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Merge pull request #820 from PaddlePaddle/ctc

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more ctc config
pull/822/head
Hui Zhang 3 years ago committed by GitHub
parent 5e063adf44 2480be8ebc
commit 91bc5959a9
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26 changed files with 163 additions and 112 deletions
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61
deepspeech/exps/u2/model.py
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@ -34,6 +34,7 @@ from deepspeech.io.sampler import SortagradDistributedBatchSampler
from deepspeech.models.u2 import U2Model from deepspeech.models.u2 import U2Model
from deepspeech.training.optimizer import OptimizerFactory from deepspeech.training.optimizer import OptimizerFactory
from deepspeech.training.scheduler import LRSchedulerFactory from deepspeech.training.scheduler import LRSchedulerFactory
from deepspeech.training.timer import Timer
from deepspeech.training.trainer import Trainer from deepspeech.training.trainer import Trainer
from deepspeech.utils import ctc_utils from deepspeech.utils import ctc_utils
from deepspeech.utils import error_rate from deepspeech.utils import error_rate
@ -184,40 +185,42 @@ class U2Trainer(Trainer):
self.save(tag='init') self.save(tag='init')
self.lr_scheduler.step(self.iteration) self.lr_scheduler.step(self.iteration)
if self.parallel: if self.parallel and hasattr(self.train_loader, 'batch_sampler'):
self.train_loader.batch_sampler.set_epoch(self.epoch) self.train_loader.batch_sampler.set_epoch(self.epoch)
logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}") logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
while self.epoch < self.config.training.n_epoch: while self.epoch < self.config.training.n_epoch:
self.model.train() with Timer("Epoch-Train Time Cost: {}"):
try: self.model.train()
data_start_time = time.time() try:
for batch_index, batch in enumerate(self.train_loader):
dataload_time = time.time() - data_start_time
msg = "Train: Rank: {}, ".format(dist.get_rank())
msg += "epoch: {}, ".format(self.epoch)
msg += "step: {}, ".format(self.iteration)
msg += "batch : {}/{}, ".format(batch_index + 1,
len(self.train_loader))
msg += "lr: {:>.8f}, ".format(self.lr_scheduler())
msg += "data time: {:>.3f}s, ".format(dataload_time)
self.train_batch(batch_index, batch, msg)
data_start_time = time.time() data_start_time = time.time()
except Exception as e: for batch_index, batch in enumerate(self.train_loader):
logger.error(e) dataload_time = time.time() - data_start_time
raise e msg = "Train: Rank: {}, ".format(dist.get_rank())
msg += "epoch: {}, ".format(self.epoch)
total_loss, num_seen_utts = self.valid() msg += "step: {}, ".format(self.iteration)
if dist.get_world_size() > 1: msg += "batch : {}/{}, ".format(batch_index + 1,
num_seen_utts = paddle.to_tensor(num_seen_utts) len(self.train_loader))
# the default operator in all_reduce function is sum. msg += "lr: {:>.8f}, ".format(self.lr_scheduler())
dist.all_reduce(num_seen_utts) msg += "data time: {:>.3f}s, ".format(dataload_time)
total_loss = paddle.to_tensor(total_loss) self.train_batch(batch_index, batch, msg)
dist.all_reduce(total_loss) data_start_time = time.time()
cv_loss = total_loss / num_seen_utts except Exception as e:
cv_loss = float(cv_loss) logger.error(e)
else: raise e
cv_loss = total_loss / num_seen_utts
with Timer("Eval Time Cost: {}"):
total_loss, num_seen_utts = self.valid()
if dist.get_world_size() > 1:
num_seen_utts = paddle.to_tensor(num_seen_utts)
