def augmentation(self):
""" Apply Spec-Augmentation """
augment_end_idx = int(0 + (
(len(self.audio_paths) - 0) * self.augment_ratio))
logger.info("Applying Augmentation...")
for idx in range(augment_end_idx):
self.augment_flags.append(True)
self.audio_paths.append(self.audio_paths[idx])
self.label_paths.append(self.label_paths[idx])
def load_pickle(filepath, message=""):
"""
load pickle file
Args:
filepath (str): Path to pickle file to load
message (str): message to print
Returns: load_result
-**load_result** : load result of pickle
"""
with open(filepath, "rb") as f:
load_result = pickle.load(f)
logger.info(message)
return load_result
def evaluate(model, queue, criterion, device):
r"""
Args:
model (torch.nn.Module): Model to be evaluated
queue (queue): queue for threading
criterion (torch.nn): one of PyTorch’s loss function. Refer to http://pytorch.org/docs/master/nn.html#loss-functions for a list of them.
device (torch.cuda): device used ('cuda' or 'cpu')
Returns: loss, cer
- **loss** (float): loss of evalution
- **cer** (float): character error rate
"""
logger.info('evaluate() start')
total_loss = 0.
total_num = 0
total_dist = 0
total_length = 0
total_sentence_num = 0
model.eval()
with torch.no_grad():
while True:
feats, scripts, feat_lengths, script_lengths = queue.get()
if feats.shape[0] == 0:
break
feats = feats.to(device)
scripts = scripts.to(device)
target = scripts[:, 1:]
model.module.flatten_parameters()
y_hat, logit = model(feats,
scripts,
teacher_forcing_ratio=0.0,
use_beam_search=False)
loss = criterion(logit.contiguous().view(-1, logit.size(-1)),
target.contiguous().view(-1))
total_loss += loss.item()
total_num += sum(feat_lengths)
dist, length = get_distance(target, y_hat, id2char, EOS_TOKEN)
total_dist += dist
total_length += length
total_sentence_num += target.size(0)
logger.info('evaluate() completed')
return total_loss / total_num, total_dist / total_length
def evaluate(model, queue, perplexity, device):
logger.info('evaluate() start')
total_loss = 0
total_num = 0
model.eval()
with torch.no_grad():
while True:
loss = perplexity
inputs, targets, input_lengths, target_lengths = queue.get()
if inputs.shape[0] == 0:
break
inputs = inputs.to(device)
targets = targets.to(device)
model.module.flatten_parameters()
outputs = model(inputs, teacher_forcing_ratio=0.0)
loss.reset()
for step, step_output in enumerate(outputs):
batch_size = targets.size(0)
loss.eval_batch(step_output.contiguous().view(batch_size, -1),
targets[:, step])
loss = loss.get_loss()
total_loss += loss
total_num += sum(input_lens)
logger.info('evaluate() completed')
return total_loss / total_num
def supervised_train(model, config, epoch, total_time_step, queue,
criterion, optimizer, device, train_begin, worker_num,
print_every=10, teacher_forcing_ratio=0.90):
r"""
Args:
train_begin: train begin time
total_time_step: total time step in epoch
epoch (int): present epoch
config (Config): configuration
model (torch.nn.Module): Model to be trained
optimizer (torch.optim): optimizer for training
teacher_forcing_ratio (float): The probability that teacher forcing will be used (default: 0.90)
print_every (int): Parameters to determine how many steps to output
queue (Queue.queue): queue for threading
criterion (torch.nn): one of PyTorch’s loss function.
Refer to http://pytorch.org/docs/master/nn.html#loss-functions for a list of them.
device (torch.cuda): device used ('cuda' or 'cpu')
worker_num (int): the number of cpu cores used
Returns: loss, cer
- **loss** (float): loss of present epoch
- **cer** (float): character error rate
"""
epoch_loss_total = 0.
print_loss_total = 0.
