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PaddleSpeech/paddlespeech/s2t/models/ds2/deepspeech2.py

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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Deepspeech2 ASR Model"""
import paddle
import paddle.nn.functional as F
from paddle import nn
from paddlespeech.s2t.models.ds2.conv import Conv2dSubsampling4Pure
from paddlespeech.s2t.modules.ctc import CTCDecoder
from paddlespeech.s2t.utils import layer_tools
from paddlespeech.s2t.utils.checkpoint import Checkpoint
from paddlespeech.s2t.utils.log import Log
logger = Log(__name__).getlog()
__all__ = ['DeepSpeech2Model', 'DeepSpeech2InferModel']
class CRNNEncoder(nn.Layer):
def __init__(self,
feat_size,
dict_size,
num_conv_layers=2,
num_rnn_layers=4,
rnn_size=1024,
rnn_direction='forward',
num_fc_layers=2,
fc_layers_size_list=[512, 256],
use_gru=False):
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.num_fc_layers = num_fc_layers
self.rnn_direction = rnn_direction
self.fc_layers_size_list = fc_layers_size_list
self.use_gru = use_gru
self.conv = Conv2dSubsampling4Pure(feat_size, 32, dropout_rate=0.0)
self.output_dim = self.conv.output_dim
i_size = self.conv.output_dim
self.rnn = nn.LayerList()
self.layernorm_list = nn.LayerList()
self.fc_layers_list = nn.LayerList()
if rnn_direction == 'bidirect' or rnn_direction == 'bidirectional':
layernorm_size = 2 * rnn_size
elif rnn_direction == 'forward':
layernorm_size = rnn_size
else:
raise Exception("Wrong rnn direction")
for i in range(0, num_rnn_layers):
if i == 0:
rnn_input_size = i_size
else:
rnn_input_size = layernorm_size
if use_gru is True:
self.rnn.append(
nn.GRU(
input_size=rnn_input_size,
hidden_size=rnn_size,
num_layers=1,
direction=rnn_direction))
else:
self.rnn.append(
nn.LSTM(
input_size=rnn_input_size,
hidden_size=rnn_size,
num_layers=1,
direction=rnn_direction))
self.layernorm_list.append(nn.LayerNorm(layernorm_size))
self.output_dim = layernorm_size
fc_input_size = layernorm_size
for i in range(self.num_fc_layers):
self.fc_layers_list.append(
nn.Linear(fc_input_size, fc_layers_size_list[i]))
fc_input_size = fc_layers_size_list[i]
self.output_dim = fc_layers_size_list[i]
@property
def output_size(self):
return self.output_dim
def forward(self, x, x_lens, init_state_h_box=None, init_state_c_box=None):
"""Compute Encoder outputs
Args:
x (Tensor): [B, T, D]
x_lens (Tensor): [B]
init_state_h_box(Tensor): init_states h for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
init_state_c_box(Tensor): init_states c for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
Return:
x (Tensor): encoder outputs, [B, T, D]
x_lens (Tensor): encoder length, [B]
final_state_h_box(Tensor): final_states h for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
final_state_c_box(Tensor): final_states c for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
"""
if init_state_h_box is not None:
init_state_list = None
if self.use_gru is True:
init_state_h_list = paddle.split(
init_state_h_box, self.num_rnn_layers, axis=0)
init_state_list = init_state_h_list
else:
init_state_h_list = paddle.split(
init_state_h_box, self.num_rnn_layers, axis=0)
init_state_c_list = paddle.split(
init_state_c_box, self.num_rnn_layers, axis=0)
init_state_list = [(init_state_h_list[i], init_state_c_list[i])
for i in range(self.num_rnn_layers)]
else:
init_state_list = [None] * self.num_rnn_layers
x, x_lens = self.conv(x, x_lens)
final_chunk_state_list = []
for i in range(0, self.num_rnn_layers):
x, final_state = self.rnn[i](x, init_state_list[i],
x_lens) #[B, T, D]
final_chunk_state_list.append(final_state)
x = self.layernorm_list[i](x)
for i in range(self.num_fc_layers):
x = self.fc_layers_list[i](x)
x = F.relu(x)