# the default operator in all_reduce function is sum.
dist.all_reduce(num_seen_utts)
total_loss = paddle.to_tensor(total_loss)
dist.all_reduce(total_loss)
cv_loss = total_loss / num_seen_utts
cv_loss = float(cv_loss)
else:
cv_loss = total_loss / num_seen_utts
logger.info( logger.info(
'Epoch {} Val info val_loss {}'.format(self.epoch, cv_loss)) 'Epoch {} Val info val_loss {}'.format(self.epoch, cv_loss))

59
deepspeech/exps/u2_kaldi/model.py
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@ -32,6 +32,7 @@ from deepspeech.io.dataloader import BatchDataLoader
from deepspeech.models.u2 import U2Model from deepspeech.models.u2 import U2Model
from deepspeech.training.optimizer import OptimizerFactory from deepspeech.training.optimizer import OptimizerFactory
from deepspeech.training.scheduler import LRSchedulerFactory from deepspeech.training.scheduler import LRSchedulerFactory
from deepspeech.training.timer import Timer
from deepspeech.training.trainer import Trainer from deepspeech.training.trainer import Trainer
from deepspeech.utils import ctc_utils from deepspeech.utils import ctc_utils
from deepspeech.utils import error_rate from deepspeech.utils import error_rate
@ -190,35 +191,37 @@ class U2Trainer(Trainer):
logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}") logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
while self.epoch < self.config.training.n_epoch: while self.epoch < self.config.training.n_epoch:
self.model.train() with Timer("Epoch-Train Time Cost: {}"):
try: self.model.train()
data_start_time = time.time() try:
for batch_index, batch in enumerate(self.train_loader):
dataload_time = time.time() - data_start_time
msg = "Train: Rank: {}, ".format(dist.get_rank())
msg += "epoch: {}, ".format(self.epoch)
msg += "step: {}, ".format(self.iteration)
msg += "batch : {}/{}, ".format(batch_index + 1,
len(self.train_loader))
msg += "lr: {:>.8f}, ".format(self.lr_scheduler())
msg += "data time: {:>.3f}s, ".format(dataload_time)
self.train_batch(batch_index, batch, msg)
data_start_time = time.time() data_start_time = time.time()
except Exception as e: for batch_index, batch in enumerate(self.train_loader):
logger.error(e) dataload_time = time.time() - data_start_time
raise e msg = "Train: Rank: {}, ".format(dist.get_rank())
msg += "epoch: {}, ".format(self.epoch)
total_loss, num_seen_utts = self.valid() msg += "step: {}, ".format(self.iteration)
if dist.get_world_size() > 1: msg += "batch : {}/{}, ".format(batch_index + 1,
num_seen_utts = paddle.to_tensor(num_seen_utts) len(self.train_loader))
# the default operator in all_reduce function is sum. msg += "lr: {:>.8f}, ".format(self.lr_scheduler())
dist.all_reduce(num_seen_utts) msg += "data time: {:>.3f}s, ".format(dataload_time)
total_loss = paddle.to_tensor(total_loss) self.train_batch(batch_index, batch, msg)
dist.all_reduce(total_loss) data_start_time = time.time()
cv_loss = total_loss / num_seen_utts except Exception as e:
cv_loss = float(cv_loss) logger.error(e)
else: raise e
cv_loss = total_loss / num_seen_utts
with Timer("Eval Time Cost: {}"):
total_loss, num_seen_utts = self.valid()
if dist.get_world_size() > 1:
num_seen_utts = paddle.to_tensor(num_seen_utts)
