class Solver(BaseSolver):
''' Solver for training'''
def __init__(self, config, paras, mode):
super().__init__(config, paras, mode)
# ToDo : support tr/eval on different corpus
assert self.config['data']['corpus']['name'] == self.src_config['data']['corpus']['name']
self.config['data']['corpus']['path'] = self.src_config['data']['corpus']['path']
self.config['data']['corpus']['bucketing'] = False
# The follow attribute should be identical to training config
self.config['data']['audio'] = self.src_config['data']['audio']
self.config['data']['corpus']['train_split'] = self.src_config['data']['corpus']['train_split']
self.config['data']['text'] = self.src_config['data']['text']
self.tokenizer = load_text_encoder(**self.config['data']['text'])
self.config['model'] = self.src_config['model']
self.finetune_first = 5
self.best_wer = {'att': 3.0, 'ctc': 3.0}
# Output file
self.output_file = str(self.ckpdir)+'_{}_{}.csv'
# Override batch size for beam decoding
self.greedy = self.config['decode']['beam_size'] == 1
self.dealer = Datadealer(self.config['data']['audio'])
self.ctc = self.config['decode']['ctc_weight'] == 1.0
if not self.greedy:
self.config['data']['corpus']['batch_size'] = 1
else:
# ToDo : implement greedy
raise NotImplementedError
# Logger settings
self.logdir = os.path.join(paras.logdir, self.exp_name)
self.log = SummaryWriter(
self.logdir, flush_secs=self.TB_FLUSH_FREQ)
self.timer = Timer()
def fetch_data(self, data):
''' Move data to device and compute text seq. length'''
_, feat, feat_len, txt = data
feat = feat.to(self.device)
feat_len = feat_len.to(self.device)
txt = txt.to(self.device)
txt_len = torch.sum(txt != 0, dim=-1)
return feat, feat_len, txt, txt_len
def load_data(self, batch_size=7):
''' Load data for training/validation, store tokenizer and input/output shape'''
prev_batch_size = self.config['data']['corpus']['batch_size']
self.config['data']['corpus']['batch_size'] = batch_size
self.tr_set, self.dv_set, self.feat_dim, self.vocab_size, self.tokenizer, msg = \
load_dataset(self.paras.njobs, self.paras.gpu,
self.paras.pin_memory, False, **self.config['data'])
self.config['data']['corpus']['batch_size'] = prev_batch_size
self.verbose(msg)
def set_model(self):
''' Setup ASR model '''
# Model
self.feat_dim = 120
self.vocab_size = 46
init_adadelta = True
''' Setup ASR model and optimizer '''
# Model
# init_adadelta = self.config['hparas']['optimizer'] == 'Adadelta'
self.model = ASR(self.feat_dim, self.vocab_size, init_adadelta, **
self.src_config['model']).to(self.device)
self.verbose(self.model.create_msg())
if self.finetune_first>0:
names = ["encoder.layers.%d"%i for i in range(self.finetune_first)]
model_paras = [{"params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in names)]}]
else:
model_paras = [{'params': self.model.parameters()}]
# Losses
self.seq_loss = torch.nn.CrossEntropyLoss(ignore_index=0)
# Note: zero_infinity=False is unstable?
self.ctc_loss = torch.nn.CTCLoss(blank=0, zero_infinity=False)
# Plug-ins
self.emb_fuse = False
self.emb_reg = ('emb' in self.config) and (
self.config['emb']['enable'])
if self.emb_reg:
from src.plugin import EmbeddingRegularizer
self.emb_decoder = EmbeddingRegularizer(
self.tokenizer, self.model.dec_dim, **self.config['emb']).to(self.device)
model_paras.append({'params': self.emb_decoder.parameters()})
self.emb_fuse = self.emb_decoder.apply_fuse
if self.emb_fuse:
self.seq_loss = torch.nn.NLLLoss(ignore_index=0)
self.verbose(self.emb_decoder.create_msg())
# Optimizer
self.optimizer = Optimizer(model_paras, **self.src_config['hparas'])
self.verbose(self.optimizer.create_msg())
# Enable AMP if needed
self.enable_apex()
# Automatically load pre-trained model if self.paras.load is given
self.load_ckpt()
# Beam decoder
self.decoder = BeamDecoder(
self.model, self.emb_decoder, **self.config['decode'])
self.verbose(self.decoder.create_msg())
# del self.model
# del self.emb_decoder
self.decoder.to(self.device)
def exec(self):
''' Testing End-to-end ASR system '''
while True:
try:
