Spaces:
Running
Running
File size: 10,192 Bytes
b3fa29f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 |
import librosa.display as lbd
import matplotlib.pyplot as plt
import torch
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.path_to_transcript_dicts import *
from Utility.utils import cumsum_durations
from Utility.utils import delete_old_checkpoints
from Utility.utils import get_most_recent_checkpoint
def train_loop(net,
datasets,
device,
save_directory,
batch_size,
steps,
steps_per_checkpoint,
lr,
path_to_checkpoint,
resume=False,
warmup_steps=4000):
# ============
# Preparations
# ============
net = net.to(device)
torch.multiprocessing.set_sharing_strategy('file_system')
train_loaders = list()
train_iters = list()
for dataset in datasets:
train_loaders.append(DataLoader(batch_size=batch_size,
dataset=dataset,
drop_last=True,
num_workers=2,
pin_memory=True,
shuffle=True,
prefetch_factor=5,
collate_fn=collate_and_pad,
persistent_workers=True))
train_iters.append(iter(train_loaders[-1]))
default_embeddings = {"en": None, "de": None, "el": None, "es": None, "fi": None, "ru": None, "hu": None, "nl": None, "fr": None}
for index, lang in enumerate(["en", "de", "el", "es", "fi", "ru", "hu", "nl", "fr"]):
default_embedding = None
for datapoint in datasets[index]:
if default_embedding is None:
default_embedding = datapoint[7].squeeze()
else:
default_embedding = default_embedding + datapoint[7].squeeze()
default_embeddings[lang] = (default_embedding / len(datasets[index])).to(device)
optimizer = torch.optim.RAdam(net.parameters(), lr=lr, eps=1.0e-06, weight_decay=0.0)
grad_scaler = GradScaler()
scheduler = WarmupScheduler(optimizer, warmup_steps=warmup_steps)
if resume:
previous_checkpoint = get_most_recent_checkpoint(checkpoint_dir=save_directory)
if previous_checkpoint is not None:
path_to_checkpoint = previous_checkpoint
else:
raise RuntimeError(f"No checkpoint found that can be resumed from in {save_directory}")
step_counter = 0
train_losses_total = list()
if path_to_checkpoint is not None:
check_dict = torch.load(os.path.join(path_to_checkpoint), map_location=device)
net.load_state_dict(check_dict["model"])
if resume:
optimizer.load_state_dict(check_dict["optimizer"])
step_counter = check_dict["step_counter"]
grad_scaler.load_state_dict(check_dict["scaler"])
scheduler.load_state_dict(check_dict["scheduler"])
if step_counter > steps:
print("Desired steps already reached in loaded checkpoint.")
return
net.train()
# =============================
# Actual train loop starts here
# =============================
for step in tqdm(range(step_counter, steps)):
batches = []
for index in range(len(datasets)):
# we get one batch for each task (i.e. language in this case)
try:
batch = next(train_iters[index])
batches.append(batch)
except StopIteration:
train_iters[index] = iter(train_loaders[index])
batch = next(train_iters[index])
batches.append(batch)
train_loss = 0.0
for batch in batches:
with autocast():
# we sum the loss for each task, as we would do for the
# second order regular MAML, but we do it only over one
# step (i.e. iterations of inner loop = 1)
train_loss = 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)
# then we directly update our meta-parameters without
# the need for any task specific parameters
train_losses_total.append(train_loss.item())
optimizer.zero_grad()
grad_scaler.scale(train_loss).backward()
grad_scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(net.parameters(), 1.0, error_if_nonfinite=False)
grad_scaler.step(optimizer)
grad_scaler.update()
scheduler.step()
if step % steps_per_checkpoint == 0:
# ==============================
# Enough steps for some insights
# ==============================
net.eval()
print(f"Total Loss: {round(sum(train_losses_total) / len(train_losses_total), 3)}")
train_losses_total = list()
torch.save({
"model" : net.state_dict(),
"optimizer" : optimizer.state_dict(),
"scaler" : grad_scaler.state_dict(),
"scheduler" : scheduler.state_dict(),
"step_counter": step,
"default_emb" : default_embeddings["en"]
},
os.path.join(save_directory, "checkpoint_{}.pt".format(step)))
delete_old_checkpoints(save_directory, keep=5)
for lang in ["en", "de", "el", "es", "fi", "ru", "hu", "nl", "fr"]:
plot_progress_spec(net=net,
device=device,
lang=lang,
save_dir=save_directory,
step=step,
utt_embeds=default_embeddings)
net.train()
@torch.inference_mode()
def plot_progress_spec(net, device, save_dir, step, lang, utt_embeds):
tf = ArticulatoryCombinedTextFrontend(language=lang)
sentence = ""
default_embed = utt_embeds[lang]
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_embed,
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"), f"{step}_{lang}.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]))
|