# the default operator in all_reduce function is sum.
dist.all_reduce(num_seen_utts)
total_loss = paddle.to_tensor(total_loss)
dist.all_reduce(total_loss)
cv_loss = total_loss / num_seen_utts
cv_loss = float(cv_loss)
else:
cv_loss = total_loss / num_seen_utts
logger.info( logger.info(
'Epoch {} Val info val_loss {}'.format(self.epoch, cv_loss)) 'Epoch {} Val info val_loss {}'.format(self.epoch, cv_loss))

59
deepspeech/exps/u2_st/model.py
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@ -38,6 +38,7 @@ from deepspeech.io.sampler import SortagradDistributedBatchSampler
from deepspeech.models.u2_st import U2STModel from deepspeech.models.u2_st import U2STModel
from deepspeech.training.gradclip import ClipGradByGlobalNormWithLog from deepspeech.training.gradclip import ClipGradByGlobalNormWithLog
from deepspeech.training.scheduler import WarmupLR from deepspeech.training.scheduler import WarmupLR
from deepspeech.training.timer import Timer
from deepspeech.training.trainer import Trainer from deepspeech.training.trainer import Trainer
from deepspeech.utils import bleu_score from deepspeech.utils import bleu_score
from deepspeech.utils import ctc_utils from deepspeech.utils import ctc_utils
@ -207,35 +208,37 @@ class U2STTrainer(Trainer):
logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}") logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
while self.epoch < self.config.training.n_epoch: while self.epoch < self.config.training.n_epoch:
self.model.train() with Timer("Epoch-Train Time Cost: {}"):
try: self.model.train()
data_start_time = time.time() try:
for batch_index, batch in enumerate(self.train_loader):
dataload_time = time.time() - data_start_time
msg = "Train: Rank: {}, ".format(dist.get_rank())
msg += "epoch: {}, ".format(self.epoch)
msg += "step: {}, ".format(self.iteration)
msg += "batch : {}/{}, ".format(batch_index + 1,
len(self.train_loader))
msg += "lr: {:>.8f}, ".format(self.lr_scheduler())
msg += "data time: {:>.3f}s, ".format(dataload_time)
self.train_batch(batch_index, batch, msg)
data_start_time = time.time() data_start_time = time.time()
except Exception as e: for batch_index, batch in enumerate(self.train_loader):
logger.error(e) dataload_time = time.time() - data_start_time
raise e msg = "Train: Rank: {}, ".format(dist.get_rank())
msg += "epoch: {}, ".format(self.epoch)
total_loss, num_seen_utts = self.valid() msg += "step: {}, ".format(self.iteration)
if dist.get_world_size() > 1: msg += "batch : {}/{}, ".format(batch_index + 1,
num_seen_utts = paddle.to_tensor(num_seen_utts) len(self.train_loader))
# the default operator in all_reduce function is sum. msg += "lr: {:>.8f}, ".format(self.lr_scheduler())
dist.all_reduce(num_seen_utts) msg += "data time: {:>.3f}s, ".format(dataload_time)
total_loss = paddle.to_tensor(total_loss) self.train_batch(batch_index, batch, msg)
dist.all_reduce(total_loss) data_start_time = time.time()
cv_loss = total_loss / num_seen_utts except Exception as e:
cv_loss = float(cv_loss) logger.error(e)
else: raise e
cv_loss = total_loss / num_seen_utts
with Timer("Eval Time Cost: {}"):
total_loss, num_seen_utts = self.valid()
if dist.get_world_size() > 1:
num_seen_utts = paddle.to_tensor(num_seen_utts)