filename = input("Input wav file name: ")
if filename == "exit":
return
feat, feat_len = self.dealer(filename)
feat = feat.to(self.device)
feat_len = feat_len.to(self.device)
# Decode
with torch.no_grad():
hyps = self.decoder(feat, feat_len)
hyp_seqs = [hyp.outIndex for hyp in hyps]
hyp_txts = [self.tokenizer.decode(hyp, ignore_repeat=self.ctc) for hyp in hyp_seqs]
for txt in hyp_txts:
print(txt)
except:
print("Invalid file")
pass
def recognize(self, filename):
try:
feat, feat_len = self.dealer(filename)
feat = feat.to(self.device)
feat_len = feat_len.to(self.device)
# Decode
with torch.no_grad():
hyps = self.decoder(feat, feat_len)
hyp_seqs = [hyp.outIndex for hyp in hyps]
hyp_txts = [self.tokenizer.decode(hyp, ignore_repeat=self.ctc) for hyp in hyp_seqs]
return hyp_txts[0]
except Exception as e:
print(e)
app.logger.debug(e)
return "Invalid file"
def fetch_finetune_data(self, filename, fixed_text):
feat, feat_len = self.dealer(filename)
feat = feat.to(self.device)
feat_len = feat_len.to(self.device)
text = self.tokenizer.encode(fixed_text)
text = torch.tensor(text).to(self.device)
text_len = len(text)
return [feat, feat_len, text, text_len]
def merge_batch(self, main_batch, attach_batch):
max_feat_len = max(main_batch[1])
max_text_len = max(main_batch[3])
if attach_batch[0].shape[1] > max_feat_len:
# reduce extra long example
attach_batch[0] = attach_batch[0][:,:max_feat_len]
attach_batch[1][0] = max_feat_len
else:
# pad to max_feat_len
padding = torch.zeros(1, max_feat_len - attach_batch[0].shape[1], attach_batch[0].shape[2], dtype=attach_batch[0].dtype).to(self.device)
attach_batch[0] = torch.cat([attach_batch[0], padding], dim=1)
if attach_batch[2].shape[0] > max_text_len:
attach_batch[2] = attach_batch[2][:max_text_len]
main_batch[3][0] = max_text_len
else:
padding = torch.zeros(max_text_len - attach_batch[2].shape[0], dtype=attach_batch[2].dtype).to(self.device)
try:
attach_batch[2] = torch.cat([attach_batch[2], padding], dim=0).unsqueeze(0)
except:
pdb.set_trace()
new_batch = (
torch.cat([main_batch[0], attach_batch[0]], dim=0),
torch.cat([main_batch[1], attach_batch[1]], dim=0),
torch.cat([main_batch[2], attach_batch[2]], dim=0),
torch.cat([main_batch[3], torch.tensor([attach_batch[3]]).to(self.device)], dim=0)
)
return new_batch
def finetune(self, filename, fixed_text, max_step=5):
# Load data for finetune
self.verbose('Total training steps {}.'.format(
human_format(max_step)))
ctc_loss, att_loss, emb_loss = None, None, None
n_epochs = 0
accum_count = 0
self.timer.set()
step = 0
for data in self.tr_set:
# Pre-step : update tf_rate/lr_rate and do zero_grad
if max_step == 0:
break
tf_rate = self.optimizer.pre_step(400000)
total_loss = 0
# Fetch data
finetune_data = self.fetch_finetune_data(filename, fixed_text)
main_batch = self.fetch_data(data)
new_batch = self.merge_batch(main_batch, finetune_data)
feat, feat_len, txt, txt_len = new_batch
self.timer.cnt('rd')
# Forward model
# Note: txt should NOT start w/ <sos>
ctc_output, encode_len, att_output, att_align, dec_state = \
self.model(feat, feat_len, max(txt_len), tf_rate=tf_rate,
teacher=txt, get_dec_state=self.emb_reg)
# Plugins
if self.emb_reg:
emb_loss, fuse_output = self.emb_decoder(
dec_state, att_output, label=txt)
total_loss += self.emb_decoder.weight*emb_loss
# Compute all objectives
if ctc_output is not None:
if self.paras.cudnn_ctc:
ctc_loss = self.ctc_loss(ctc_output.transpose(0, 1),
txt.to_sparse().values().to(device='cpu', dtype=torch.int32),
[ctc_output.shape[1]] *
len(ctc_output),
txt_len.cpu().tolist())
else:
ctc_loss = self.ctc_loss(ctc_output.transpose(
0, 1), txt, encode_len, txt_len)
total_loss += ctc_loss*self.model.ctc_weight
if att_output is not None:
b, t, _ = att_output.shape
att_output = fuse_output if self.emb_fuse else att_output
att_loss = self.seq_loss(
att_output.contiguous().view(b*t, -1), txt.contiguous().view(-1))
total_loss += att_loss*(1-self.model.ctc_weight)
self.timer.cnt('fw')
# Backprop
grad_norm = self.backward(total_loss)
step += 1
