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import os
import time
import librosa.display as lbd
import matplotlib.pyplot as plt
import torch
import torch.multiprocessing
import torch.multiprocessing
from torch.cuda.amp import GradScaler
from torch.cuda.amp import autocast
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
from Preprocessing.ArticulatoryCombinedTextFrontend import ArticulatoryCombinedTextFrontend
from Preprocessing.ArticulatoryCombinedTextFrontend import get_language_id
from Utility.WarmupScheduler import WarmupScheduler
from Utility.utils import cumsum_durations
from Utility.utils import delete_old_checkpoints
from Utility.utils import get_most_recent_checkpoint
@torch.no_grad()
def plot_progress_spec(net, device, save_dir, step, lang, default_emb):
tf = ArticulatoryCombinedTextFrontend(language=lang)
sentence = ""
if lang == "en":
sentence = "This is a complex sentence, it even has a pause!"
elif lang == "de":
sentence = "Dies ist ein komplexer Satz, er hat sogar eine Pause!"
elif lang == "el":
sentence = "Αυτή είναι μια σύνθετη πρόταση, έχει ακόμη και παύση!"
elif lang == "es":
sentence = "Esta es una oración compleja, ¡incluso tiene una pausa!"
elif lang == "fi":
sentence = "Tämä on monimutkainen lause, sillä on jopa tauko!"
elif lang == "ru":
sentence = "Это сложное предложение, в нем даже есть пауза!"
elif lang == "hu":
sentence = "Ez egy összetett mondat, még szünet is van benne!"
elif lang == "nl":
sentence = "Dit is een complexe zin, er zit zelfs een pauze in!"
elif lang == "fr":
sentence = "C'est une phrase complexe, elle a même une pause !"
phoneme_vector = tf.string_to_tensor(sentence).squeeze(0).to(device)
spec, durations, *_ = net.inference(text=phoneme_vector,
return_duration_pitch_energy=True,
utterance_embedding=default_emb,
lang_id=get_language_id(lang).to(device))
spec = spec.transpose(0, 1).to("cpu").numpy()
duration_splits, label_positions = cumsum_durations(durations.cpu().numpy())
if not os.path.exists(os.path.join(save_dir, "spec")):
os.makedirs(os.path.join(save_dir, "spec"))
fig, ax = plt.subplots(nrows=1, ncols=1)
lbd.specshow(spec,
ax=ax,
sr=16000,
cmap='GnBu',
y_axis='mel',
x_axis=None,
hop_length=256)
ax.yaxis.set_visible(False)
ax.set_xticks(duration_splits, minor=True)
ax.xaxis.grid(True, which='minor')
ax.set_xticks(label_positions, minor=False)
ax.set_xticklabels(tf.get_phone_string(sentence))
ax.set_title(sentence)
plt.savefig(os.path.join(os.path.join(save_dir, "spec"), str(step) + ".png"))
plt.clf()
plt.close()
def collate_and_pad(batch):
# text, text_len, speech, speech_len, durations, energy, pitch, utterance condition, language_id
return (pad_sequence([datapoint[0] for datapoint in batch], batch_first=True),
torch.stack([datapoint[1] for datapoint in batch]).squeeze(1),
pad_sequence([datapoint[2] for datapoint in batch], batch_first=True),
torch.stack([datapoint[3] for datapoint in batch]).squeeze(1),
pad_sequence([datapoint[4] for datapoint in batch], batch_first=True),
pad_sequence([datapoint[5] for datapoint in batch], batch_first=True),
pad_sequence([datapoint[6] for datapoint in batch], batch_first=True),
torch.stack([datapoint[7] for datapoint in batch]).squeeze(),
torch.stack([datapoint[8] for datapoint in batch]))
def train_loop(net,
train_dataset,
device,
save_directory,
batch_size=32,
steps=300000,
epochs_per_save=1,
lang="en",
lr=0.0001,
warmup_steps=4000,
path_to_checkpoint=None,
fine_tune=False,
resume=False):
"""
Args:
resume: whether to resume from the most recent checkpoint
warmup_steps: how long the learning rate should increase before it reaches the specified value
steps: How many steps to train
lr: The initial learning rate for the optimiser
path_to_checkpoint: reloads a checkpoint to continue training from there
fine_tune: whether to load everything from a checkpoint, or only the model parameters
lang: language of the synthesis
net: Model to train
train_dataset: Pytorch Dataset Object for train data
device: Device to put the loaded tensors on
save_directory: Where to save the checkpoints
batch_size: How many elements should be loaded at once
epochs_per_save: how many epochs to train in between checkpoints
"""
net = net.to(device)
torch.multiprocessing.set_sharing_strategy('file_system')
train_loader = DataLoader(batch_size=batch_size,
dataset=train_dataset,
drop_last=True,
num_workers=8,
pin_memory=True,
shuffle=True,
prefetch_factor=8,
collate_fn=collate_and_pad,
persistent_workers=True)
default_embedding = None
for index in range(20): # slicing is not implemented for datasets, so this detour is needed.