# the default operator in all_reduce function is sum.
dist.all_reduce(num_seen_utts)
total_loss = paddle.to_tensor(total_loss)
dist.all_reduce(total_loss)
cv_loss = total_loss / num_seen_utts
cv_loss = float(cv_loss)
else:
cv_loss = total_loss / num_seen_utts
logger.info( logger.info(
'Epoch {} Val info val_loss {}'.format(self.epoch, cv_loss)) 'Epoch {} Val info val_loss {}'.format(self.epoch, cv_loss))

18
deepspeech/models/u2.py
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@ -115,7 +115,8 @@ class U2BaseModel(nn.Layer):
ctc_weight: float=0.5, ctc_weight: float=0.5,
ignore_id: int=IGNORE_ID, ignore_id: int=IGNORE_ID,
lsm_weight: float=0.0, lsm_weight: float=0.0,
length_normalized_loss: bool=False): length_normalized_loss: bool=False,
**kwargs):
assert 0.0 <= ctc_weight <= 1.0, ctc_weight assert 0.0 <= ctc_weight <= 1.0, ctc_weight
super().__init__() super().__init__()
@ -661,9 +662,7 @@ class U2BaseModel(nn.Layer):
xs, offset, required_cache_size, subsampling_cache, xs, offset, required_cache_size, subsampling_cache,
elayers_output_cache, conformer_cnn_cache) elayers_output_cache, conformer_cnn_cache)
# @jit.to_static([ # @jit.to_static
# paddle.static.InputSpec(shape=[1, None, feat_dim],dtype='float32'), # audio feat, [B,T,D]
# ])
def ctc_activation(self, xs: paddle.Tensor) -> paddle.Tensor: def ctc_activation(self, xs: paddle.Tensor) -> paddle.Tensor:
""" Export interface for c++ call, apply linear transform and log """ Export interface for c++ call, apply linear transform and log
softmax before ctc softmax before ctc
@ -830,6 +829,7 @@ class U2Model(U2BaseModel):
Returns: Returns:
int, nn.Layer, nn.Layer, nn.Layer: vocab size, encoder, decoder, ctc int, nn.Layer, nn.Layer, nn.Layer: vocab size, encoder, decoder, ctc
""" """
# cmvn
if configs['cmvn_file'] is not None: if configs['cmvn_file'] is not None:
mean, istd = load_cmvn(configs['cmvn_file'], mean, istd = load_cmvn(configs['cmvn_file'],
configs['cmvn_file_type']) configs['cmvn_file_type'])
@ -839,11 +839,13 @@ class U2Model(U2BaseModel):
else: else:
global_cmvn = None global_cmvn = None
# input & output dim
input_dim = configs['input_dim'] input_dim = configs['input_dim']
vocab_size = configs['output_dim'] vocab_size = configs['output_dim']
assert input_dim != 0, input_dim assert input_dim != 0, input_dim
assert vocab_size != 0, vocab_size assert vocab_size != 0, vocab_size
# encoder
encoder_type = configs.get('encoder', 'transformer') encoder_type = configs.get('encoder', 'transformer')
logger.info(f"U2 Encoder type: {encoder_type}") logger.info(f"U2 Encoder type: {encoder_type}")
if encoder_type == 'transformer': if encoder_type == 'transformer':
@ -855,17 +857,21 @@ class U2Model(U2BaseModel):
else: else:
raise ValueError(f"not support encoder type:{encoder_type}") raise ValueError(f"not support encoder type:{encoder_type}")
# decoder
decoder = TransformerDecoder(vocab_size, decoder = TransformerDecoder(vocab_size,
encoder.output_size(), encoder.output_size(),
**configs['decoder_conf']) **configs['decoder_conf'])
# ctc decoder and ctc loss
model_conf = configs['model_conf']
ctc = CTCDecoder( ctc = CTCDecoder(
odim=vocab_size, odim=vocab_size,
enc_n_units=encoder.output_size(), enc_n_units=encoder.output_size(),
blank_id=0, blank_id=0,
dropout_rate=0.0, dropout_rate=model_conf['ctc_dropoutrate'],
reduction=True, # sum reduction=True, # sum
batch_average=True, # sum / batch_size batch_average=True, # sum / batch_size
grad_norm_type='instance') grad_norm_type=model_conf['ctc_grad_norm_type'])
return vocab_size, encoder, decoder, ctc return vocab_size, encoder, decoder, ctc

16
deepspeech/models/u2_st.py
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@ -413,26 +413,26 @@ class U2STBaseModel(nn.Layer):
best_hyps = best_hyps[:, 1:] best_hyps = best_hyps[:, 1:]
return best_hyps return best_hyps
@jit.to_static # @jit.to_static
def subsampling_rate(self) -> int: def subsampling_rate(self) -> int:
""" Export interface for c++ call, return subsampling_rate of the """ Export interface for c++ call, return subsampling_rate of the