total_num = 0
total_dist = 0
total_length = 0
time_step = 0
decay_speed = 1.0
RAMPUP_POWER = 3
RANMPUP_PERIOD = 3000
EXP_DECAY_PERIOD = total_time_step * 3
model.train()
begin = epoch_begin = time.time()
while True:
# LR Wamp-Up
if config.use_multistep_lr and epoch == 0 and time_step < RANMPUP_PERIOD:
set_lr(optimizer, lr=config.high_plateau_lr * ((time_step + 1) / RANMPUP_PERIOD) ** RAMPUP_POWER)
# LR Exponential-Decay
if config.use_multistep_lr and (epoch == 1 or epoch == 2 or epoch == 3):
decay_rate = config.low_plateau_lr / config.high_plateau_lr
decay_speed *= decay_rate ** (1 / EXP_DECAY_PERIOD)
set_lr(optimizer, config.high_plateau_lr * decay_speed)
feats, scripts, feat_lens, target_lens = queue.get()
if feats.shape[0] == 0:
# empty feats means closing one loader
worker_num -= 1
logger.debug('left train_loader: %d' % worker_num)
if worker_num == 0:
break
else:
continue
inputs = feats.to(device)
scripts = scripts.to(device)
targets = scripts[:, 1:]
model.module.flatten_parameters()
y_hat, logit = model(inputs, scripts, teacher_forcing_ratio=teacher_forcing_ratio)
loss = criterion(logit.contiguous().view(-1, logit.size(-1)), targets.contiguous().view(-1))
epoch_loss_total += loss.item()
print_loss_total += loss.item()
total_num += sum(feat_lens)
dist, length = get_distance(targets, y_hat, id2char, EOS_TOKEN)
total_dist += dist
total_length += length
optimizer.zero_grad()
loss.backward()
optimizer.step()
time_step += 1
torch.cuda.empty_cache()
if time_step % print_every == 0:
current = time.time()
elapsed = current - begin
epoch_elapsed = (current - epoch_begin) / 60.0
train_elapsed = (current - train_begin) / 3600.0
logger.info('timestep: {:4d}/{:4d}, loss: {:.4f}, cer: {:.2f}, elapsed: {:.2f}s {:.2f}m {:.2f}h'.format(
time_step,
total_time_step,
print_loss_total / print_every,
total_dist / total_length,
elapsed, epoch_elapsed, train_elapsed)
)
print_loss_total = 0
begin = time.time()
if time_step % 1000 == 0:
save_step_result(train_step_result, epoch_loss_total / total_num, total_dist / total_length)
if time_step % 10000 == 0:
torch.save(model, "./data/weight_file/epoch_%s_step_%s.pt" % (str(epoch), str(time_step)))
logger.info('train() completed')
return epoch_loss_total / total_num, total_dist / total_length
def supervised_train(model,
hparams,
epoch,
total_time_step,
queue,
criterion,
optimizer,
device,
train_begin,
worker_num,
print_time_step=10,
teacher_forcing_ratio=0.90):
"""
Args:
model (torch.nn.Module): Model to be trained
optimizer (torch.optim): optimizer for training
teacher_forcing_ratio (float): The probability that teacher forcing will be used (default: 0.90)
print_time_step (int): Parameters to determine how many steps to output
queue (Queue.queue): queue for threading
criterion (torch.nn): one of PyTorch’s loss function. Refer to http://pytorch.org/docs/master/nn.html#loss-functions for a list of them.
device (torch.cuda): device used ('cuda' or 'cpu')
worker_num (int): the number of cpu cores used
Returns: loss, cer
- **loss** (float): loss of present epoch
- **cer** (float): character error rate
"""
total_loss = 0.