# Logger
self.progress('Tr stat | Loss - {:.2f} | Grad. Norm - {:.2f} | {}'
.format(total_loss.cpu().item(), grad_norm, self.timer.show()))
self.write_log(
'loss', {'tr_ctc': ctc_loss, 'tr_att': att_loss})
self.write_log('emb_loss', {'tr': emb_loss})
self.write_log('wer', {'tr_att': cal_er(self.tokenizer, att_output, txt),
'tr_ctc': cal_er(self.tokenizer, ctc_output, txt, ctc=True)})
if self.emb_fuse:
if self.emb_decoder.fuse_learnable:
self.write_log('fuse_lambda', {
'emb': self.emb_decoder.get_weight()})
self.write_log(
'fuse_temp', {'temp': self.emb_decoder.get_temp()})
# End of step
# https://github.com/pytorch/pytorch/issues/13246#issuecomment-529185354
torch.cuda.empty_cache()
self.timer.set()
if step > max_step:
break
ret = self.validate()
self.log.close()
return ret
def validate(self):
# Eval mode
self.model.eval()
if self.emb_decoder is not None:
self.emb_decoder.eval()
dev_wer = {'att': [], 'ctc': []}
for i, data in enumerate(self.dv_set):
self.progress('Valid step - {}/{}'.format(i+1, len(self.dv_set)))
# Fetch data
feat, feat_len, txt, txt_len = self.fetch_data(data)
# Forward model
with torch.no_grad():
ctc_output, encode_len, att_output, att_align, dec_state = \
self.model(feat, feat_len, int(max(txt_len)*self.DEV_STEP_RATIO),
emb_decoder=self.emb_decoder)
dev_wer['att'].append(cal_er(self.tokenizer, att_output, txt))
dev_wer['ctc'].append(cal_er(self.tokenizer, ctc_output, txt, ctc=True))
# Show some example on tensorboard
if i == len(self.dv_set)//2:
for i in range(min(len(txt), self.DEV_N_EXAMPLE)):
if True:
self.write_log('true_text{}'.format(
i), self.tokenizer.decode(txt[i].tolist()))
if att_output is not None:
self.write_log('att_align{}'.format(i), feat_to_fig(
att_align[i, 0, :, :].cpu().detach()))
self.write_log('att_text{}'.format(i), self.tokenizer.decode(
att_output[i].argmax(dim=-1).tolist()))
if ctc_output is not None:
self.write_log('ctc_text{}'.format(i), self.tokenizer.decode(ctc_output[i].argmax(dim=-1).tolist(),
ignore_repeat=True))
# Skip save model here
# Ckpt if performance improves
to_prints = []
for task in ['att', 'ctc']:
dev_wer[task] = sum(dev_wer[task]) / len(dev_wer[task])
if dev_wer[task] < self.best_wer[task]:
to_print = f"WER of {task}: {dev_wer[task]} < prev best ({self.best_wer[task]})"
self.best_wer[task] = dev_wer[task]
else:
to_print = f"WER of {task}: {dev_wer[task]} >= prev best ({self.best_wer[task]})"
print(to_print, flush=True)
to_prints.append(to_print)
# self.save_checkpoint('best_{}.pth'.format(task), 'wer', dev_wer[task])
self.write_log('wer', {'dv_'+task: dev_wer[task]})
# self.save_checkpoint('latest.pth', 'wer', dev_wer['att'], show_msg=False)
# Resume training
self.model.train()
if self.emb_decoder is not None:
self.emb_decoder.train()
return '\n'.join(to_prints)
class Solver(BaseSolver):
''' Solver for training'''
def __init__(self, config, paras, mode):
super().__init__(config, paras, mode)
# Logger settings
self.best_wer = {'att': 3.0, 'ctc': 3.0}
# Curriculum learning affects data loader
self.curriculum = self.config['hparas']['curriculum']
def fetch_data(self, data):
''' Move data to device and compute text seq. length'''
_, feat, feat_len, txt = data
feat = feat.to(self.device)
feat_len = feat_len.to(self.device)
txt = txt.to(self.device)
txt_len = torch.sum(txt != 0, dim=-1)
return feat, feat_len, txt, txt_len
def load_data(self):
''' Load data for training/validation, store tokenizer and input/output shape'''
self.tr_set, self.dv_set, self.feat_dim, self.vocab_size, self.tokenizer, msg = \
load_dataset(self.paras.njobs, self.paras.gpu, self.paras.pin_memory,
self.curriculum > 0, **self.config['data'])
self.verbose(msg)
def set_model(self):
''' Setup ASR model and optimizer '''
# Model
init_adadelta = self.config['hparas']['optimizer'] == 'Adadelta'
self.model = ASR(self.feat_dim, self.vocab_size, init_adadelta,
**self.config['model']).to(self.device)
self.verbose(self.model.create_msg())
model_paras = [{'params': self.model.parameters()}]
# Losses
self.seq_loss = torch.nn.CrossEntropyLoss(ignore_index=0)