model model
""" """
return self.encoder.embed.subsampling_rate return self.encoder.embed.subsampling_rate
@jit.to_static # @jit.to_static
def right_context(self) -> int: def right_context(self) -> int:
""" Export interface for c++ call, return right_context of the model """ Export interface for c++ call, return right_context of the model
""" """
return self.encoder.embed.right_context return self.encoder.embed.right_context
@jit.to_static # @jit.to_static
def sos_symbol(self) -> int: def sos_symbol(self) -> int:
""" Export interface for c++ call, return sos symbol id of the model """ Export interface for c++ call, return sos symbol id of the model
""" """
return self.sos return self.sos
@jit.to_static # @jit.to_static
def eos_symbol(self) -> int: def eos_symbol(self) -> int:
""" Export interface for c++ call, return eos symbol id of the model """ Export interface for c++ call, return eos symbol id of the model
""" """
@ -468,7 +468,7 @@ class U2STBaseModel(nn.Layer):
xs, offset, required_cache_size, subsampling_cache, xs, offset, required_cache_size, subsampling_cache,
elayers_output_cache, conformer_cnn_cache) elayers_output_cache, conformer_cnn_cache)
@jit.to_static # @jit.to_static
def ctc_activation(self, xs: paddle.Tensor) -> paddle.Tensor: def ctc_activation(self, xs: paddle.Tensor) -> paddle.Tensor:
""" Export interface for c++ call, apply linear transform and log """ Export interface for c++ call, apply linear transform and log
softmax before ctc softmax before ctc
@ -643,14 +643,16 @@ class U2STModel(U2STBaseModel):
decoder = TransformerDecoder(vocab_size, decoder = TransformerDecoder(vocab_size,
encoder.output_size(), encoder.output_size(),
**configs['decoder_conf']) **configs['decoder_conf'])
# ctc decoder and ctc loss
model_conf = configs['model_conf']
ctc = CTCDecoder( ctc = CTCDecoder(
odim=vocab_size, odim=vocab_size,
enc_n_units=encoder.output_size(), enc_n_units=encoder.output_size(),
blank_id=0, blank_id=0,
dropout_rate=0.0, dropout_rate=model_conf['ctc_dropout_rate'],
reduction=True, # sum reduction=True, # sum
batch_average=True, # sum / batch_size batch_average=True, # sum / batch_size
grad_norm_type='instance') grad_norm_type=model_conf['ctc_grad_norm_type'])
return vocab_size, encoder, (st_decoder, decoder, ctc) return vocab_size, encoder, (st_decoder, decoder, ctc)
else: else:

6
deepspeech/modules/loss.py
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@ -36,16 +36,16 @@ class CTCLoss(nn.Layer):
f"CTCLoss Loss reduction: {reduction}, div-bs: {batch_average}") f"CTCLoss Loss reduction: {reduction}, div-bs: {batch_average}")
# instance for norm_by_times # instance for norm_by_times
# batchsize for norm_by_batchsize # batch for norm_by_batchsize
# frame for norm_by_total_logits_len # frame for norm_by_total_logits_len
assert grad_norm_type in ('instance', 'batchsize', 'frame', None) assert grad_norm_type in ('instance', 'batch', 'frame', None)
self.norm_by_times = False self.norm_by_times = False
self.norm_by_batchsize = False self.norm_by_batchsize = False
self.norm_by_total_logits_len = False self.norm_by_total_logits_len = False
logger.info(f"CTCLoss Grad Norm Type: {grad_norm_type}") logger.info(f"CTCLoss Grad Norm Type: {grad_norm_type}")
if grad_norm_type == 'instance': if grad_norm_type == 'instance':
self.norm_by_times = True self.norm_by_times = True
if grad_norm_type == 'batchsize': if grad_norm_type == 'batch':
self.norm_by_times = True self.norm_by_times = True
if grad_norm_type == 'frame': if grad_norm_type == 'frame':
self.norm_by_total_logits_len = True self.norm_by_total_logits_len = True

16
deepspeech/training/gradclip.py
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@ -47,9 +47,10 @@ class ClipGradByGlobalNormWithLog(paddle.nn.ClipGradByGlobalNorm):
sum_square = layers.reduce_sum(square) sum_square = layers.reduce_sum(square)
sum_square_list.append(sum_square) sum_square_list.append(sum_square)
# debug log # debug log, not dump all since slow down train process
logger.debug( if i < 10:
f"Grad Before Clip: {p.name}: {float(sum_square.sqrt()) }") logger.debug(
