You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
235 lines
6.5 KiB
235 lines
6.5 KiB
#include "scorer.h"
|
|
|
|
#include <unistd.h>
|
|
#include <iostream>
|
|
|
|
#include "lm/config.hh"
|
|
#include "lm/model.hh"
|
|
#include "lm/state.hh"
|
|
#include "util/string_piece.hh"
|
|
#include "util/tokenize_piece.hh"
|
|
|
|
#include "decoder_utils.h"
|
|
|
|
using namespace lm::ngram;
|
|
|
|
Scorer::Scorer(double alpha,
|
|
double beta,
|
|
const std::string& lm_path,
|
|
const std::vector<std::string>& vocab_list) {
|
|
this->alpha = alpha;
|
|
this->beta = beta;
|
|
|
|
dictionary = nullptr;
|
|
is_character_based_ = true;
|
|
language_model_ = nullptr;
|
|
|
|
max_order_ = 0;
|
|
dict_size_ = 0;
|
|
SPACE_ID_ = -1;
|
|
|
|
setup(lm_path, vocab_list);
|
|
}
|
|
|
|
Scorer::~Scorer() {
|
|
if (language_model_ != nullptr) {
|
|
delete static_cast<lm::base::Model*>(language_model_);
|
|
}
|
|
if (dictionary != nullptr) {
|
|
delete static_cast<fst::StdVectorFst*>(dictionary);
|
|
}
|
|
}
|
|
|
|
void Scorer::setup(const std::string& lm_path,
|
|
const std::vector<std::string>& vocab_list) {
|
|
// load language model
|
|
load_lm(lm_path);
|
|
// set char map for scorer
|
|
set_char_map(vocab_list);
|
|
// fill the dictionary for FST
|
|
if (!is_character_based()) {
|
|
fill_dictionary(true);
|
|
}
|
|
}
|
|
|
|
void Scorer::load_lm(const std::string& lm_path) {
|
|
const char* filename = lm_path.c_str();
|
|
VALID_CHECK_EQ(access(filename, F_OK), 0, "Invalid language model path");
|
|
|
|
RetriveStrEnumerateVocab enumerate;
|
|
lm::ngram::Config config;
|
|
config.enumerate_vocab = &enumerate;
|
|
language_model_ = lm::ngram::LoadVirtual(filename, config);
|
|
max_order_ = static_cast<lm::base::Model*>(language_model_)->Order();
|
|
vocabulary_ = enumerate.vocabulary;
|
|
for (size_t i = 0; i < vocabulary_.size(); ++i) {
|
|
if (is_character_based_ && vocabulary_[i] != UNK_TOKEN &&
|
|
vocabulary_[i] != START_TOKEN && vocabulary_[i] != END_TOKEN &&
|
|
get_utf8_str_len(enumerate.vocabulary[i]) > 1) {
|
|
is_character_based_ = false;
|
|
}
|
|
}
|
|
}
|
|
|
|
double Scorer::get_log_cond_prob(const std::vector<std::string>& words) {
|
|
lm::base::Model* model = static_cast<lm::base::Model*>(language_model_);
|
|
double cond_prob;
|
|
lm::ngram::State state, tmp_state, out_state;
|
|
// avoid to inserting <s> in begin
|
|
model->NullContextWrite(&state);
|
|
for (size_t i = 0; i < words.size(); ++i) {
|
|
lm::WordIndex word_index = model->BaseVocabulary().Index(words[i]);
|
|
// encounter OOV
|
|
if (word_index == 0) {
|
|
return OOV_SCORE;
|
|
}
|
|
cond_prob = model->BaseScore(&state, word_index, &out_state);
|
|
tmp_state = state;
|
|
state = out_state;
|
|
out_state = tmp_state;
|
|
}
|
|
// return log10 prob
|
|
return cond_prob;
|
|
}
|
|
|
|
double Scorer::get_sent_log_prob(const std::vector<std::string>& words) {
|
|
std::vector<std::string> sentence;
|
|
if (words.size() == 0) {
|
|
for (size_t i = 0; i < max_order_; ++i) {
|
|
sentence.push_back(START_TOKEN);
|
|
}
|
|
} else {
|
|
for (size_t i = 0; i < max_order_ - 1; ++i) {
|
|
sentence.push_back(START_TOKEN);
|
|
}
|
|
sentence.insert(sentence.end(), words.begin(), words.end());
|
|
}
|
|
sentence.push_back(END_TOKEN);
|
|
return get_log_prob(sentence);
|
|
}
|
|
|
|
double Scorer::get_log_prob(const std::vector<std::string>& words) {
|
|
assert(words.size() > max_order_);
|
|
double score = 0.0;
|
|
for (size_t i = 0; i < words.size() - max_order_ + 1; ++i) {
|
|