# Note: zero_infinity=False is unstable?
self.ctc_loss = torch.nn.CTCLoss(blank=0, zero_infinity=False)
# Plug-ins
self.emb_fuse = False
self.emb_reg = ('emb'
in self.config) and (self.config['emb']['enable'])
if self.emb_reg:
from src.plugin import EmbeddingRegularizer
self.emb_decoder = EmbeddingRegularizer(
self.tokenizer, self.model.dec_dim,
**self.config['emb']).to(self.device)
model_paras.append({'params': self.emb_decoder.parameters()})
self.emb_fuse = self.emb_decoder.apply_fuse
if self.emb_fuse:
self.seq_loss = torch.nn.NLLLoss(ignore_index=0)
self.verbose(self.emb_decoder.create_msg())
# Optimizer
self.optimizer = Optimizer(model_paras, **self.config['hparas'])
self.verbose(self.optimizer.create_msg())
# Enable AMP if needed
self.enable_apex()
# Automatically load pre-trained model if self.paras.load is given
self.load_ckpt()
# ToDo: other training methods
def exec(self):
''' Training End-to-end ASR system '''
self.verbose('Total training steps {}.'.format(
human_format(self.max_step)))
ctc_loss, att_loss, emb_loss = None, None, None
n_epochs = 0
self.timer.set()
while self.step < self.max_step:
# Renew dataloader to enable random sampling
if self.curriculum > 0 and n_epochs == self.curriculum:
self.verbose(
'Curriculum learning ends after {} epochs, starting random sampling.'
.format(n_epochs))
self.tr_set, _, _, _, _, _ = \
load_dataset(self.paras.njobs, self.paras.gpu, self.paras.pin_memory,
False, **self.config['data'])
for data in self.tr_set:
# Pre-step : update tf_rate/lr_rate and do zero_grad
tf_rate = self.optimizer.pre_step(self.step)
total_loss = 0
# Fetch data
feat, feat_len, txt, txt_len = self.fetch_data(data)
self.timer.cnt('rd')
# Forward model
# Note: txt should NOT start w/ <sos>
ctc_output, encode_len, att_output, att_align, dec_state = \
self.model(feat, feat_len, max(txt_len), tf_rate=tf_rate,
teacher=txt, get_dec_state=self.emb_reg)
# Plugins
if self.emb_reg:
emb_loss, fuse_output = self.emb_decoder(dec_state,
att_output,
label=txt)
total_loss += self.emb_decoder.weight * emb_loss
# Compute all objectives
if ctc_output is not None:
if self.paras.cudnn_ctc:
ctc_loss = self.ctc_loss(
ctc_output.transpose(0, 1),
txt.to_sparse().values().to(device='cpu',
dtype=torch.int32),
[ctc_output.shape[1]] * len(ctc_output),
txt_len.cpu().tolist())
else:
ctc_loss = self.ctc_loss(ctc_output.transpose(0, 1),
txt, encode_len, txt_len)
total_loss += ctc_loss * self.model.ctc_weight
if att_output is not None:
b, t, _ = att_output.shape
att_output = fuse_output if self.emb_fuse else att_output
att_loss = self.seq_loss(
att_output.contiguous().view(b * t, -1),
txt.contiguous().view(-1))
total_loss += att_loss * (1 - self.model.ctc_weight)
self.timer.cnt('fw')
# Backprop
grad_norm = self.backward(total_loss)
self.step += 1
# Logger
if (self.step == 1) or (self.step % self.PROGRESS_STEP == 0):
self.progress(
'Tr stat | Loss - {:.2f} | Grad. Norm - {:.2f} | {}'.