if self.use_gru is True:
final_chunk_state_h_box = paddle.concat(
final_chunk_state_list, axis=0)
final_chunk_state_c_box = init_state_c_box
else:
final_chunk_state_h_list = [
final_chunk_state_list[i][0] for i in range(self.num_rnn_layers)
]
final_chunk_state_c_list = [
final_chunk_state_list[i][1] for i in range(self.num_rnn_layers)
]
final_chunk_state_h_box = paddle.concat(
final_chunk_state_h_list, axis=0)
final_chunk_state_c_box = paddle.concat(
final_chunk_state_c_list, axis=0)
return x, x_lens, final_chunk_state_h_box, final_chunk_state_c_box
def forward_chunk_by_chunk(self, x, x_lens, decoder_chunk_size=8):
"""Compute Encoder outputs
Args:
x (Tensor): [B, T, D]
x_lens (Tensor): [B]
decoder_chunk_size: The chunk size of decoder
Returns:
eouts_list (List of Tensor): The list of encoder outputs in chunk_size: [B, chunk_size, D] * num_chunks
eouts_lens_list (List of Tensor): The list of encoder length in chunk_size: [B] * num_chunks
final_state_h_box(Tensor): final_states h for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
final_state_c_box(Tensor): final_states c for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
"""
subsampling_rate = self.conv.subsampling_rate
receptive_field_length = self.conv.receptive_field_length
chunk_size = (decoder_chunk_size - 1
) * subsampling_rate + receptive_field_length
chunk_stride = subsampling_rate * decoder_chunk_size
max_len = x.shape[1]
assert (chunk_size <= max_len)
eouts_chunk_list = []
eouts_chunk_lens_list = []
if (max_len - chunk_size) % chunk_stride != 0:
padding_len = chunk_stride - (max_len - chunk_size) % chunk_stride
else:
padding_len = 0
padding = paddle.zeros((x.shape[0], padding_len, x.shape[2]))
padded_x = paddle.concat([x, padding], axis=1)
num_chunk = (max_len + padding_len - chunk_size) / chunk_stride + 1
num_chunk = int(num_chunk)
chunk_state_h_box = None
chunk_state_c_box = None
final_state_h_box = None
final_state_c_box = None
for i in range(0, num_chunk):
start = i * chunk_stride
end = start + chunk_size
x_chunk = padded_x[:, start:end, :]
x_len_left = paddle.where(x_lens - i * chunk_stride < 0,
paddle.zeros_like(x_lens),
x_lens - i * chunk_stride)
x_chunk_len_tmp = paddle.ones_like(x_lens) * chunk_size
x_chunk_lens = paddle.where(x_len_left < x_chunk_len_tmp,
x_len_left, x_chunk_len_tmp)
eouts_chunk, eouts_chunk_lens, chunk_state_h_box, chunk_state_c_box = self.forward(
x_chunk, x_chunk_lens, chunk_state_h_box, chunk_state_c_box)
eouts_chunk_list.append(eouts_chunk)
eouts_chunk_lens_list.append(eouts_chunk_lens)
final_state_h_box = chunk_state_h_box
final_state_c_box = chunk_state_c_box
return eouts_chunk_list, eouts_chunk_lens_list, final_state_h_box, final_state_c_box
class DeepSpeech2Model(nn.Layer):
"""The DeepSpeech2 network structure.
:param audio: Audio spectrogram data layer.
:type audio: Variable
:param text: Transcription text data layer.
:type text: Variable
:param audio_len: Valid sequence length data layer.
:type audio_len: Variable
:param feat_size: feature size for audio.
:type feat_size: int
:param dict_size: Dictionary size for tokenized transcription.
:type dict_size: int
:param num_conv_layers: Number of stacking convolution layers.
:type num_conv_layers: int
:param num_rnn_layers: Number of stacking RNN layers.
:type num_rnn_layers: int
:param rnn_size: RNN layer size (dimension of RNN cells).
:type rnn_size: int
:param num_fc_layers: Number of stacking FC layers.
:type num_fc_layers: int
:param fc_layers_size_list: The list of FC layer sizes.
:type fc_layers_size_list: [int,]
:param use_gru: Use gru if set True. Use simple rnn if set False.
:type use_gru: bool
:return: A tuple of an output unnormalized log probability layer (
before softmax) and a ctc cost layer.
