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Trainer.py
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Trainer.py
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import torch
from torch import nn
from os.path import join
from tqdm import tqdm
from lrschedule import noam_learning_rate_decay
from util import logit, masked_mean, sequence_mask, prepare_spec_image
import audio
import numpy as np
from warnings import warn
class MaskedL1Loss(nn.Module):
def __init__(self):
super(MaskedL1Loss, self).__init__()
self.criterion = nn.L1Loss(size_average=False)
def forward(self, input, target, lengths=None, mask=None, max_len=None):
if lengths is None and mask is None:
raise RuntimeError("Should provide either lengths or mask")
# (B, T, 1)
if mask is None:
mask = sequence_mask(lengths, max_len).unsqueeze(-1)
raise RuntimeError("Mask is None")
# (B, T, D)
mask_ = mask.expand_as(input)
loss = self.criterion(input * mask_, target * mask_)
return loss / mask_.sum()
class Trainer:
def __init__(self, model, train_loader, valid_loader, optimizer, writer, checkpoint_dir, device, hparams):
self.model = model
self.hparams = hparams
self.device = device
self.checkpoint_dir = checkpoint_dir
self.train_loader = train_loader
self.valid_loader = valid_loader
self.writer = writer
self.optimizer = optimizer
self.checkpoint_interval = hparams.checkpoint_interval
self.eval_interval = hparams.eval_interval
self.w = hparams.binary_divergence_weight
self.epoch = hparams.nepochs
self.fs = hparams.sample_rate
def spec_loss(self, y_hat, y, mask, priority_bin=None, priority_w=0):
masked_l1 = MaskedL1Loss()
l1 = nn.L1Loss()
w = self.hparams.masked_loss_weight
# L1 loss
if w > 0:
assert mask is not None
l1_loss = w * masked_l1(y_hat, y, mask=mask) + (1 - w) * l1(y_hat, y)
else:
assert mask is None
l1_loss = l1(y_hat, y)
# Priority L1 loss
if priority_bin is not None and priority_w > 0:
if w > 0:
priority_loss = w * masked_l1(
y_hat[:, :, :priority_bin], y[:, :, :priority_bin], mask=mask) \
+ (1 - w) * l1(y_hat[:, :, :priority_bin], y[:, :, :priority_bin])
else:
priority_loss = l1(y_hat[:, :, :priority_bin], y[:, :, :priority_bin])
l1_loss = (1 - priority_w) * l1_loss + priority_w * priority_loss
# Binary divergence loss
if self.w <= 0:
binary_div = y.data.new(1).zero_()
else:
y_hat_logits = logit(y_hat)
z = -y * y_hat_logits + torch.log1p(torch.exp(y_hat_logits))
if w > 0:
binary_div = w * masked_mean(z, mask) + (1 - w) * z.mean()
else:
binary_div = z.mean()
return l1_loss, binary_div
def train(self, train_seq2seq, train_postnet, global_epoch=1, global_step=0):
while global_epoch < self.epoch:
running_loss = 0.
running_linear_loss = 0.
running_mel_loss = 0.
for step, (ling, mel, linear, lengths, speaker_ids) in enumerate(tqdm(self.train_loader)):
self.model.train()
ismultispeaker = speaker_ids is not None
# Learn rate scheduler
current_lr = noam_learning_rate_decay(self.hparams.initial_learning_rate, global_step)
for param_group in self.optimizer.param_groups:
param_group['lr'] = current_lr
self.optimizer.zero_grad()
# Transform data to CUDA device
if train_seq2seq :
ling = ling.to(self.device)
mel = mel.to(self.device)
if train_postnet :
linear = linear.to(self.device)
lengths = lengths.to(self.device)
speaker_ids = speaker_ids.to(self.device) if ismultispeaker else None
target_mask = sequence_mask(lengths, max_len=mel.size(1)).unsqueeze(-1)
# Apply model
if train_seq2seq and train_postnet:
_, mel_outputs, linear_outputs = self.model(ling, mel, speaker_ids=speaker_ids)
#elif train_seq2seq:
# mel_style = self.model.gst(tmel)
# style_embed = mel_style.expand_as(smel)
# mel_input = smel + style_embed
# mel_outputs = self.model.seq2seq(mel_input)
# linear_outputs = None
#elif train_postnet:
# linear_outputs = self.model.postnet(smel)
# mel_outputs = None
# Losses
if train_seq2seq:
mel_l1_loss, mel_binary_div = self.spec_loss(mel_outputs, mel, target_mask)
mel_loss = (1 - self.w) * mel_l1_loss + self.w * mel_binary_div
if train_postnet:
linear_l1_loss, linear_binary_div = self.spec_loss(linear_outputs, linear, target_mask)
linear_loss = (1 - self.w) * linear_l1_loss + self.w * linear_binary_div
# Combine losses
if train_seq2seq and train_postnet:
loss = mel_loss + linear_loss
elif train_seq2seq:
loss = mel_loss
elif train_postnet:
loss = linear_loss
# Update
loss.backward()
self.optimizer.step()
# Logs
if train_seq2seq:
self.writer.add_scalar("mel loss", float(mel_loss.item()), global_step)
self.writer.add_scalar("mel_l1_loss", float(mel_l1_loss.item()), global_step)
self.writer.add_scalar("mel_binary_div_loss", float(mel_binary_div.item()), global_step)
if train_postnet:
self.writer.add_scalar("linear_loss", float(linear_loss.item()), global_step)
self.writer.add_scalar("linear_l1_loss", float(linear_l1_loss.item()), global_step)
self.writer.add_scalar("linear_binary_div_loss", float(
linear_binary_div.item()), global_step)
self.writer.add_scalar("loss", float(loss.item()), global_step)
self.writer.add_scalar("learning rate", current_lr, global_step)
global_step += 1
running_loss += loss.item()
running_linear_loss += linear_loss.item()
running_mel_loss += mel_loss.item()
if (global_epoch % self.checkpoint_interval == 0):
self.save_checkpoint(global_step, global_epoch)
if global_epoch % self.eval_interval == 0:
self.save_states(global_epoch, mel_outputs, linear_outputs, ling, mel, linear, lengths)
self.eval_model(global_epoch, train_seq2seq, train_postnet)
avg_loss = running_loss / len(self.train_loader)
avg_linear_loss = running_linear_loss / len(self.train_loader)
avg_mel_loss = running_mel_loss / len(self.train_loader)
self.writer.add_scalar("train loss (per epoch)", avg_loss, global_epoch)
self.writer.add_scalar("train linear loss (per epoch)", avg_linear_loss, global_epoch)
self.writer.add_scalar("train mel loss (per epoch)", avg_mel_loss, global_epoch)
print("Train Loss: {}".format(avg_loss))
global_epoch += 1
def eval_model(self, global_epoch, train_seq2seq, train_postnet):
happy_ref = np.load('../feat/Acoustic_frame/mel/emc00103.npy')
happy_ref = torch.from_numpy(happy_ref).unsqueeze(0)
sad_ref = np.load('../feat/Acoustic_frame/mel/ema00203.npy')
sad_ref = torch.from_numpy(sad_ref).unsqueeze(0)
angry_ref = np.load('../feat/Acoustic_frame/mel/eme00303.npy')
angry_ref = torch.from_numpy(angry_ref).unsqueeze(0)
running_loss = 0.
running_linear_loss = 0.
running_mel_loss = 0.
for step, (ling, mel, linear, lengths, speaker_ids) in enumerate(self.valid_loader):
self.model.eval()
ismultispeaker = speaker_ids is not None
if train_seq2seq:
ling = ling.to(self.device)
mel = mel.to(self.device)
happy_ref = happy_ref.to(self.device)
sad_ref = sad_ref.to(self.device)
angry_ref = angry_ref.to(self.device)
if train_postnet:
linear = linear.to(self.device)
lengths = lengths.to(self.device)
speaker_ids = speaker_ids.to(self.device) if ismultispeaker else None
target_mask = sequence_mask(lengths, max_len=mel.size(1)).unsqueeze(-1)
with torch.no_grad():
# Apply model
if train_seq2seq and train_postnet:
_, mel_outputs, linear_outputs = self.model(ling, mel, speaker_ids=speaker_ids)
"""
elif train_seq2seq:
mel_style = self.model.gst(tmel)
style_embed = mel_style.expand_as(smel)
mel_input = smel + style_embed
mel_outputs = self.model.seq2seq(mel_input)
linear_outputs = None
elif train_postnet:
linear_outputs = self.model.postnet(tmel)
mel_outputs = None
"""
# Losses
if train_seq2seq:
mel_l1_loss, mel_binary_div = self.spec_loss(mel_outputs, mel, target_mask)
mel_loss = (1 - self.w) * mel_l1_loss + self.w * mel_binary_div