format(total_loss.cpu().item(), grad_norm,
self.timer.show()))
self.write_log('loss', {
'tr_ctc': ctc_loss,
'tr_att': att_loss
})
self.write_log('emb_loss', {'tr': emb_loss})
self.write_log(
'wer', {
'tr_att':
cal_er(self.tokenizer, att_output, txt),
'tr_ctc':
cal_er(self.tokenizer, ctc_output, txt, ctc=True)
})
if self.emb_fuse:
if self.emb_decoder.fuse_learnable:
self.write_log(
'fuse_lambda',
{'emb': self.emb_decoder.get_weight()})
self.write_log('fuse_temp',
{'temp': self.emb_decoder.get_temp()})
# Validation
if (self.step == 1) or (self.step % self.valid_step == 0):
self.validate()
# End of step
# https://github.com/pytorch/pytorch/issues/13246#issuecomment-529185354
torch.cuda.empty_cache()
self.timer.set()
if self.step > self.max_step:
break
n_epochs += 1
self.log.close()
def validate(self):
# Eval mode
self.model.eval()
if self.emb_decoder is not None:
self.emb_decoder.eval()
dev_wer = {'att': [], 'ctc': []}
for i, data in enumerate(self.dv_set):
self.progress('Valid step - {}/{}'.format(i + 1, len(self.dv_set)))
# Fetch data
feat, feat_len, txt, txt_len = self.fetch_data(data)
# Forward model
with torch.no_grad():
ctc_output, encode_len, att_output, att_align, dec_state = \
self.model(feat, feat_len, int(max(txt_len)*self.DEV_STEP_RATIO),
emb_decoder=self.emb_decoder)
dev_wer['att'].append(cal_er(self.tokenizer, att_output, txt))
dev_wer['ctc'].append(
cal_er(self.tokenizer, ctc_output, txt, ctc=True))
# Show some example on tensorboard
if i == len(self.dv_set) // 2:
for i in range(min(len(txt), self.DEV_N_EXAMPLE)):
if self.step == 1:
self.write_log('true_text{}'.format(i),
self.tokenizer.decode(txt[i].tolist()))
if att_output is not None:
self.write_log(
'att_align{}'.format(i),
feat_to_fig(att_align[i, 0, :, :].cpu().detach()))
self.write_log(
'att_text{}'.format(i),
self.tokenizer.decode(
att_output[i].argmax(dim=-1).tolist()))
if ctc_output is not None:
self.write_log(
'ctc_text{}'.format(i),
self.tokenizer.decode(
ctc_output[i].argmax(dim=-1).tolist(),
ignore_repeat=True))
# Ckpt if performance improves
for task in ['att', 'ctc']:
dev_wer[task] = sum(dev_wer[task]) / len(dev_wer[task])
if dev_wer[task] < self.best_wer[task]:
self.best_wer[task] = dev_wer[task]
self.save_checkpoint('best_{}.pth'.format(task), 'wer',
dev_wer[task])
self.write_log('wer', {'dv_' + task: dev_wer[task]})
self.save_checkpoint('latest.pth',
'wer',
dev_wer['att'],
show_msg=False)
# Resume training
self.model.train()
if self.emb_decoder is not None:
self.emb_decoder.train()
class Solver(BaseSolver):
''' Solver for training'''
def __init__(self, config, paras, mode):
super().__init__(config, paras, mode)
# Curriculum learning affects data loader
self.curriculum = self.config['hparas']['curriculum']
self.val_mode = self.config['hparas']['val_mode'].lower()
self.WER = 'per' if self.val_mode == 'per' else 'wer'
def fetch_data(self, data, train=False):
''' Move data to device and compute text seq. length'''
# feat: B x T x D
_, feat, feat_len, txt = data
if self.paras.upstream is not None:
# feat is raw waveform
device = 'cpu' if self.paras.deterministic else self.device
self.upstream.to(device)
self.specaug.to(device)
def to_device(feat):
return [f.to(device) for f in feat]
def extract_feature(feat):
feat = self.upstream(to_device(feat))
if train and self.config['data']['audio'][
'augment'] and 'aug' not in self.paras.upstream:
feat = [self.specaug(f) for f in feat]
return feat
if HALF_BATCHSIZE_AUDIO_LEN < 3500 and train:
first_len = extract_feature(feat[:1])[0].shape[0]
if first_len > HALF_BATCHSIZE_AUDIO_LEN:
feat = feat[::2]
txt = txt[::2]
if self.paras.upstream_trainable:
self.upstream.train()
feat = extract_feature(feat)
else:
with torch.no_grad():
self.upstream.eval()
feat = extract_feature(feat)
feat_len = torch.LongTensor([len(f) for f in feat])
feat = pad_sequence(feat, batch_first=True)
txt = pad_sequence(txt, batch_first=True)
feat = feat.to(self.device)
feat_len = feat_len.to(self.device)
txt = txt.to(self.device)
txt_len = torch.sum(txt != 0, dim=-1)
