Medix-AI/coqui-tts
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coqui-tts
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guided attn

This commit is contained in:
Eren Golge 2018-04-24 11:27:57 -07:00
parent 95e1ef813e
commit 286282ef13
2 changed files with 867 additions and 35 deletions
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881
notebooks/TacotronPlayGround.ipynb
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File diff suppressed because one or more lines are too long

21
train.py
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@ -66,6 +66,7 @@ def train(model, criterion, data_loader, optimizer, epoch):
epoch_time = 0
avg_linear_loss = 0
avg_mel_loss = 0
avg_attn_loss = 0
print(" | > Epoch {}/{}".format(epoch, c.epochs))
progbar = Progbar(len(data_loader.dataset) / c.batch_size)
@ -102,6 +103,18 @@ def train(model, criterion, data_loader, optimizer, epoch):
mel_spec_var = mel_spec_var.cuda()
mel_lengths_var = mel_lengths_var.cuda()
linear_spec_var = linear_spec_var.cuda()
# create attention mask
# TODO: vectorize
M = np.zeros([N, T])
for t in range(T):
for n in range(N):
val = 20 * np.exp(-pow((n/N)-(t/T), 2.0)/0.05)
M[n, t] = val
e_x = np.exp(M - np.max(M))
M = e_x / e_x.sum(axis=0) # only difference
M = Variable(M)
M = torch.stack([M]*32)
# forward pass
mel_output, linear_output, alignments =\
@ -113,7 +126,8 @@ def train(model, criterion, data_loader, optimizer, epoch):
+ 0.5 * criterion(linear_output[:, :, :n_priority_freq],
linear_spec_var[:, :, :n_priority_freq],
mel_lengths_var)
loss = mel_loss + linear_loss
attention_loss = criterion(, alignments, mel_lengths_var)
loss = mel_loss + linear_loss + 0.2 * attention_loss
# backpass and check the grad norm
loss.backward()
@ -132,15 +146,18 @@ def train(model, criterion, data_loader, optimizer, epoch):
('linear_loss',
linear_loss.data[0]),
('mel_loss', mel_loss.data[0]),
('attn_loss', attention_loss.data[0]),
('grad_norm', grad_norm)])
avg_linear_loss += linear_loss.data[0]
avg_mel_loss += mel_loss.data[0]
avg_attn_loss += attention_loss.data[0]
# Plot Training Iter Stats
tb.add_scalar('TrainIterLoss/TotalLoss', loss.data[0], current_step)
tb.add_scalar('TrainIterLoss/LinearLoss', linear_loss.data[0],
current_step)
tb.add_scalar('TrainIterLoss/MelLoss', mel_loss.data[0], current_step)
tb.add_scalar('TrainIterLoss/AttnLoss', attention_loss.data[0], current_step)
tb.add_scalar('Params/LearningRate', optimizer.param_groups[0]['lr'],
current_step)
tb.add_scalar('Params/GradNorm', grad_norm, current_step)
@ -181,12 +198,14 @@ def train(model, criterion, data_loader, optimizer, epoch):
avg_linear_loss /= (num_iter + 1)
avg_mel_loss /= (num_iter + 1)
avg_attn_loss /= (num_iter + 1)
avg_total_loss = avg_mel_loss + avg_linear_loss
# Plot Training Epoch Stats
tb.add_scalar('TrainEpochLoss/TotalLoss', avg_total_loss, current_step)
tb.add_scalar('TrainEpochLoss/LinearLoss', avg_linear_loss, current_step)
tb.add_scalar('TrainEpochLoss/MelLoss', avg_mel_loss, current_step)
tb.add_scalar('TrainEpochLoss/AttnLoss', avg_attn_loss, current_step)
tb.add_scalar('Time/EpochTime', epoch_time, epoch)
epoch_time = 0