if train_postnet:
linear_l1_loss, linear_binary_div = self.spec_loss(linear_outputs, linear, target_mask)
linear_loss = (1 - self.w) * linear_l1_loss + self.w * linear_binary_div
# Combine losses
if train_seq2seq and train_postnet:
loss = mel_loss + linear_loss
elif train_seq2seq:
loss = mel_loss
elif train_postnet:
loss = linear_loss
running_loss += loss.item()
running_linear_loss += linear_loss.item()
running_mel_loss += mel_loss.item()
B = ling.size(0)
if ismultispeaker :
speaker_ids = np.zeros(B)
speaker_ids = torch.LongTensor(speaker_ids).to(self.device)
else :
speaker_ids = None
_, happy_mel_outputs, happy_linear_outputs = self.model(ling, happy_ref, speaker_ids)
_, sad_mel_outputs, sad_linear_outputs = self.model(ling, sad_ref, speaker_ids)
_, angry_mel_outputs, angry_linear_outputs = self.model(ling, angry_ref, speaker_ids)
if global_epoch % self.eval_interval == 0:
for idx in range(B):
if mel_outputs is not None:
happy_mel_output = happy_mel_outputs[idx].cpu().data.numpy()
happy_mel_output = prepare_spec_image(audio._denormalize(happy_mel_output))
self.writer.add_image("(Eval) Happy mel spectrogram {}".format(idx), happy_mel_output, global_epoch)
sad_mel_output = sad_mel_outputs[idx].cpu().data.numpy()
sad_mel_output = prepare_spec_image(audio._denormalize(sad_mel_output))
self.writer.add_image("(Eval) Sad mel spectrogram {}".format(idx), sad_mel_output, global_epoch)
angry_mel_output = angry_mel_outputs[idx].cpu().data.numpy()
angry_mel_output = prepare_spec_image(audio._denormalize(angry_mel_output))
self.writer.add_image("(Eval) Angry mel spectrogram {}".format(idx), angry_mel_output, global_epoch)
mel_output = mel_outputs[idx].cpu().data.numpy()
mel_output = prepare_spec_image(audio._denormalize(mel_output))
self.writer.add_image("(Eval) Predicted mel spectrogram {}".format(idx), mel_output, global_epoch)
mel1 = mel[idx].cpu().data.numpy()
mel1 = prepare_spec_image(audio._denormalize(mel1))
self.writer.add_image("(Eval) Source mel spectrogram {}".format(idx), mel1, global_epoch)
if linear_outputs is not None:
linear_output = linear_outputs[idx].cpu().data.numpy()
spectrogram = prepare_spec_image(audio._denormalize(linear_output))
self.writer.add_image("(Eval) Predicted spectrogram {}".format(idx), spectrogram, global_epoch)
signal = audio.inv_spectrogram(linear_output.T)
signal /= np.max(np.abs(signal))
path = join(self.checkpoint_dir, "epoch{:09d}_{}_predicted.wav".format(global_epoch, idx))
audio.save_wav(signal, path)
try:
self.writer.add_audio("(Eval) Predicted audio signal {}".format(idx), signal, global_epoch, sample_rate=self.fs)
except Exception as e:
warn(str(e))
pass
happy_linear_output = happy_linear_outputs[idx].cpu().data.numpy()
spectrogram = prepare_spec_image(audio._denormalize(happy_linear_output))
self.writer.add_image("(Eval) Happy spectrogram {}".format(idx), spectrogram, global_epoch)
signal = audio.inv_spectrogram(happy_linear_output.T)
signal /= np.max(np.abs(signal))
path = join(self.checkpoint_dir, "epoch{:09d}_{}_happy.wav".format(global_epoch, idx))
audio.save_wav(signal, path)
try:
self.writer.add_audio("(Eval) Happy audio signal {}".format(idx), signal, global_epoch, sample_rate=self.fs)
except Exception as e:
warn(str(e))
pass
angry_linear_output = angry_linear_outputs[idx].cpu().data.numpy()
spectrogram = prepare_spec_image(audio._denormalize(angry_linear_output))
self.writer.add_image("(Eval) Angry spectrogram {}".format(idx), spectrogram, global_epoch)
signal = audio.inv_spectrogram(angry_linear_output.T)
signal /= np.max(np.abs(signal))
path = join(self.checkpoint_dir, "epoch{:09d}_{}_angry.wav".format(global_epoch, idx))
audio.save_wav(signal, path)
try:
self.writer.add_audio("(Eval) Angry audio signal {}".format(idx), signal, global_epoch, sample_rate=self.fs)