return feat, feat_len, txt, txt_len
def load_data(self):
''' Load data for training/validation, store tokenizer and input/output shape'''
if self.paras.upstream is not None:
print(f'[Solver] - using S3PRL {self.paras.upstream}')
self.tr_set, self.dv_set, self.vocab_size, self.tokenizer, msg = \
load_wav_dataset(self.paras.njobs, self.paras.gpu, self.paras.pin_memory,
self.curriculum>0,
**self.config['data'])
self.upstream = torch.hub.load(
's3prl/s3prl',
self.paras.upstream,
feature_selection=self.paras.upstream_feature_selection,
refresh=self.paras.upstream_refresh,
ckpt=self.paras.upstream_ckpt,
force_reload=True,
)
self.feat_dim = self.upstream.get_output_dim()
self.specaug = Augment()
else:
self.tr_set, self.dv_set, self.feat_dim, self.vocab_size, self.tokenizer, msg = \
load_dataset(self.paras.njobs, self.paras.gpu, self.paras.pin_memory,
self.curriculum>0,
**self.config['data'])
self.verbose(msg)
# Dev set sames
self.dv_names = []
if type(self.dv_set) is list:
for ds in self.config['data']['corpus']['dev_split']:
self.dv_names.append(ds[0])
else:
self.dv_names = self.config['data']['corpus']['dev_split'][0]
# Logger settings
if type(self.dv_names) is str:
self.best_wer = {
'att': {
self.dv_names: 3.0
},
'ctc': {
self.dv_names: 3.0
}
}
else:
self.best_wer = {'att': {}, 'ctc': {}}
for name in self.dv_names:
self.best_wer['att'][name] = 3.0
self.best_wer['ctc'][name] = 3.0
def set_model(self):
''' Setup ASR model and optimizer '''
# Model
#print(self.feat_dim) #160
batch_size = self.config['data']['corpus']['batch_size'] // 2
self.model = ASR(self.feat_dim, self.vocab_size, batch_size,
**self.config['model']).to(self.device)
self.verbose(self.model.create_msg())
model_paras = [{'params': self.model.parameters()}]
# Losses
'''label smoothing'''
if self.config['hparas']['label_smoothing']:
self.seq_loss = LabelSmoothingLoss(31, 0.1)
print('[INFO] using label smoothing. ')
else:
self.seq_loss = torch.nn.CrossEntropyLoss(ignore_index=0)
self.ctc_loss = torch.nn.CTCLoss(
blank=0,
zero_infinity=False) # Note: zero_infinity=False is unstable?
# Plug-ins
self.emb_fuse = False
self.emb_reg = ('emb'
in self.config) and (self.config['emb']['enable'])
if self.emb_reg:
from src.plugin import EmbeddingRegularizer
self.emb_decoder = EmbeddingRegularizer(
self.tokenizer, self.model.dec_dim,
**self.config['emb']).to(self.device)
model_paras.append({'params': self.emb_decoder.parameters()})
self.emb_fuse = self.emb_decoder.apply_fuse
if self.emb_fuse:
self.seq_loss = torch.nn.NLLLoss(ignore_index=0)
self.verbose(self.emb_decoder.create_msg())
# Optimizer
self.optimizer = Optimizer(model_paras, **self.config['hparas'])
self.lr_scheduler = self.optimizer.lr_scheduler
self.verbose(self.optimizer.create_msg())
# Enable AMP if needed
self.enable_apex()
# Transfer Learning
if self.transfer_learning:
self.verbose('Apply transfer learning: ')
self.verbose(' Train encoder layers: {}'.format(
self.train_enc))
self.verbose(' Train decoder: {}'.format(
self.train_dec))
self.verbose(' Save name: {}'.format(
self.save_name))
# Automatically load pre-trained model if self.paras.load is given
self.load_ckpt()
def exec(self):
''' Training End-to-end ASR system '''
self.verbose('Total training steps {}.'.format(
human_format(self.max_step)))
if self.transfer_learning:
self.model.encoder.fix_layers(self.fix_enc)
if self.fix_dec and self.model.enable_att:
self.model.decoder.fix_layers()
if self.fix_dec and self.model.enable_ctc:
self.model.fix_ctc_layer()
self.n_epochs = 0
self.timer.set()
'''early stopping for ctc '''
self.early_stoping = self.config['hparas']['early_stopping']
stop_epoch = 10
batch_size = self.config['data']['corpus']['batch_size']
stop_step = len(self.tr_set) * stop_epoch // batch_size
while self.step < self.max_step:
ctc_loss, att_loss, emb_loss = None, None, None
# Renew dataloader to enable random sampling
if self.curriculum > 0 and n_epochs == self.curriculum:
self.verbose(
'Curriculum learning ends after {} epochs, starting random sampling.'