total_num = 0
total_dist = 0
total_length = 0
total_sent_num = 0
time_step = 0
model.train()
begin = epoch_begin = time.time()
while True:
if hparams.use_multistep_lr and epoch == 0 and time_step < 1000:
ramp_up(optimizer, time_step, hparams)
if hparams.use_multistep_lr and epoch == 1:
exp_decay(optimizer, total_time_step, hparams)
feats, targets, feat_lengths, label_lengths = queue.get()
if feats.shape[0] == 0:
# empty feats means closing one loader
worker_num -= 1
logger.debug('left train_loader: %d' % (worker_num))
if worker_num == 0:
break
else:
continue
optimizer.zero_grad()
feats = feats.to(device)
targets = targets.to(device)
target = targets[:, 1:]
model.module.flatten_parameters()
y_hat, logit = model(feats,
targets,
teacher_forcing_ratio=teacher_forcing_ratio)
loss = criterion(logit.contiguous().view(-1, logit.size(-1)),
target.contiguous().view(-1))
total_loss += loss.item()
total_num += sum(feat_lengths)
dist, length = get_distance(target, y_hat, id2char, EOS_TOKEN)
total_dist += dist
total_length += length
total_sent_num += target.size(0)
loss.backward()
optimizer.step()
if time_step % print_time_step == 0:
current = time.time()
elapsed = current - begin
epoch_elapsed = (current - epoch_begin) / 60.0
train_elapsed = (current - train_begin) / 3600.0
logger.info(
'timestep: {:4d}/{:4d}, loss: {:.4f}, cer: {:.2f}, elapsed: {:.2f}s {:.2f}m {:.2f}h'
.format(time_step, total_time_step, total_loss / total_num,
total_dist / total_length, elapsed, epoch_elapsed,
train_elapsed))
begin = time.time()
if time_step % 1000 == 0:
save_step_result(train_step_result, total_loss / total_num,
total_dist / total_length)
if time_step % 10000 == 0:
torch.save(model, "model.pt")
torch.save(
model, "./data/weight_file/epoch_%s_step_%s.pt" %
(str(epoch), str(time_step)))
time_step += 1
supervised_train.cumulative_batch_count += 1
torch.cuda.empty_cache(
) # GPU memory free. if you have enough GPU memory, delete this line
loss = total_loss / total_num
cer = total_dist / total_length
logger.info('train() completed')
return loss, cer
def logger_hparams(self):
""" print information of hyperparameters """
logger.info("use_bidirectional : %s" % str(self.use_bidirectional))
logger.info("use_attention : %s" % str(self.use_attention))
logger.info("use_pickle : %s" % str(self.use_pickle))
logger.info("use_augment : %s" % str(self.use_augment))
logger.info("use_pyramidal : %s" % str(self.use_pyramidal))
logger.info("augment_ratio : %0.2f" % self.augment_ratio)
logger.info("input_reverse : %s" % str(self.input_reverse))
logger.info("hidden_size : %d" % self.hidden_size)
logger.info("listener_layer_size : %d" % self.listener_layer_size)
logger.info("speller_layer_size : %d" % self.speller_layer_size)
logger.info("dropout : %0.2f" % self.dropout)
logger.info("batch_size : %d" % self.batch_size)
logger.info("worker_num : %d" % self.worker_num)
logger.info("max_epochs : %d" % self.max_epochs)
logger.info("initial learning rate : %0.4f" % self.init_lr)
if self.use_multistep_lr:
logger.info("high plateau learning rate : %0.4f" %
self.high_plateau_lr)
logger.info("low plateau learning rate : %0.4f" %
self.low_plateau_lr)
logger.info("teacher_forcing_ratio : %0.2f" % self.teacher_forcing)
logger.info("seed : %d" % self.seed)
logger.info("max_len : %d" % self.max_len)
logger.info("use_cuda : %s" % str(self.use_cuda))
def split_dataset(config,
audio_paths,
label_paths,
valid_ratio=0.05,
target_dict=None):
"""
Dataset split into training and validation Dataset.
Args:
valid_ratio: validation set ratio of total dataset
config (package.config.HyperParams): set of configures
audio_paths (list): set of audio path
label_paths (list): set of label path
target_dict (dict): dictionary of filename and target
Returns: train_batch_num, train_dataset_list, valid_dataset
- **train_batch_num** (int): num of batch for training
- **train_dataset_list** (list): list of training dataset
- **valid_dataset** (utils.dataset.BaseDataset): validation dataset
"""
logger.info("split dataset start !!")
trainset_list = list()
train_num = math.ceil(len(audio_paths) * (1 - valid_ratio))
total_time_step = math.ceil(len(audio_paths) / config.batch_size)
valid_time_step = math.ceil(total_time_step * valid_ratio)
train_time_step = total_time_step - valid_time_step
if config.use_augment:
train_time_step = int(train_time_step * (1 + config.augment_ratio))
train_num_per_worker = math.ceil(train_num / config.worker_num)
# audio_paths & label_paths shuffled in the same order
# for seperating train & validation
data_paths = list(zip(audio_paths, label_paths))
random.shuffle(data_paths)
audio_paths, label_paths = zip(*data_paths)
# seperating the train dataset by the number of workers
for idx in range(config.worker_num):
train_begin_idx = train_num_per_worker * idx
train_end_idx = min(train_num_per_worker * (idx + 1), train_num)
trainset_list.append(
CustomDataset(
audio_paths=audio_paths[train_begin_idx:train_end_idx],
label_paths=label_paths[train_begin_idx:train_end_idx],
sos_id=SOS_TOKEN,
eos_id=EOS_TOKEN,
target_dict=target_dict,
input_reverse=config.input_reverse,
use_augment=config.use_augment,
batch_size=config.batch_size,
augment_ratio=config.augment_ratio))
validset = CustomDataset(audio_paths=audio_paths[train_num:],
label_paths=label_paths[train_num:],
sos_id=SOS_TOKEN,
eos_id=EOS_TOKEN,
batch_size=config.batch_size,
target_dict=target_dict,
input_reverse=config.input_reverse,
use_augment=False)
save_pickle(trainset_list, './data/pickle/trainset_list')
save_pickle(validset, './data/pickle/validset')
logger.info("split dataset complete !!")
