File size: 8,767 Bytes
ad16788 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 |
#!/usr/bin/env python3
# Copyright 2018 Mitsubishi Electric Research Laboratories (Takaaki Hori)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import math
import chainer
import chainer.functions as F
from espnet.lm.lm_utils import make_lexical_tree
# Definition of a multi-level (subword/word) language model
class MultiLevelLM(chainer.Chain):
logzero = -10000000000.0
zero = 1.0e-10
def __init__(
self,
wordlm,
subwordlm,
word_dict,
subword_dict,
subwordlm_weight=0.8,
oov_penalty=1.0,
open_vocab=True,
):
super(MultiLevelLM, self).__init__()
self.wordlm = wordlm
self.subwordlm = subwordlm
self.word_eos = word_dict["<eos>"]
self.word_unk = word_dict["<unk>"]
self.xp_word_eos = self.xp.full(1, self.word_eos, "i")
self.xp_word_unk = self.xp.full(1, self.word_unk, "i")
self.space = subword_dict["<space>"]
self.eos = subword_dict["<eos>"]
self.lexroot = make_lexical_tree(word_dict, subword_dict, self.word_unk)
self.log_oov_penalty = math.log(oov_penalty)
self.open_vocab = open_vocab
self.subword_dict_size = len(subword_dict)
self.subwordlm_weight = subwordlm_weight
self.normalized = True
def __call__(self, state, x):
# update state with input label x
if state is None: # make initial states and log-prob vectors
wlm_state, z_wlm = self.wordlm(None, self.xp_word_eos)
wlm_logprobs = F.log_softmax(z_wlm).data
clm_state, z_clm = self.subwordlm(None, x)
log_y = F.log_softmax(z_clm).data * self.subwordlm_weight
new_node = self.lexroot
clm_logprob = 0.0
xi = self.space
else:
clm_state, wlm_state, wlm_logprobs, node, log_y, clm_logprob = state
xi = int(x)
if xi == self.space: # inter-word transition
if node is not None and node[1] >= 0: # check if the node is word end
w = self.xp.full(1, node[1], "i")
else: # this node is not a word end, which means <unk>
w = self.xp_word_unk
# update wordlm state and log-prob vector
wlm_state, z_wlm = self.wordlm(wlm_state, w)
wlm_logprobs = F.log_softmax(z_wlm).data
new_node = self.lexroot # move to the tree root
clm_logprob = 0.0
elif node is not None and xi in node[0]: # intra-word transition
new_node = node[0][xi]
clm_logprob += log_y[0, xi]
elif self.open_vocab: # if no path in the tree, enter open-vocabulary mode
new_node = None
clm_logprob += log_y[0, xi]
else: # if open_vocab flag is disabled, return 0 probabilities
log_y = self.xp.full((1, self.subword_dict_size), self.logzero, "f")
return (clm_state, wlm_state, None, log_y, 0.0), log_y
clm_state, z_clm = self.subwordlm(clm_state, x)
log_y = F.log_softmax(z_clm).data * self.subwordlm_weight
# apply word-level probabilies for <space> and <eos> labels
if xi != self.space:
if new_node is not None and new_node[1] >= 0: # if new node is word end
wlm_logprob = wlm_logprobs[:, new_node[1]] - clm_logprob
else:
wlm_logprob = wlm_logprobs[:, self.word_unk] + self.log_oov_penalty
log_y[:, self.space] = wlm_logprob
log_y[:, self.eos] = wlm_logprob
else:
log_y[:, self.space] = self.logzero
log_y[:, self.eos] = self.logzero
return (clm_state, wlm_state, wlm_logprobs, new_node, log_y, clm_logprob), log_y
def final(self, state):
clm_state, wlm_state, wlm_logprobs, node, log_y, clm_logprob = state
if node is not None and node[1] >= 0: # check if the node is word end
w = self.xp.full(1, node[1], "i")
else: # this node is not a word end, which means <unk>
w = self.xp_word_unk
wlm_state, z_wlm = self.wordlm(wlm_state, w)