.format(n_epochs))
self.tr_set, _, _, _, _, _ = \
load_dataset(self.paras.njobs, self.paras.gpu, self.paras.pin_memory,
False, **self.config['data'])
for data in self.tr_set:
# Pre-step : update tf_rate/lr_rate and do zero_grad
tf_rate = self.optimizer.pre_step(self.step)
total_loss = 0
# Fetch data
feat, feat_len, txt, txt_len = self.fetch_data(data,
train=True)
self.timer.cnt('rd')
# Forward model
# Note: txt should NOT start w/ <sos>
ctc_output, encode_len, att_output, att_align, dec_state = \
self.model( feat, feat_len, max(txt_len), tf_rate=tf_rate,
teacher=txt, get_dec_state=self.emb_reg)
# Clear not used objects
del att_align
# Plugins
if self.emb_reg:
emb_loss, fuse_output = self.emb_decoder(dec_state,
att_output,
label=txt)
total_loss += self.emb_decoder.weight * emb_loss
else:
del dec_state
''' early stopping ctc'''
if self.early_stoping:
if self.step > stop_step:
ctc_output = None
self.model.ctc_weight = 0
#print(ctc_output.shape)
# Compute all objectives
if ctc_output is not None:
if self.paras.cudnn_ctc:
ctc_loss = self.ctc_loss(
ctc_output.transpose(0, 1),
txt.to_sparse().values().to(device='cpu',
dtype=torch.int32),
[ctc_output.shape[1]] * len(ctc_output),
#[int(encode_len.max()) for _ in encode_len],
txt_len.cpu().tolist())
else:
ctc_loss = self.ctc_loss(ctc_output.transpose(0, 1),
txt, encode_len, txt_len)
total_loss += ctc_loss * self.model.ctc_weight
del encode_len
if att_output is not None:
#print(att_output.shape)
b, t, _ = att_output.shape
att_output = fuse_output if self.emb_fuse else att_output
att_loss = self.seq_loss(att_output.view(b * t, -1),
txt.view(-1))
# Sum each uttr and devide by length then mean over batch
# att_loss = torch.mean(torch.sum(att_loss.view(b,t),dim=-1)/torch.sum(txt!=0,dim=-1).float())
total_loss += att_loss * (1 - self.model.ctc_weight)
self.timer.cnt('fw')
# Backprop
grad_norm = self.backward(total_loss)
self.step += 1
# Logger
if (self.step == 1) or (self.step % self.PROGRESS_STEP == 0):
self.progress('Tr stat | Loss - {:.2f} | Grad. Norm - {:.2f} | {}'\
.format(total_loss.cpu().item(),grad_norm,self.timer.show()))
self.write_log('emb_loss', {'tr': emb_loss})
if att_output is not None:
self.write_log('loss', {'tr_att': att_loss})
self.write_log(self.WER, {
'tr_att':
cal_er(self.tokenizer, att_output, txt)
})
self.write_log(
'cer', {
'tr_att':
cal_er(self.tokenizer,
att_output,
txt,
mode='cer')
})
if ctc_output is not None:
self.write_log('loss', {'tr_ctc': ctc_loss})
self.write_log(
self.WER, {
'tr_ctc':
cal_er(
self.tokenizer, ctc_output, txt, ctc=True)
})
self.write_log(
'cer', {
'tr_ctc':
cal_er(self.tokenizer,
ctc_output,
txt,
mode='cer',
ctc=True)
})
self.write_log(
'ctc_text_train',
self.tokenizer.decode(
ctc_output[0].argmax(dim=-1).tolist(),
ignore_repeat=True))
# if self.step==1 or self.step % (self.PROGRESS_STEP * 5) == 0:
# self.write_log('spec_train',feat_to_fig(feat[0].transpose(0,1).cpu().detach(), spec=True))
#del total_loss
if self.emb_fuse:
if self.emb_decoder.fuse_learnable:
self.write_log(
'fuse_lambda',
{'emb': self.emb_decoder.get_weight()})
self.write_log('fuse_temp',
{'temp': self.emb_decoder.get_temp()})
# Validation
if (self.step == 1) or (self.step % self.valid_step == 0):
if type(self.dv_set) is list:
for dv_id in range(len(self.dv_set)):
self.validate(self.dv_set[dv_id],
self.dv_names[dv_id])
else:
self.validate(self.dv_set, self.dv_names)
if self.step % (len(self.tr_set) //
batch_size) == 0: # one epoch
print('Have finished epoch: ', self.n_epochs)
self.n_epochs += 1
if self.lr_scheduler == None:
lr = self.optimizer.opt.param_groups[0]['lr']
if self.step == 1:
print(
'[INFO] using lr schedular defined by Daniel, init lr = ',
lr)
if self.step > 99999 and self.step % 2000 == 0:
lr = lr * 0.85
for param_group in self.optimizer.opt.param_groups:
param_group['lr'] = lr
print('[INFO] at step:', self.step)
print('[INFO] lr reduce to', lr)
#self.lr_scheduler.step(total_loss)
# End of step
# if self.step % EMPTY_CACHE_STEP == 0:
# Empty cuda cache after every fixed amount of steps
torch.cuda.empty_cache(
) # https://github.com/pytorch/pytorch/issues/13246#issuecomment-529185354
self.timer.set()
if self.step > self.max_step: break
#update lr_scheduler
self.log.close()
print('[INFO] Finished training after', human_format(self.max_step),
'steps.')