if default_embedding is None:
default_embedding = train_dataset[index][7].squeeze()
else:
default_embedding = default_embedding + train_dataset[index][7].squeeze()
default_embedding = (default_embedding / len(train_dataset)).to(device)
# default speaker embedding for inference is the average of the first 20 speaker embeddings. So if you use multiple datasets combined,
# put a single speaker one with the nicest voice first into the concat dataset.
step_counter = 0
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
scheduler = WarmupScheduler(optimizer, warmup_steps=warmup_steps)
scaler = GradScaler()
epoch = 0
if resume:
path_to_checkpoint = get_most_recent_checkpoint(checkpoint_dir=save_directory)
if path_to_checkpoint is not None:
check_dict = torch.load(path_to_checkpoint, map_location=device)
net.load_state_dict(check_dict["model"])
if not fine_tune:
optimizer.load_state_dict(check_dict["optimizer"])
scheduler.load_state_dict(check_dict["scheduler"])
step_counter = check_dict["step_counter"]
scaler.load_state_dict(check_dict["scaler"])
start_time = time.time()
while True:
net.train()
epoch += 1
optimizer.zero_grad()
train_losses_this_epoch = list()
for batch in tqdm(train_loader):
with autocast():
train_loss = net(text_tensors=batch[0].to(device),
text_lengths=batch[1].to(device),
gold_speech=batch[2].to(device),
speech_lengths=batch[3].to(device),
gold_durations=batch[4].to(device),
gold_pitch=batch[6].to(device), # mind the switched order
gold_energy=batch[5].to(device), # mind the switched order
utterance_embedding=batch[7].to(device),
lang_ids=batch[8].to(device),
return_mels=False)
train_losses_this_epoch.append(train_loss.item())
optimizer.zero_grad()
scaler.scale(train_loss).backward()
del train_loss
step_counter += 1
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(net.parameters(), 1.0, error_if_nonfinite=False)
scaler.step(optimizer)
scaler.update()
scheduler.step()
net.eval()
if epoch % epochs_per_save == 0:
torch.save({
"model" : net.state_dict(),
"optimizer" : optimizer.state_dict(),
"step_counter": step_counter,
"scaler" : scaler.state_dict(),
"scheduler" : scheduler.state_dict(),
"default_emb" : default_embedding,
}, os.path.join(save_directory, "checkpoint_{}.pt".format(step_counter)))
delete_old_checkpoints(save_directory, keep=5)
plot_progress_spec(net, device, save_dir=save_directory, step=step_counter, lang=lang, default_emb=default_embedding)
if step_counter > steps:
# DONE
return
print("Epoch: {}".format(epoch))
print("Train Loss: {}".format(sum(train_losses_this_epoch) / len(train_losses_this_epoch)))
print("Time elapsed: {} Minutes".format(round((time.time() - start_time) / 60)))
print("Steps: {}".format(step_counter))
net.train()
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