return train_time_step, trainset_list, validset
from torch import optim
from package.config import Config
from package.definition import char2id, logger, SOS_token, EOS_token, PAD_token
from package.data_loader import CustomDataset, load_corpus, CustomDataLoader
from package.evaluator import evaluate
from package.loss import Perplexity
from package.trainer import supervised_train
from model import LanguageModel
# Character-level Recurrent Neural Network Language Model implement in Pytorch
# https://github.com/sooftware/char-rnnlm
if __name__ == '__main__':
os.environ[
"CUDA_LAUNCH_BLOCKING"] = "1" # if you use Multi-GPU, delete this line
logger.info("device : %s" % torch.cuda.get_device_name(0))
logger.info("CUDA is available : %s" % (torch.cuda.is_available()))
logger.info("CUDA version : %s" % torch.version.cuda)
logger.info("PyTorch version : %s" % torch.__version__)
config = Config(use_cuda=True,
hidden_size=512,
dropout_p=0.5,
n_layers=4,
batch_size=16,
max_epochs=40,
lr=0.0001,
teacher_forcing_ratio=1.0,
seed=1,
max_len=428,
worker_num=1)
def get_librosa_mfcc(filepath,
n_mfcc=40,
del_silence=False,
input_reverse=True):
r""":
Mel-frequency cepstral coefficients (MFCCs)
Args:
filepath (str): specific path of audio file
n_mfcc (int): number of mel filter
del_silence (bool): flag indication whether to delete silence or not (default: True)
input_reverse (bool): flag indication whether to reverse input or not (default: True)
Feature Parameters:
- **sample rate**: A.I Hub dataset`s sample rate is 16,000
- **frame length**: 25ms
- **stride**: 10ms
- **overlap**: 15ms
- **window**: Hamming Window
.. math::
\begin{array}{ll}
NFFT = sr * frame length \\
HopLength = sr * stride \\
\end{array}
Returns: mfcc
- **mfcc** (torch.Tensor): return mel frequency cepstral coefficient feature
Examples::
Generate mfccs from a time series
>>> get_librosa_mfcc("KaiSpeech_021458.pcm", n_mfcc=40, input_reverse=True)
Tensor([[ -5.229e+02, -4.944e+02, ..., -5.229e+02, -5.229e+02],
[ 7.105e-15, 3.787e+01, ..., -7.105e-15, -7.105e-15],
...,
[ 1.066e-14, -7.500e+00, ..., 1.421e-14, 1.421e-14],
[ 3.109e-14, -5.058e+00, ..., 2.931e-14, 2.931e-14]])
"""
if filepath.split('.')[-1] == 'pcm':
try:
pcm = np.memmap(filepath, dtype='h', mode='r')
except: # exception handling
logger.info("%s Error Occur !!" % filepath)
return None
signal = np.array([float(x) for x in pcm])
elif filepath.split('.')[-1] == 'wav':
signal, _ = librosa.core.load(filepath, sr=16000)
else:
raise ValueError("Invalid format !!")
if del_silence:
non_silence_ids = librosa.effects.split(signal, top_db=30)
signal = np.concatenate(
[signal[start:end] for start, end in non_silence_ids])
mfcc = librosa.feature.mfcc(signal,
16000,
hop_length=160,
n_mfcc=n_mfcc,
n_fft=400,
window='hamming')
if input_reverse:
mfcc = mfcc[:, ::-1]
mfcc = torch.FloatTensor(np.ascontiguousarray(np.swapaxes(mfcc, 0, 1)))
return mfcc
def get_librosa_melspectrogram(filepath,
n_mels=128,
del_silence=False,
input_reverse=True,
mel_type='log_mel'):
r"""
Compute a mel-scaled soectrigram (or Log-Mel).