return F.log_softmax(z_wlm).data[:, self.word_eos]
# Definition of a look-ahead word language model
class LookAheadWordLM(chainer.Chain):
logzero = -10000000000.0
zero = 1.0e-10
def __init__(
self, wordlm, word_dict, subword_dict, oov_penalty=0.0001, open_vocab=True
):
super(LookAheadWordLM, self).__init__()
self.wordlm = wordlm
self.word_eos = word_dict["<eos>"]
self.word_unk = word_dict["<unk>"]
self.xp_word_eos = self.xp.full(1, self.word_eos, "i")
self.xp_word_unk = self.xp.full(1, self.word_unk, "i")
self.space = subword_dict["<space>"]
self.eos = subword_dict["<eos>"]
self.lexroot = make_lexical_tree(word_dict, subword_dict, self.word_unk)
self.oov_penalty = oov_penalty
self.open_vocab = open_vocab
self.subword_dict_size = len(subword_dict)
self.normalized = True
def __call__(self, state, x):
# update state with input label x
if state is None: # make initial states and cumlative probability vector
wlm_state, z_wlm = self.wordlm(None, self.xp_word_eos)
cumsum_probs = self.xp.cumsum(F.softmax(z_wlm).data, axis=1)
new_node = self.lexroot
xi = self.space
else:
wlm_state, cumsum_probs, node = state
xi = int(x)
if xi == self.space: # inter-word transition
if node is not None and node[1] >= 0: # check if the node is word end
w = self.xp.full(1, node[1], "i")
else: # this node is not a word end, which means <unk>
w = self.xp_word_unk
# update wordlm state and cumlative probability vector
wlm_state, z_wlm = self.wordlm(wlm_state, w)
cumsum_probs = self.xp.cumsum(F.softmax(z_wlm).data, axis=1)
new_node = self.lexroot # move to the tree root
elif node is not None and xi in node[0]: # intra-word transition
new_node = node[0][xi]
elif self.open_vocab: # if no path in the tree, enter open-vocabulary mode
new_node = None
else: # if open_vocab flag is disabled, return 0 probabilities
log_y = self.xp.full((1, self.subword_dict_size), self.logzero, "f")
return (wlm_state, None, None), log_y
if new_node is not None:
succ, wid, wids = new_node
# compute parent node probability
sum_prob = (
(cumsum_probs[:, wids[1]] - cumsum_probs[:, wids[0]])
if wids is not None
else 1.0
)
if sum_prob < self.zero:
log_y = self.xp.full((1, self.subword_dict_size), self.logzero, "f")
return (wlm_state, cumsum_probs, new_node), log_y
# set <unk> probability as a default value
unk_prob = (
cumsum_probs[:, self.word_unk] - cumsum_probs[:, self.word_unk - 1]
)
y = self.xp.full(
(1, self.subword_dict_size), unk_prob * self.oov_penalty, "f"
)
# compute transition probabilities to child nodes
for cid, nd in succ.items():
y[:, cid] = (
cumsum_probs[:, nd[2][1]] - cumsum_probs[:, nd[2][0]]
) / sum_prob
# apply word-level probabilies for <space> and <eos> labels
if wid >= 0:
wlm_prob = (cumsum_probs[:, wid] - cumsum_probs[:, wid - 1]) / sum_prob
y[:, self.space] = wlm_prob
y[:, self.eos] = wlm_prob
elif xi == self.space:
y[:, self.space] = self.zero
y[:, self.eos] = self.zero
log_y = self.xp.log(
self.xp.clip(y, self.zero, None)
) # clip to avoid log(0)
else: # if no path in the tree, transition probability is one
log_y = self.xp.zeros((1, self.subword_dict_size), "f")
return (wlm_state, cumsum_probs, new_node), log_y
def final(self, state):
wlm_state, cumsum_probs, node = state
if node is not None and node[1] >= 0: # check if the node is word end
w = self.xp.full(1, node[1], "i")
else: # this node is not a word end, which means <unk>
w = self.xp_word_unk
wlm_state, z_wlm = self.wordlm(wlm_state, w)
return F.log_softmax(z_wlm).data[:, self.word_eos]
|