std::vector<std::string> ngram(words.begin() + i,
|
|
words.begin() + i + max_order_);
|
|
score += get_log_cond_prob(ngram);
|
|
}
|
|
return score;
|
|
}
|
|
|
|
void Scorer::reset_params(float alpha, float beta) {
|
|
this->alpha = alpha;
|
|
this->beta = beta;
|
|
}
|
|
|
|
std::string Scorer::vec2str(const std::vector<int>& input) {
|
|
std::string word;
|
|
for (auto ind : input) {
|
|
word += char_list_[ind];
|
|
}
|
|
return word;
|
|
}
|
|
|
|
std::vector<std::string> Scorer::split_labels(const std::vector<int>& labels) {
|
|
if (labels.empty()) return {};
|
|
|
|
std::string s = vec2str(labels);
|
|
std::vector<std::string> words;
|
|
if (is_character_based_) {
|
|
words = split_utf8_str(s);
|
|
} else {
|
|
words = split_str(s, " ");
|
|
}
|
|
return words;
|
|
}
|
|
|
|
void Scorer::set_char_map(const std::vector<std::string>& char_list) {
|
|
char_list_ = char_list;
|
|
char_map_.clear();
|
|
|
|
for (size_t i = 0; i < char_list_.size(); i++) {
|
|
if (char_list_[i] == " ") {
|
|
SPACE_ID_ = i;
|
|
char_map_[' '] = i;
|
|
} else if (char_list_[i].size() == 1) {
|
|
char_map_[char_list_[i][0]] = i;
|
|
}
|
|
}
|
|
}
|
|
|
|
std::vector<std::string> Scorer::make_ngram(PathTrie* prefix) {
|
|
std::vector<std::string> ngram;
|
|
PathTrie* current_node = prefix;
|
|
PathTrie* new_node = nullptr;
|
|
|
|
for (int order = 0; order < max_order_; order++) {
|
|
std::vector<int> prefix_vec;
|
|
|
|
if (is_character_based_) {
|
|
new_node = current_node->get_path_vec(prefix_vec, SPACE_ID_, 1);
|
|
current_node = new_node;
|
|
} else {
|
|
new_node = current_node->get_path_vec(prefix_vec, SPACE_ID_);
|
|
current_node = new_node->parent; // Skipping spaces
|
|
}
|
|
|
|
// reconstruct word
|
|
std::string word = vec2str(prefix_vec);
|
|
ngram.push_back(word);
|
|
|
|
if (new_node->character == -1) {
|
|
// No more spaces, but still need order
|
|
for (int i = 0; i < max_order_ - order - 1; i++) {
|
|
ngram.push_back(START_TOKEN);
|
|
}
|
|
break;
|
|
}
|
|
}
|
|
std::reverse(ngram.begin(), ngram.end());
|
|
return ngram;
|
|
}
|
|
|
|
void Scorer::fill_dictionary(bool add_space) {
|
|
fst::StdVectorFst dictionary;
|
|
// First reverse char_list so ints can be accessed by chars
|
|
std::unordered_map<std::string, int> char_map;
|
|
for (size_t i = 0; i < char_list_.size(); i++) {
|
|
char_map[char_list_[i]] = i;
|
|
}
|
|
|
|
// For each unigram convert to ints and put in trie
|
|
int dict_size = 0;
|
|
for (const auto& word : vocabulary_) {
|
|
bool added = add_word_to_dictionary(
|
|
word, char_map, add_space, SPACE_ID_, &dictionary);
|
|
dict_size += added ? 1 : 0;
|
|
}
|
|
|
|
dict_size_ = dict_size;
|
|
|
|
/* Simplify FST
|
|
|
|
* This gets rid of "epsilon" transitions in the FST.
|
|
* These are transitions that don't require a string input to be taken.
|
|
* Getting rid of them is necessary to make the FST determinisitc, but
|
|
* can greatly increase the size of the FST
|
|
*/
|
|
fst::RmEpsilon(&dictionary);
|
|
fst::StdVectorFst* new_dict = new fst::StdVectorFst;
|
|
|
|
/* This makes the FST deterministic, meaning for any string input there's
|
|
* only one possible state the FST could be in. It is assumed our
|
|
* dictionary is deterministic when using it.
|
|
* (lest we'd have to check for multiple transitions at each state)
|
|
*/
|
|
fst::Determinize(dictionary, new_dict);
|
|
|
|
/* Finds the simplest equivalent fst. This is unnecessary but decreases
|
|
* memory usage of the dictionary
|
|
*/
|
|
fst::Minimize(new_dict);
|
|
this->dictionary = new_dict;
|
|
}
|