Args:
filepath (str): specific path of audio file
n_mels (int): number of mel filter
del_silence (bool): flag indication whether to delete silence or not (default: True)
mel_type (str): if 'log_mel' return log-mel (default: 'log_mel')
input_reverse (bool): flag indication whether to reverse input or not (default: True)
Feature Parameters:
- **sample rate**: A.I Hub dataset`s sample rate is 16,000
- **frame length**: 25ms
- **stride**: 10ms
- **overlap**: 15ms
- **window**: Hamming Window
.. math::
\begin{array}{ll}
NFFT = sr * frame length \\
Hop Length = sr * stride \\
\end{array}
Returns: mel_spectrogram
- **mel_spectrogram** (torch.Tensor): return Mel-Spectrogram (or Log-Mel) feature
Examples::
Generate mel spectrogram from a time series
>>> get_librosa_melspectrogram("KaiSpeech_021458.pcm", n_mels=128, input_reverse=True)
Tensor([[ 2.891e-07, 2.548e-03, ..., 8.116e-09, 5.633e-09],
[ 1.986e-07, 1.162e-02, ..., 9.332e-08, 6.716e-09],
...,
[ 3.668e-09, 2.029e-08, ..., 3.208e-09, 2.864e-09],
[ 2.561e-10, 2.096e-09, ..., 7.543e-10, 6.101e-10]])
"""
if filepath.split('.')[-1] == 'pcm':
try:
pcm = np.memmap(filepath, dtype='h', mode='r')
except: # exception handling
logger.info("%s Error Occur !!" % filepath)
return None
signal = np.array([float(x) for x in pcm])
elif filepath.split('.')[-1] == 'wav':
signal, _ = librosa.core.load(filepath, sr=16000)
else:
raise ValueError("Invalid format !!")
if del_silence:
non_silence_ids = librosa.effects.split(y=signal, top_db=30)
signal = np.concatenate(
[signal[start:end] for start, end in non_silence_ids])
mel_spectrogram = librosa.feature.melspectrogram(signal,
sr=16000,
n_mels=n_mels,
n_fft=400,
hop_length=160,
window='hamming')
if mel_type == 'log_mel':
mel_spectrogram = librosa.amplitude_to_db(mel_spectrogram, ref=np.max)
if input_reverse:
mel_spectrogram = mel_spectrogram[:, ::-1]
mel_spectrogram = torch.FloatTensor(
np.ascontiguousarray(np.swapaxes(mel_spectrogram, 0, 1)))
return mel_spectrogram
def supervised_train(model, queue, perplexity, optimizer, device, print_every,
epoch, teacher_forcing_ratio, worker_num, total_time_step,
train_begin):
print_loss_total = 0 # Reset every print_every
epoch_loss_total = 0 # Reset every epoch
total_num = 0
time_step = 0
model.train()
begin = epoch_begin = time.time()
while True:
loss = perplexity
inputs, targets, input_lens, target_lens = queue.get()
if inputs.shape[0] == 0:
# empty feats means closing one loader
worker_num -= 1
logger.debug('left train_loader: %d' % worker_num)
if worker_num == 0:
break
else:
continue
inputs = inputs.to(device)
targets = targets.to(device)
model.module.flatten_parameters()
outputs = model(inputs, teacher_forcing_ratio=teacher_forcing_ratio)
# Get loss
loss.reset()
for step, step_output in enumerate(outputs):
batch_size = targets.size(0)
loss.eval_batch(step_output.contiguous().view(batch_size, -1),
targets[:, step])
# Backpropagation
model.zero_grad()
loss.backward()
optimizer.step()
loss = loss.get_loss()
epoch_loss_total += loss
print_loss_total += loss
total_num += sum(input_lens)
time_step += 1
torch.cuda.empty_cache()
if time_step % print_every == 0:
current = time.time()
elapsed = current - begin
epoch_elapsed = (current - epoch_begin) / 60.0
train_elapsed = (current - train_begin) / 3600.0
logger.info(
'timestep: {:4d}/{:4d}, perplexity: {:.4f}, elapsed: {:.2f}s {:.2f}m {:.2f}h'
.format(time_step, total_time_step,
print_loss_total / print_every, elapsed, epoch_elapsed,
train_elapsed))
print_loss_total = 0
begin = time.time()
if time_step % 50000 == 0:
torch.save(model,
"./data/epoch%s_%s.pt" % (str(epoch), str(time_step)))
logger.info('train() completed')
return epoch_loss_total / total_num