:rtype: tuple of LayerOutput
"""
def __init__(
self,
feat_size,
dict_size,
num_conv_layers=2,
num_rnn_layers=4,
rnn_size=1024,
rnn_direction='forward',
num_fc_layers=2,
fc_layers_size_list=[512, 256],
use_gru=False,
blank_id=0,
ctc_grad_norm_type=None, ):
super().__init__()
self.encoder = CRNNEncoder(
feat_size=feat_size,
dict_size=dict_size,
num_conv_layers=num_conv_layers,
num_rnn_layers=num_rnn_layers,
rnn_direction=rnn_direction,
num_fc_layers=num_fc_layers,
fc_layers_size_list=fc_layers_size_list,
rnn_size=rnn_size,
use_gru=use_gru)
self.decoder = CTCDecoder(
odim=dict_size, # <blank> is in vocab
enc_n_units=self.encoder.output_size,
blank_id=blank_id,
dropout_rate=0.0,
reduction=True, # sum
batch_average=True, # sum / batch_size
grad_norm_type=ctc_grad_norm_type)
def forward(self, audio, audio_len, text, text_len):
"""Compute Model loss
Args:
audio (Tensor): [B, T, D]
audio_len (Tensor): [B]
text (Tensor): [B, U]
text_len (Tensor): [B]
Returns:
loss (Tensor): [1]
"""
eouts, eouts_len, final_state_h_box, final_state_c_box = self.encoder(
audio, audio_len, None, None)
loss = self.decoder(eouts, eouts_len, text, text_len)
return loss
@paddle.no_grad()
def decode(self, audio, audio_len):
# decoders only accept string encoded in utf-8
# Make sure the decoder has been initialized
eouts, eouts_len, final_state_h_box, final_state_c_box = self.encoder(
audio, audio_len, None, None)
probs = self.decoder.softmax(eouts)
batch_size = probs.shape[0]
self.decoder.reset_decoder(batch_size=batch_size)
self.decoder.next(probs, eouts_len)
trans_best, trans_beam = self.decoder.decode()
return trans_best
@classmethod
def from_pretrained(cls, dataloader, config, checkpoint_path):
"""Build a DeepSpeech2Model model from a pretrained model.
Parameters
----------
dataloader: paddle.io.DataLoader
config: yacs.config.CfgNode
model configs
checkpoint_path: Path or str
the path of pretrained model checkpoint, without extension name
Returns
-------
DeepSpeech2Model
The model built from pretrained result.
"""
model = cls(
feat_size=dataloader.feat_dim,
dict_size=dataloader.vocab_size,
num_conv_layers=config.num_conv_layers,
num_rnn_layers=config.num_rnn_layers,
rnn_size=config.rnn_layer_size,
rnn_direction=config.rnn_direction,
num_fc_layers=config.num_fc_layers,
fc_layers_size_list=config.fc_layers_size_list,
use_gru=config.use_gru,
blank_id=config.blank_id,
ctc_grad_norm_type=config.get('ctc_grad_norm_type', None), )
infos = Checkpoint().load_parameters(
model, checkpoint_path=checkpoint_path)
logger.info(f"checkpoint info: {infos}")
layer_tools.summary(model)
return model
@classmethod
def from_config(cls, config):
"""Build a DeepSpeec2Model from config
Parameters
config: yacs.config.CfgNode
config
Returns
-------
DeepSpeech2Model
The model built from config.
"""
model = cls(
feat_size=config.input_dim,
dict_size=config.output_dim,
num_conv_layers=config.num_conv_layers,
num_rnn_layers=config.num_rnn_layers,
rnn_size=config.rnn_layer_size,
rnn_direction=config.rnn_direction,
num_fc_layers=config.num_fc_layers,
fc_layers_size_list=config.fc_layers_size_list,
use_gru=config.use_gru,
blank_id=config.blank_id,
ctc_grad_norm_type=config.get('ctc_grad_norm_type', None), )
return model
class DeepSpeech2InferModel(DeepSpeech2Model):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self,
audio_chunk,
audio_chunk_lens,
chunk_state_h_box=None,
chunk_state_c_box=None):
if self.encoder.rnn_direction == "forward":
eouts_chunk, eouts_chunk_lens, final_state_h_box, final_state_c_box = self.encoder(
audio_chunk, audio_chunk_lens, chunk_state_h_box,
chunk_state_c_box)
probs_chunk = self.decoder.softmax(eouts_chunk)
return probs_chunk, eouts_chunk_lens, final_state_h_box, final_state_c_box
elif self.encoder.rnn_direction == "bidirect":
eouts, eouts_len, _, _ = self.encoder(audio_chunk, audio_chunk_lens)
probs = self.decoder.softmax(eouts)
return probs, eouts_len
else:
raise Exception("wrong model type")
def export(self):
if self.encoder.rnn_direction == "forward":
static_model = paddle.jit.to_static(
self,
input_spec=[
paddle.static.InputSpec(
shape=[None, None, self.encoder.feat_size
], #[B, chunk_size, feat_dim]
dtype='float32'),
paddle.static.InputSpec(shape=[None],
dtype='int64'), # audio_length, [B]
paddle.static.InputSpec(
shape=[None, None, None], dtype='float32'),
paddle.static.InputSpec(
shape=[None, None, None], dtype='float32')
])
elif self.encoder.rnn_direction == "bidirect":
static_model = paddle.jit.to_static(
self,
input_spec=[
paddle.static.InputSpec(
shape=[None, None, self.encoder.feat_size],
dtype='float32'), # audio, [B,T,D]
paddle.static.InputSpec(shape=[None],
dtype='int64'), # audio_length, [B]
])
else:
raise Exception("wrong model type")
return static_model
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