -d=True',
shell=True,
stderr=subprocess.STDOUT)
print('Finished preprocessing for tmp data!')
tmp_tts_train = get_tts_datasets(tts_dt_path,
batch_size=2,
r=1,
model_type='forward_dt')
print("Loaded tmp dataset!")
# unsuper TTS training
for i, (x, m, ids, x_lens, mel_lens,
dur) in enumerate(tmp_tts_train, 1):
start = time.time()
model_tts.to(device)
model_tts.train()
# optimizer_tts.zero_grad()
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_u_loss = m1_loss + m2_loss + 0.1 * dur_loss
# optimizer_tts.zero_grad()
# tts_u_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 (unsupervised 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 | '
stream(msg_tts)
# m_val_loss, dur_val_loss = self.evaluate(model_tts, tts_session.val_set)
#TODO: combine L and update
# asr_s_loss = torch.tensor(asr_s_loss).to(device)
combined_loss = 0.5 * (tts_s_loss + asr_s_loss) + (tts_u_loss +
asr_u_loss)
# backwards
combined_loss.to(device)
# print(combined_loss)
combined_loss.backward()
optimizer_tts.step()
for state in optimizer_asr.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.to(device)
optimizer_asr.step()
m_loss_avg.reset()
duration_avg.reset()
# pitch_loss_avg.reset()
dt_msg = f'\n\nFinished DT loop in epoch {e}!\n'
stream(dt_msg)
print(' ')
return tts_u_loss, asr_u_loss