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PaddleSpeech/paddlespeech/text/speechtask/punctuation_restoration/model/blstm.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.
import paddle
import paddle.nn as nn
import paddle.nn.initializer as I
class BiLSTM(nn.Layer):
"""LSTM for Punctuation Restoration
"""
def __init__(self,
vocab_size,
embedding_size,
hidden_size,
num_layers,
num_class,
init_scale=0.1):
super(BiLSTM, self).__init__()
# hyper parameters
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.num_class = num_class
# 网络中的层
self.embedding = nn.Embedding(
vocab_size,
embedding_size,
weight_attr=paddle.ParamAttr(initializer=I.Uniform(
low=-init_scale, high=init_scale)))
# print(hidden_size)
# print(embedding_size)
self.lstm = nn.LSTM(
input_size=embedding_size,
hidden_size=hidden_size,
num_layers=num_layers,
direction="bidirect",
weight_ih_attr=paddle.ParamAttr(initializer=I.Uniform(
low=-init_scale, high=init_scale)),
weight_hh_attr=paddle.ParamAttr(initializer=I.Uniform(
low=-init_scale, high=init_scale)))
# Here is a one direction LSTM. If bidirection LSTM, (hidden_size*2(,))
self.fc = nn.Linear(
in_features=hidden_size * 2,
out_features=num_class,
weight_attr=paddle.ParamAttr(initializer=I.Uniform(
low=-init_scale, high=init_scale)),
bias_attr=paddle.ParamAttr(initializer=I.Uniform(
low=-init_scale, high=init_scale)))
# self.fc = nn.Linear(hidden_size, num_class)
self.softmax = nn.Softmax()
def forward(self, input):
"""The forward process of Net
Parameters
----------
inputs : tensor
Training data, batch first
"""
# Inherit the knowledge of context
# hidden = self.init_hidden(inputs.size(0))
# print('input_size',inputs.size())
embedding = self.embedding(input)
# print('embedding_size', embedding.size())
# packed = pack_sequence(embedding, inputs_lengths, batch_first=True)
# embedding本身是同样长度的用这个函数主要是为了用pack
# *****************************************************************************
y, (_, _) = self.lstm(embedding)
# print(y.size())
y = self.fc(y)
y = paddle.reshape(y, shape=[-1, self.num_class])
logit = self.softmax(y)
return y, logit