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修改了deepspeech2.py部分LSTM和GRU的代码，增加了LayerNorm

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pull/735/head
huangyuxin 3 years ago
parent ce1e8ab5b6
commit 2cacbaf48e
5 changed files with 62 additions and 14 deletions
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6
deepspeech/exps/deepspeech2/model.py
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@ -127,7 +127,8 @@ class DeepSpeech2Trainer(Trainer):
num_rnn_layers=config.model.num_rnn_layers,
rnn_size=config.model.rnn_layer_size,
use_gru=config.model.use_gru,
share_rnn_weights=config.model.share_rnn_weights)
share_rnn_weights=config.model.share_rnn_weights,
apply_online=config.model.apply_online)
if self.parallel:
model = paddle.DataParallel(model)
@ -374,7 +375,8 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
num_rnn_layers=config.model.num_rnn_layers,
rnn_size=config.model.rnn_layer_size,
use_gru=config.model.use_gru,
share_rnn_weights=config.model.share_rnn_weights)
share_rnn_weights=config.model.share_rnn_weights,
apply_online=config.model.apply_online)
self.model = model
logger.info("Setup model!")

65
deepspeech/models/ds2/deepspeech2.py
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@ -25,6 +25,11 @@ from deepspeech.utils import layer_tools
from deepspeech.utils.checkpoint import Checkpoint
from deepspeech.utils.log import Log
from paddle.nn import LSTM, GRU
from paddle.nn import LayerNorm
from paddle.nn import LayerList
logger = Log(__name__).getlog()
__all__ = ['DeepSpeech2Model', 'DeepSpeech2InferMode']
@ -38,25 +43,50 @@ class CRNNEncoder(nn.Layer):
num_rnn_layers=3,
rnn_size=1024,
use_gru=False,
share_rnn_weights=True):
share_rnn_weights=True,
apply_online=True):
super().__init__()
self.rnn_size = rnn_size
self.feat_size = feat_size # 161 for linear
self.dict_size = dict_size
self.num_rnn_layers = num_rnn_layers
self.apply_online = apply_online
self.conv = ConvStack(feat_size, num_conv_layers)
i_size = self.conv.output_height # H after conv stack
self.rnn = LayerList()
self.layernorm_list = LayerList()
if (apply_online == True):
rnn_direction = 'forward'
else:
rnn_direction = 'bidirect'
if use_gru == True:
self.rnn.append(GRU(input_size=i_size, hidden_size=rnn_size, num_layers=1, direction = rnn_direction))
self.layernorm_list.append(LayerNorm(rnn_size))
for i in range(1, num_rnn_layers):
self.rnn.append(GRU(input_size=rnn_size, hidden_size=rnn_size, num_layers=1, direction = rnn_direction))
self.layernorm_list.append(LayerNorm(rnn_size))
else:
self.rnn.append(LSTM(input_size=i_size, hidden_size=rnn_size, num_layers=1, direction = rnn_direction))
self.layernorm_list.append(LayerNorm(rnn_size))
for i in range(1, num_rnn_layers):
self.rnn.append(LSTM(input_size=rnn_size, hidden_size=rnn_size, num_layers=1, direction = rnn_direction))
self.layernorm_list.append(LayerNorm(rnn_size))
"""
self.rnn = RNNStack(
i_size=i_size,
h_size=rnn_size,
num_stacks=num_rnn_layers,
use_gru=use_gru,
share_rnn_weights=share_rnn_weights)
"""
@property
def output_size(self):
return self.rnn_size * 2
return self.rnn_size
def forward(self, audio, audio_len):
"""Compute Encoder outputs
@ -86,7 +116,15 @@ class CRNNEncoder(nn.Layer):
x = x.reshape([0, 0, -1]) #[B, T, C*D]
# remove padding part
x, x_lens = self.rnn(x, x_lens) #[B, T, D]
print ("x.shape:", x.shape)
x, output_state = self.rnn[0](x, None, x_lens)
x = self.layernorm_list[0](x)
for i in range(1, self.num_rnn_layers):
x, output_state = self.rnn[i](x, output_state, x_lens) #[B, T, D]
x = self.layernorm_list[i](x)
"""
x, x_lens = self.rnn(x, x_lens)
"""
return x, x_lens
@ -141,7 +179,8 @@ class DeepSpeech2Model(nn.Layer):
num_rnn_layers=3,
rnn_size=1024,
use_gru=False,
share_rnn_weights=True):
share_rnn_weights=True,
apply_online = True):
super().__init__()
self.encoder = CRNNEncoder(
feat_size=feat_size,
@ -150,8 +189,9 @@ class DeepSpeech2Model(nn.Layer):
num_rnn_layers=num_rnn_layers,
rnn_size=rnn_size,
use_gru=use_gru,
share_rnn_weights=share_rnn_weights)
assert (self.encoder.output_size == rnn_size * 2)
share_rnn_weights=share_rnn_weights,
apply_online=apply_online)
assert (self.encoder.output_size == rnn_size)
self.decoder = CTCDecoder(
odim=dict_size, # <blank> is in vocab
@ -221,7 +261,8 @@ class DeepSpeech2Model(nn.Layer):
num_rnn_layers=config.model.num_rnn_layers,
rnn_size=config.model.rnn_layer_size,
use_gru=config.model.use_gru,
share_rnn_weights=config.model.share_rnn_weights)
share_rnn_weights=config.model.share_rnn_weights,
apply_online=config.model.apply_online)
infos = Checkpoint().load_parameters(
model, checkpoint_path=checkpoint_path)
logger.info(f"checkpoint info: {infos}")
@ -237,7 +278,8 @@ class DeepSpeech2InferModel(DeepSpeech2Model):
num_rnn_layers=3,
rnn_size=1024,
use_gru=False,
share_rnn_weights=True):
share_rnn_weights=True,
apply_online = True):
super().__init__(
feat_size=feat_size,
dict_size=dict_size,
@ -245,7 +287,8 @@ class DeepSpeech2InferModel(DeepSpeech2Model):
num_rnn_layers=num_rnn_layers,
rnn_size=rnn_size,
use_gru=use_gru,
share_rnn_weights=share_rnn_weights)
share_rnn_weights=share_rnn_weights,
apply_online=apply_online)
def forward(self, audio, audio_len):
"""export model function

3
examples/aishell/s0/conf/deepspeech2.yaml
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@ -36,10 +36,11 @@ collator:
model:
num_conv_layers: 2
num_rnn_layers: 3
num_rnn_layers: 4
rnn_layer_size: 1024
use_gru: True
share_rnn_weights: False
apply_online: False
training:
n_epoch: 50

1
examples/librispeech/s0/conf/deepspeech2.yaml
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@ -40,6 +40,7 @@ model:
rnn_layer_size: 2048
use_gru: False
share_rnn_weights: True
apply_online: False
training:
n_epoch: 50

1
examples/tiny/s0/conf/deepspeech2.yaml
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@ -41,6 +41,7 @@ model:
rnn_layer_size: 2048
use_gru: False
share_rnn_weights: True
apply_online: True
training:
n_epoch: 10

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