Medix-AI/coqui-tts
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coqui-tts
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linter fixes

This commit is contained in:
erogol 2020-03-10 11:30:13 +01:00
parent e97bb45aba
commit 669a2e1d73
3 changed files with 30 additions and 34 deletions
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9
layers/losses.py
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@ -126,7 +126,7 @@ class BCELossMasked(nn.Module):
loss = loss / mask.sum()
return loss
class GuidedAttentionLoss(torch.nn.Module):
def __init__(self, sigma=0.4):
super(GuidedAttentionLoss, self).__init__()
@ -179,6 +179,7 @@ class TacotronLoss(torch.nn.Module):
if c.ga_alpha > 0:
self.criterion_ga = GuidedAttentionLoss(sigma=ga_sigma)
# stopnet loss
# pylint: disable=not-callable
self.criterion_st = BCELossMasked(pos_weight=torch.tensor(stopnet_pos_weight)) if c.stopnet else None
def forward(self, postnet_output, decoder_output, mel_input, linear_input,
@ -213,7 +214,7 @@ class TacotronLoss(torch.nn.Module):
if not self.config.separate_stopnet and self.config.stopnet:
loss += stop_loss
return_dict['stopnet_loss'] = stop_loss
# backward decoder loss (if enabled)
if self.config.bidirectional_decoder:
if self.config.loss_masking:
@ -224,13 +225,13 @@ class TacotronLoss(torch.nn.Module):
loss += decoder_b_loss + decoder_c_loss
return_dict['decoder_b_loss'] = decoder_b_loss
return_dict['decoder_c_loss'] = decoder_c_loss
# guided attention loss (if enabled)
if self.config.ga_alpha > 0:
ga_loss = self.criterion_ga(alignments, input_lens, alignment_lens)
loss += ga_loss * self.ga_alpha
return_dict['ga_loss'] = ga_loss * self.ga_alpha
return_dict['loss'] = loss
return return_dict

24
train.py
View File

@ -214,7 +214,7 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
"GradNormST:{:.5f} AvgTextLen:{:.1f} AvgSpecLen:{:.1f} StepTime:{:.2f} "
"LoaderTime:{:.2f} LR:{:.6f}".format(
num_iter, batch_n_iter, global_step, loss_dict['postnet_loss'].item(),
loss_dict['decoder_loss'].item(), loss_dict['stopnet_loss'].item(),
loss_dict['decoder_loss'].item(), loss_dict['stopnet_loss'].item(),
loss_dict['ga_loss'].item(), grad_norm, grad_norm_st, avg_text_length,
avg_spec_length, step_time, loader_time, current_lr),
flush=True)
@ -232,7 +232,7 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
'avg_postnet_loss': float(loss_dict['postnet_loss'].item()),
'avg_decoder_loss': float(loss_dict['decoder_loss'].item()),
'avg_stop_loss': loss_dict['stopnet_loss'].item()
if isinstance(loss_dict['stopnet_loss'], float) else float(loss_dict['stopnet_loss'].item()),
if isinstance(loss_dict['stopnet_loss'], float) else float(loss_dict['stopnet_loss'].item()),
'avg_step_time': step_time,
'avg_loader_time': loader_time
}
@ -399,16 +399,16 @@ def evaluate(model, criterion, ap, global_step, epoch):
" | > TotalLoss: {:.5f} PostnetLoss: {:.5f} - {:.5f} DecoderLoss:{:.5f} - {:.5f} "
"StopLoss: {:.5f} - {:.5f} GALoss: {:.5f} - {:.5f} AlignScore: {:.4f} - {:.4f}"
.format(loss_dict['loss'].item(),
loss_dict['postnet_loss'].item(),
keep_avg['avg_postnet_loss'],
loss_dict['decoder_loss'].item(),
keep_avg['avg_decoder_loss'],
loss_dict['stopnet_loss'].item(),
keep_avg['avg_stop_loss'],
loss_dict['ga_loss'].item(),
keep_avg['avg_ga_loss'],
align_score, keep_avg['avg_align_score']),
flush=True)
loss_dict['postnet_loss'].item(),
keep_avg['avg_postnet_loss'],
loss_dict['decoder_loss'].item(),
keep_avg['avg_decoder_loss'],
loss_dict['stopnet_loss'].item(),
keep_avg['avg_stop_loss'],
loss_dict['ga_loss'].item(),
keep_avg['avg_ga_loss'],
align_score, keep_avg['avg_align_score']),
flush=Tr ue)
if args.rank == 0:
# Diagnostic visualizations

31
utils/audio.py
View File

@ -50,7 +50,7 @@ class AudioProcessor(object):
self.clip_norm = clip_norm
self.do_trim_silence = do_trim_silence
self.trim_db = trim_db
self.sound_norm = sound_norm
self.do_sound_norm = sound_norm
# setup stft parameters
if hop_length is None:
self.n_fft, self.hop_length, self.win_length = self._stft_parameters()
@ -232,17 +232,26 @@ class AudioProcessor(object):
return librosa.effects.trim(
wav, top_db=self.trim_db, frame_length=self.win_length, hop_length=self.hop_length)[0]
def sound_norm(self, x):
@staticmethod
def sound_norm(x):
return x / abs(x).max() * 0.9
### save and load ###
### save and load ###
def load_wav(self, filename, sr=None):
if sr is None:
x, sr = sf.read(filename)
else:
x, sr = librosa.load(filename, sr=sr)
if self.do_trim_silence:
try:
x = self.trim_silence(x)
except ValueError:
print(f' [!] File cannot be trimmed for silence - {filename}')
assert self.sample_rate == sr, "%s vs %s"%(self.sample_rate, sr)
if self.do_sound_norm:
x = self.sound_norm(x)
return x
def save_wav(self, wav, path):
wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav))))
scipy.io.wavfile.write(path, self.sample_rate, wav_norm.astype(np.int16))
@ -263,20 +272,6 @@ class AudioProcessor(object):
x = np.sign(wav) / mu * ((1 + mu) ** np.abs(wav) - 1)
return x
def load_wav(self, filename, sr=None):
if sr is None:
x, sr = sf.read(filename)
else:
x, sr = librosa.load(filename, sr=sr)
if self.do_trim_silence:
try:
x = self.trim_silence(x)
except ValueError:
print(f' [!] File cannot be trimmed for silence - {filename}')
assert self.sample_rate == sr, "%s vs %s"%(self.sample_rate, sr)
if self.sound_norm:
x = x / abs(x).max() * 0.9
return x
@staticmethod
def encode_16bits(x):