except Exception as e:
warn(str(e))
pass
sad_linear_output = sad_linear_outputs[idx].cpu().data.numpy()
spectrogram = prepare_spec_image(audio._denormalize(sad_linear_output))
self.writer.add_image("(Eval) Sad spectrogram {}".format(idx), spectrogram, global_epoch)
signal = audio.inv_spectrogram(sad_linear_output.T)
signal /= np.max(np.abs(signal))
path = join(self.checkpoint_dir, "epoch{:09d}_{}_sad.wav".format(global_epoch, idx))
audio.save_wav(signal, path)
try:
self.writer.add_audio("(Eval) Sad audio signal {}".format(idx), signal, global_epoch, sample_rate=self.fs)
except Exception as e:
warn(str(e))
pass
linear1 = linear[idx].cpu().data.numpy()
spectrogram = prepare_spec_image(audio._denormalize(linear1))
self.writer.add_image("(Eval) Target spectrogram {}".format(idx), spectrogram, global_epoch)
signal = audio.inv_spectrogram(linear1.T)
signal /= np.max(np.abs(signal))
try:
self.writer.add_audio("(Eval) Target audio signal {}".format(idx), signal, global_epoch, sample_rate=self.fs)
except Exception as e:
warn(str(e))
pass
avg_loss = running_loss / len(self.valid_loader)
avg_linear_loss = running_linear_loss / len(self.valid_loader)
avg_mel_loss = running_mel_loss / len(self.valid_loader)
self.writer.add_scalar("valid loss (per epoch)", avg_loss, global_epoch)
self.writer.add_scalar("valid linear loss (per epoch)", avg_linear_loss, global_epoch)
self.writer.add_scalar("valid mel loss (per epoch)", avg_mel_loss, global_epoch)
print("Valid Loss: {}".format(avg_loss))
def save_states(self, global_epoch, mel_outputs, linear_outputs, ling, mel, linear, lengths):
print("Save intermediate states at epoch {}".format(global_epoch))
# idx = np.random.randint(0, len(input_lengths))
idx = min(1, len(lengths) - 1)
# Predicted mel spectrogram
if mel_outputs is not None:
mel_output = mel_outputs[idx].cpu().data.numpy()
mel_output = prepare_spec_image(audio._denormalize(mel_output))
self.writer.add_image("Predicted mel spectrogram", mel_output, global_epoch)
# Predicted spectrogram
if linear_outputs is not None:
linear_output = linear_outputs[idx].cpu().data.numpy()
spectrogram = prepare_spec_image(audio._denormalize(linear_output))
self.writer.add_image("Predicted spectrogram", spectrogram, global_epoch)
# Predicted audio signal
signal = audio.inv_spectrogram(linear_output.T)
signal /= np.max(np.abs(signal))
path = join(self.checkpoint_dir, "epoch{:09d}_predicted.wav".format(global_epoch))
try:
self.writer.add_audio("Predicted audio signal", signal, global_epoch, sample_rate=self.fs)
except Exception as e:
warn(str(e))
pass
audio.save_wav(signal, path)
# Target mel spectrogram
if mel_outputs is not None:
#ling = ling[idx].cpu().data.numpy()
#mel = prepare_spec_image(audio._denormalize(mel))
#self.writer.add_image("Source mel spectrogram", ling, global_epoch)
mel = mel[idx].cpu().data.numpy()
mel = prepare_spec_image(audio._denormalize(mel))
self.writer.add_image("Target mel spectrogram", mel, global_epoch)
if linear_outputs is not None:
linear = linear[idx].cpu().data.numpy()
spectrogram = prepare_spec_image(audio._denormalize(linear))
self.writer.add_image("Target spectrogram", spectrogram, global_epoch)
# Target audio signal
signal = audio.inv_spectrogram(linear.T)
signal /= np.max(np.abs(signal))
try:
self.writer.add_audio("Target audio signal", signal, global_epoch, sample_rate=self.fs)
except Exception as e:
warn(str(e))
pass
def save_checkpoint(self, global_step, global_epoch):
checkpoint_path = join(self.checkpoint_dir, "checkpoint_epoch{:09d}.pth".format(global_epoch))
torch.save({
"state_dict": self.model.state_dict(),
"optimizer": self.optimizer.state_dict(),
"global_step": global_step,
"global_epoch": global_epoch,
}, checkpoint_path)
print("Saved checkpoint:", checkpoint_path)