Medix-AI/coqui-tts
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coqui-tts
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remove stop token prediciton

This commit is contained in:
Eren Golge 2018-03-22 12:47:54 -07:00
parent b9fbdfa7ce
commit 802c1cc5b4
3 changed files with 28 additions and 58 deletions
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34
layers/custom_layers.py
View File

@ -4,23 +4,23 @@ from torch.autograd import Variable
from torch import nn
class StopProjection(nn.Module):
r""" Simple projection layer to predict the "stop token"
# class StopProjection(nn.Module):
# r""" Simple projection layer to predict the "stop token"
Args:
in_features (int): size of the input vector
out_features (int or list): size of each output vector. aka number
of predicted frames.
"""
# Args:
# in_features (int): size of the input vector
# out_features (int or list): size of each output vector. aka number
# of predicted frames.
# """
def __init__(self, in_features, out_features):
super(StopProjection, self).__init__()
self.linear = nn.Linear(in_features, out_features)
self.dropout = nn.Dropout(0.5)
self.sigmoid = nn.Sigmoid()
# def __init__(self, in_features, out_features):
# super(StopProjection, self).__init__()
# self.linear = nn.Linear(in_features, out_features)
# self.dropout = nn.Dropout(0.5)
# self.sigmoid = nn.Sigmoid()
def forward(self, inputs):
out = self.dropout(inputs)
out = self.linear(out)
out = self.sigmoid(out)
return out
# def forward(self, inputs):
# out = self.dropout(inputs)
# out = self.linear(out)
# out = self.sigmoid(out)
# return out

12
layers/tacotron.py
View File

@ -5,7 +5,6 @@ from torch import nn
from .attention import AttentionRNN
from .attention import get_mask_from_lengths
from .custom_layers import StopProjection
class Prenet(nn.Module):
r""" Prenet as explained at https://arxiv.org/abs/1703.10135.
@ -233,8 +232,6 @@ class Decoder(nn.Module):
[nn.GRUCell(256, 256) for _ in range(2)])
# RNN_state -> |Linear| -> mel_spec
self.proj_to_mel = nn.Linear(256, memory_dim * r)
# RNN_state | attention_context -> |Linear| -> stop_token
self.stop_token = StopProjection(256 + in_features, r)
def forward(self, inputs, memory=None):
"""
@ -286,7 +283,6 @@ class Decoder(nn.Module):
outputs = []
alignments = []
stop_outputs = []
t = 0
memory_input = initial_memory
@ -323,18 +319,13 @@ class Decoder(nn.Module):
decoder_input = decoder_rnn_hiddens[idx] + decoder_input
output = decoder_input
stop_token_input = decoder_input
# stop token prediction
stop_token_input = torch.cat((output, current_context_vec), -1)
stop_output = self.stop_token(stop_token_input)
# predict mel vectors from decoder vectors
output = self.proj_to_mel(output)
outputs += [output]
alignments += [alignment]
stop_outputs += [stop_output]
t += 1
@ -354,9 +345,8 @@ class Decoder(nn.Module):
# Back to batch first
alignments = torch.stack(alignments).transpose(0, 1)
outputs = torch.stack(outputs).transpose(0, 1).contiguous()
stop_outputs = torch.stack(stop_outputs).transpose(0, 1).contiguous()
return outputs, alignments, stop_outputs
return outputs, alignments
def is_end_of_frames(output, eps=0.2): #0.2