def validate(self, _dv_set, _name):
# Eval mode
self.model.eval()
if self.emb_decoder is not None: self.emb_decoder.eval()
dev_wer = {'att': [], 'ctc': []}
dev_cer = {'att': [], 'ctc': []}
dev_er = {'att': [], 'ctc': []}
for i, data in enumerate(_dv_set):
self.progress('Valid step - {}/{}'.format(i + 1, len(_dv_set)))
# Fetch data
feat, feat_len, txt, txt_len = self.fetch_data(data)
# Forward model
with torch.no_grad():
ctc_output, encode_len, att_output, att_align, dec_state = \
self.model( feat, feat_len, int(max(txt_len)*self.DEV_STEP_RATIO),
emb_decoder=self.emb_decoder)
if att_output is not None:
dev_wer['att'].append(
cal_er(self.tokenizer, att_output, txt, mode='wer'))
dev_cer['att'].append(
cal_er(self.tokenizer, att_output, txt, mode='cer'))
dev_er['att'].append(
cal_er(self.tokenizer, att_output, txt,
mode=self.val_mode))
if ctc_output is not None:
dev_wer['ctc'].append(
cal_er(self.tokenizer,
ctc_output,
txt,
mode='wer',
ctc=True))
dev_cer['ctc'].append(
cal_er(self.tokenizer,
ctc_output,
txt,
mode='cer',
ctc=True))
dev_er['ctc'].append(
cal_er(self.tokenizer,
ctc_output,
txt,
mode=self.val_mode,
ctc=True))
# Show some example on tensorboard
if i == len(_dv_set) // 2:
for i in range(min(len(txt), self.DEV_N_EXAMPLE)):
if self.step == 1:
self.write_log('true_text_{}_{}'.format(_name, i),
self.tokenizer.decode(txt[i].tolist()))
if att_output is not None:
self.write_log(
'att_align_{}_{}'.format(_name, i),
feat_to_fig(att_align[i, 0, :, :].cpu().detach()))
self.write_log(
'att_text_{}_{}'.format(_name, i),
self.tokenizer.decode(
att_output[i].argmax(dim=-1).tolist()))
if ctc_output is not None:
self.write_log(
'ctc_text_{}_{}'.format(_name, i),
self.tokenizer.decode(
ctc_output[i].argmax(dim=-1).tolist(),
ignore_repeat=True))
# Ckpt if performance improves
tasks = []
if len(dev_er['att']) > 0:
tasks.append('att')
if len(dev_er['ctc']) > 0:
tasks.append('ctc')
for task in tasks:
dev_er[task] = sum(dev_er[task]) / len(dev_er[task])
dev_wer[task] = sum(dev_wer[task]) / len(dev_wer[task])
dev_cer[task] = sum(dev_cer[task]) / len(dev_cer[task])
if dev_er[task] < self.best_wer[task][_name]:
self.best_wer[task][_name] = dev_er[task]
self.save_checkpoint(
'best_{}_{}.pth'.format(
task, _name +
(self.save_name if self.transfer_learning else '')),
self.val_mode, dev_er[task], _name)
if self.step >= self.max_step:
self.save_checkpoint(
'last_{}_{}.pth'.format(
task, _name +
(self.save_name if self.transfer_learning else '')),
self.val_mode, dev_er[task], _name)
self.write_log(self.WER,
{'dv_' + task + '_' + _name.lower(): dev_wer[task]})
self.write_log('cer',
{'dv_' + task + '_' + _name.lower(): dev_cer[task]})
# if self.transfer_learning:
# print('[{}] WER {:.4f} / CER {:.4f} on {}'.format(human_format(self.step), dev_wer[task], dev_cer[task], _name))
# Resume training
self.model.train()
if self.transfer_learning:
self.model.encoder.fix_layers(self.fix_enc)
if self.fix_dec and self.model.enable_att:
self.model.decoder.fix_layers()
if self.fix_dec and self.model.enable_ctc:
self.model.fix_ctc_layer()
if self.emb_decoder is not None: self.emb_decoder.train()