f"Grad Before Clip: {p.name}: {float(sum_square.sqrt()) }")
# all parameters have been filterd out # all parameters have been filterd out
if len(sum_square_list) == 0: if len(sum_square_list) == 0:
@ -75,9 +76,10 @@ class ClipGradByGlobalNormWithLog(paddle.nn.ClipGradByGlobalNorm):
new_grad = layers.elementwise_mul(x=g, y=clip_var) new_grad = layers.elementwise_mul(x=g, y=clip_var)
params_and_grads.append((p, new_grad)) params_and_grads.append((p, new_grad))
# debug log # debug log, not dump all since slow down train process
logger.debug( if i < 10:
f"Grad After Clip: {p.name}: {float(new_grad.square().sum().sqrt())}" logger.debug(
) f"Grad After Clip: {p.name}: {float(new_grad.square().sum().sqrt())}"
)
return params_and_grads return params_and_grads

2
doc/src/deepspeech_architecture.md
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@ -183,5 +183,3 @@ bash run.sh --stage 0 --stop_stage 2 --model_type offline --conf_path conf/deeps
cd examples/aishell/s0 cd examples/aishell/s0
bash run.sh --stage 3 --stop_stage 5 --model_type offline --conf_path conf/deepspeech2.yaml bash run.sh --stage 3 --stop_stage 5 --model_type offline --conf_path conf/deepspeech2.yaml
``` ```

2
examples/aishell/s1/conf/chunk_conformer.yaml
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@ -76,6 +76,8 @@ model:
# hybrid CTC/attention # hybrid CTC/attention
model_conf: model_conf:
ctc_weight: 0.3 ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: instance
lsm_weight: 0.1 # label smoothing option lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false length_normalized_loss: false

2
examples/aishell/s1/conf/conformer.yaml
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@ -71,6 +71,8 @@ model:
# hybrid CTC/attention # hybrid CTC/attention
model_conf: model_conf:
ctc_weight: 0.3 ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: instance
lsm_weight: 0.1 # label smoothing option lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false length_normalized_loss: false

2
examples/librispeech/s1/conf/chunk_conformer.yaml
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@ -76,6 +76,8 @@ model:
# hybrid CTC/attention # hybrid CTC/attention
model_conf: model_conf:
ctc_weight: 0.3 ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: instance
lsm_weight: 0.1 # label smoothing option lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false length_normalized_loss: false

2
examples/librispeech/s1/conf/chunk_transformer.yaml
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@ -69,6 +69,8 @@ model:
# hybrid CTC/attention # hybrid CTC/attention
model_conf: model_conf:
ctc_weight: 0.3 ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: instance
lsm_weight: 0.1 # label smoothing option lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false length_normalized_loss: false

2
examples/librispeech/s1/conf/conformer.yaml
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@ -72,6 +72,8 @@ model:
# hybrid CTC/attention # hybrid CTC/attention
model_conf: model_conf:
ctc_weight: 0.3 ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: instance
lsm_weight: 0.1 # label smoothing option lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false length_normalized_loss: false

4
examples/librispeech/s1/conf/transformer.yaml
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@ -33,7 +33,7 @@ collator:
keep_transcription_text: False keep_transcription_text: False
sortagrad: True sortagrad: True
shuffle_method: batch_shuffle shuffle_method: batch_shuffle
num_workers: 2 num_workers: 0
# network architecture # network architecture
@ -67,6 +67,8 @@ model:
# hybrid CTC/attention # hybrid CTC/attention
model_conf: model_conf:
ctc_weight: 0.3 ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: instance
lsm_weight: 0.1 # label smoothing option lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false length_normalized_loss: false

2
examples/librispeech/s1/local/train.sh
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@ -20,7 +20,7 @@ echo "using ${device}..."