40
train.py
View File

@ -63,12 +63,11 @@ def signal_handler(signal, frame):
sys.exit(1)
def train(model, criterion, critetion_stop, data_loader, optimizer, epoch):
def train(model, criterion, data_loader, optimizer, epoch):
model = model.train()
epoch_time = 0
avg_linear_loss = 0
avg_mel_loss = 0
avg_stop_loss = 0
print(" | > Epoch {}/{}".format(epoch, c.epochs))
progbar = Progbar(len(data_loader.dataset) / c.batch_size)
@ -81,7 +80,6 @@ def train(model, criterion, critetion_stop, data_loader, optimizer, epoch):
text_lengths = data[1]
linear_input = data[2]
mel_input = data[3]
stop_targets = data[4]
current_step = num_iter + args.restore_step + epoch * len(data_loader) + 1
@ -95,7 +93,6 @@ def train(model, criterion, critetion_stop, data_loader, optimizer, epoch):
# convert inputs to variables
text_input_var = Variable(text_input)
mel_spec_var = Variable(mel_input)
stop_targets_var = Variable(stop_targets)
linear_spec_var = Variable(linear_input, volatile=True)
# sort sequence by length for curriculum learning
@ -112,10 +109,9 @@ def train(model, criterion, critetion_stop, data_loader, optimizer, epoch):
text_input_var = text_input_var.cuda()
mel_spec_var = mel_spec_var.cuda()
linear_spec_var = linear_spec_var.cuda()
stop_targets_var = stop_targets_var.cuda()
# forward pass
mel_output, linear_output, alignments, stop_output =\
mel_output, linear_output, alignments =\
model.forward(text_input_var, mel_spec_var)
# loss computation
@ -123,8 +119,7 @@ def train(model, criterion, critetion_stop, data_loader, optimizer, epoch):
linear_loss = 0.5 * criterion(linear_output, linear_spec_var) \
+ 0.5 * criterion(linear_output[:, :, :n_priority_freq],
linear_spec_var[: ,: ,:n_priority_freq])
stop_loss = critetion_stop(stop_output, stop_targets_var)
loss = mel_loss + linear_loss + 0.25*stop_loss
loss = mel_loss + linear_loss
# backpass and check the grad norm
loss.backward()
@ -141,7 +136,6 @@ def train(model, criterion, critetion_stop, data_loader, optimizer, epoch):
# update
progbar.update(num_iter+1, values=[('total_loss', loss.data[0]),
('linear_loss', linear_loss.data[0]),
('stop_loss', stop_loss.data[0]),
('mel_loss', mel_loss.data[0]),
('grad_norm', grad_norm)])
@ -150,7 +144,6 @@ def train(model, criterion, critetion_stop, data_loader, optimizer, epoch):
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/StopLoss', stop_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)
@ -191,21 +184,19 @@ def train(model, criterion, critetion_stop, data_loader, optimizer, epoch):
avg_linear_loss /= (num_iter + 1)
avg_mel_loss /= (num_iter + 1)
avg_stop_loss /= (num_iter + 1)
avg_total_loss = avg_mel_loss + avg_linear_loss + 0.25*avg_stop_loss
avg_total_loss = avg_mel_loss + avg_linear_loss
# Plot Training Epoch Stats
tb.add_scalar('TrainEpochLoss/TotalLoss', loss.data[0], current_step)
tb.add_scalar('TrainEpochLoss/LinearLoss', linear_loss.data[0], current_step)
tb.add_scalar('TrainEpochLoss/MelLoss', mel_loss.data[0], current_step)
tb.add_scalar('TrainEpochLoss/StopLoss', stop_loss.data[0], current_step)
tb.add_scalar('Time/EpochTime', epoch_time, epoch)
epoch_time = 0
return avg_linear_loss, current_step
def evaluate(model, criterion, criterion_stop, data_loader, current_step):
def evaluate(model, criterion, data_loader, current_step):
model = model.eval()
epoch_time = 0
@ -215,7 +206,6 @@ def evaluate(model, criterion, criterion_stop, data_loader, current_step):
avg_linear_loss = 0
avg_mel_loss = 0
avg_stop_loss = 0
for num_iter, data in enumerate(data_loader):
start_time = time.time()
@ -225,44 +215,38 @@ def evaluate(model, criterion, criterion_stop, data_loader, current_step):
text_lengths = data[1]
linear_input = data[2]
mel_input = data[3]
stop_targets = data[4]
# convert inputs to variables
text_input_var = Variable(text_input)
mel_spec_var = Variable(mel_input)
linear_spec_var = Variable(linear_input, volatile=True)
stop_targets_var = Variable(stop_targets)
# dispatch data to GPU
if use_cuda:
text_input_var = text_input_var.cuda()
mel_spec_var = mel_spec_var.cuda()
linear_spec_var = linear_spec_var.cuda()
stop_targets_var = stop_targets_var.cuda()
# forward pass
mel_output, linear_output, alignments, stop_output = model.forward(text_input_var, mel_spec_var)
mel_output, linear_output, alignments = model.forward(text_input_var, mel_spec_var)
# loss computation
mel_loss = criterion(mel_output, mel_spec_var)
linear_loss = 0.5 * criterion(linear_output, linear_spec_var) \
+ 0.5 * criterion(linear_output[:, :, :n_priority_freq],
linear_spec_var[: ,: ,:n_priority_freq])
stop_loss = criterion_stop(stop_output, stop_targets_var)
loss = mel_loss + linear_loss + 0.25*stop_loss
loss = mel_loss + linear_loss
step_time = time.time() - start_time
epoch_time += step_time
# update
progbar.update(num_iter+1, values=[('total_loss', loss.data[0]),
('stop_loss', stop_loss.data[0]),
('linear_loss', linear_loss.data[0]),
('mel_loss', mel_loss.data[0])])
avg_linear_loss += linear_loss.data[0]
avg_mel_loss += mel_loss.data[0]
avg_stop_loss += stop_loss.data[0]
# Diagnostic visualizations
idx = np.random.randint(mel_input.shape[0])
@ -294,14 +278,12 @@ def evaluate(model, criterion, criterion_stop, data_loader, current_step):
# compute average losses
avg_linear_loss /= (num_iter + 1)
avg_mel_loss /= (num_iter + 1)
avg_stop_loss /= (num_iter + 1)
avg_total_loss = avg_mel_loss + avg_linear_loss + 0.25*avg_stop_loss
avg_total_loss = avg_mel_loss + avg_linear_loss
# Plot Learning Stats
tb.add_scalar('ValEpochLoss/TotalLoss', avg_total_loss, current_step)
tb.add_scalar('ValEpochLoss/LinearLoss', avg_linear_loss, current_step)
tb.add_scalar('ValEpochLoss/MelLoss', avg_mel_loss, current_step)
tb.add_scalar('ValEpochLoss/StopLoss', avg_stop_loss, current_step)
return avg_linear_loss
@ -359,10 +341,8 @@ def main(args):
if use_cuda:
criterion = nn.L1Loss().cuda()
criterion_stop = nn.BCELoss().cuda()
else:
criterion = nn.L1Loss()
criterion_stop = nn.BCELoss()
if args.restore_path:
checkpoint = torch.load(args.restore_path)
@ -390,8 +370,8 @@ def main(args):
best_loss = float('inf')
for epoch in range(0, c.epochs):
train_loss, current_step = train(model, criterion, criterion_stop, train_loader, optimizer, epoch)
val_loss = evaluate(model, criterion, criterion_stop, val_loader, current_step)
train_loss, current_step = train(model, criterion, train_loader, optimizer, epoch)
val_loss = evaluate(model, criterion, val_loader, current_step)
best_loss = save_best_model(model, optimizer, val_loss,
best_loss, OUT_PATH,
current_step, epoch)