Spaces:
Runtime error
Runtime error
File size: 8,377 Bytes
2cb106d |
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 |
import os
import random
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.nn.utils.rnn import pad_sequence
from tqdm import tqdm
from Preprocessing.ArticulatoryCombinedTextFrontend import ArticulatoryCombinedTextFrontend
from TrainingInterfaces.Text_to_Spectrogram.AutoAligner.Aligner import Aligner
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
def plot_progress_spec(net, device, save_dir, step, lang):
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)
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 train_loop(net,
train_sentences,
device,
save_directory,
aligner_checkpoint,
batch_size=32,
steps=300000,
epochs_per_save=5,
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 and of the train sentences
net: Model to train
train_sentences: list of (string) sentences the CTC objective should be learned on
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')
text_to_art_vec = ArticulatoryCombinedTextFrontend(language=lang)
asr_aligner = Aligner().to(device)
check_dict = torch.load(os.path.join(aligner_checkpoint), map_location=device)
asr_aligner.load_state_dict(check_dict["asr_model"])
net.stop_gradient_from_energy_predictor = False
net.stop_gradient_from_pitch_predictor = False
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()
random.shuffle(train_sentences)
batch_of_text_vecs = list()
batch_of_tokens = list()
for sentence in tqdm(train_sentences):
if sentence.strip() == "":
continue
phonemes = text_to_art_vec.get_phone_string(sentence)
# collect batch of texts
batch_of_text_vecs.append(text_to_art_vec.string_to_tensor(phonemes, input_phonemes=True).squeeze(0).to(device))
# collect batch of tokens
tokens = list()
for phone in phonemes:
tokens.append(text_to_art_vec.phone_to_id[phone])
tokens = torch.LongTensor(tokens).to(device)
batch_of_tokens.append(tokens)
if len(batch_of_tokens) == batch_size:
token_batch = pad_sequence(batch_of_tokens, batch_first=True)
token_lens = torch.LongTensor([len(x) for x in batch_of_tokens]).to(device)
text_batch = pad_sequence(batch_of_text_vecs, batch_first=True)
spec_batch, d_outs = net.batch_inference(texts=text_batch, text_lens=token_lens)
spec_lens = torch.LongTensor([sum(x) for x in d_outs]).to(device)
asr_pred = asr_aligner(spec_batch, spec_lens)
train_loss = asr_aligner.ctc_loss(asr_pred.transpose(0, 1).log_softmax(2), token_batch, spec_lens, token_lens)
train_losses_this_epoch.append(train_loss.item())
optimizer.zero_grad()
asr_aligner.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()
batch_of_tokens = list()
batch_of_text_vecs = list()
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(),
}, os.path.join(save_directory, "checkpoint_{}.pt".format(step_counter)))
delete_old_checkpoints(save_directory, keep=5)
with torch.no_grad():
plot_progress_spec(net, device, save_dir=save_directory, step=step_counter, lang=lang)
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()
|