mkdir -p exp mkdir -p exp
seed=10086 seed=10086
if [ ${seed} != 0]; then if [ ${seed} != 0 ]; then
export FLAGS_cudnn_deterministic=True export FLAGS_cudnn_deterministic=True
fi fi

2
examples/librispeech/s2/conf/chunk_conformer.yaml
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@ -76,6 +76,8 @@ model:
# hybrid CTC/attention # hybrid CTC/attention
model_conf: model_conf:
ctc_weight: 0.3 ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: instance
lsm_weight: 0.1 # label smoothing option lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false length_normalized_loss: false

2
examples/librispeech/s2/conf/chunk_transformer.yaml
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@ -69,6 +69,8 @@ model:
# hybrid CTC/attention # hybrid CTC/attention
model_conf: model_conf:
ctc_weight: 0.3 ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: instance
lsm_weight: 0.1 # label smoothing option lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false length_normalized_loss: false

2
examples/librispeech/s2/conf/conformer.yaml
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@ -72,6 +72,8 @@ model:
# hybrid CTC/attention # hybrid CTC/attention
model_conf: model_conf:
ctc_weight: 0.3 ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: instance
lsm_weight: 0.1 # label smoothing option lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false length_normalized_loss: false

2
examples/librispeech/s2/conf/transformer.yaml
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@ -58,6 +58,8 @@ model:
# hybrid CTC/attention # hybrid CTC/attention
model_conf: model_conf:
ctc_weight: 0.3 ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: instance
lsm_weight: 0.1 # label smoothing option lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false length_normalized_loss: false

2
examples/ted_en_zh/t0/conf/transformer.yaml
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@ -68,6 +68,8 @@ model:
model_conf: model_conf:
asr_weight: 0.0 asr_weight: 0.0
ctc_weight: 0.0 ctc_weight: 0.0
ctc_dropoutrate: 0.0
ctc_grad_norm_type: instance
lsm_weight: 0.1 # label smoothing option lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false length_normalized_loss: false

2
examples/ted_en_zh/t0/conf/transformer_joint_noam.yaml
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@ -68,6 +68,8 @@ model:
model_conf: model_conf:
asr_weight: 0.5 asr_weight: 0.5
ctc_weight: 0.3 ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: instance
lsm_weight: 0.1 # label smoothing option lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false length_normalized_loss: false

2
examples/timit/s1/conf/transformer.yaml
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@ -66,6 +66,8 @@ model:
# hybrid CTC/attention # hybrid CTC/attention
model_conf: model_conf:
ctc_weight: 0.3 ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: instance
lsm_weight: 0.1 # label smoothing option lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false length_normalized_loss: false

2
examples/tiny/s1/conf/chunk_confermer.yaml
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@ -76,6 +76,8 @@ model:
# hybrid CTC/attention # hybrid CTC/attention
model_conf: model_conf:
ctc_weight: 0.3 ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: instance
lsm_weight: 0.1 # label smoothing option lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false length_normalized_loss: false

2
examples/tiny/s1/conf/chunk_transformer.yaml
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@ -69,6 +69,8 @@ model:
# hybrid CTC/attention # hybrid CTC/attention
model_conf: model_conf:
ctc_weight: 0.3 ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: instance
lsm_weight: 0.1 # label smoothing option lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false length_normalized_loss: false

2
examples/tiny/s1/conf/conformer.yaml
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@ -72,6 +72,8 @@ model:
# hybrid CTC/attention # hybrid CTC/attention
model_conf: model_conf:
ctc_weight: 0.3 ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: instance
lsm_weight: 0.1 # label smoothing option lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false length_normalized_loss: false

2
examples/tiny/s1/conf/transformer.yaml
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@ -66,6 +66,8 @@ model:
# hybrid CTC/attention # hybrid CTC/attention
model_conf: model_conf:
ctc_weight: 0.3 ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: instance
lsm_weight: 0.1 # label smoothing option lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false length_normalized_loss: false

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