Yibing Liu
7 years ago
parent
a2ddfe8d9e
commit
5208b8e40f
@ -1,337 +1,329 @@
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#include <iostream>
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#include <map>
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#include "ctc_decoders.h"
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#include <algorithm>
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#include <utility>
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#include <cmath>
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#include <iostream>
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#include <limits>
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#include "fst/fstlib.h"
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#include "ctc_decoders.h"
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#include <map>
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#include <utility>
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#include "ThreadPool.h"
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#include "decoder_utils.h"
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#include "fst/fstlib.h"
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#include "path_trie.h"
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#include "ThreadPool.h"
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std::string ctc_best_path_decoder(std::vector<std::vector<double> > probs_seq,
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std::vector<std::string> vocabulary)
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{
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// dimension check
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int num_time_steps = probs_seq.size();
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for (int i=0; i<num_time_steps; i++) {
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if (probs_seq[i].size() != vocabulary.size()+1) {
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std::cout << "The shape of probs_seq does not match"
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<< " with the shape of the vocabulary!" << std::endl;
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exit(1);
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}
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std::string ctc_best_path_decoder(std::vector<std::vector<double>> probs_seq,
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std::vector<std::string> vocabulary) {
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// dimension check
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int num_time_steps = probs_seq.size();
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for (int i = 0; i < num_time_steps; i++) {
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if (probs_seq[i].size() != vocabulary.size() + 1) {
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std::cout << "The shape of probs_seq does not match"
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<< " with the shape of the vocabulary!" << std::endl;
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exit(1);
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}
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int blank_id = vocabulary.size();
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std::vector<int> max_idx_vec;
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double max_prob = 0.0;
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int max_idx = 0;
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for (int i = 0; i < num_time_steps; i++) {
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for (int j = 0; j < probs_seq[i].size(); j++) {
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if (max_prob < probs_seq[i][j]) {
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max_idx = j;
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max_prob = probs_seq[i][j];
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}
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}
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max_idx_vec.push_back(max_idx);
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max_prob = 0.0;
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max_idx = 0;
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}
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int blank_id = vocabulary.size();
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std::vector<int> max_idx_vec;
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double max_prob = 0.0;
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int max_idx = 0;
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for (int i = 0; i < num_time_steps; i++) {
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for (int j = 0; j < probs_seq[i].size(); j++) {
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if (max_prob < probs_seq[i][j]) {
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max_idx = j;
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max_prob = probs_seq[i][j];
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}
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}
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std::vector<int> idx_vec;
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for (int i = 0; i < max_idx_vec.size(); i++) {
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if ((i == 0) || ((i > 0) && max_idx_vec[i] != max_idx_vec[i-1])) {
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idx_vec.push_back(max_idx_vec[i]);
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}
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max_idx_vec.push_back(max_idx);
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max_prob = 0.0;
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max_idx = 0;
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}
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std::vector<int> idx_vec;
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for (int i = 0; i < max_idx_vec.size(); i++) {
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if ((i == 0) || ((i > 0) && max_idx_vec[i] != max_idx_vec[i - 1])) {
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idx_vec.push_back(max_idx_vec[i]);
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}
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}
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std::string best_path_result;
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for (int i = 0; i < idx_vec.size(); i++) {
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if (idx_vec[i] != blank_id) {
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best_path_result += vocabulary[idx_vec[i]];
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}
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std::string best_path_result;
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for (int i = 0; i < idx_vec.size(); i++) {
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if (idx_vec[i] != blank_id) {
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best_path_result += vocabulary[idx_vec[i]];
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}
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return best_path_result;
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}
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return best_path_result;
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}
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std::vector<std::pair<double, std::string> >
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ctc_beam_search_decoder(std::vector<std::vector<double> > probs_seq,
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int beam_size,
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std::vector<std::string> vocabulary,
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int blank_id,
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double cutoff_prob,
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int cutoff_top_n,
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Scorer *ext_scorer)
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{
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// dimension check
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int num_time_steps = probs_seq.size();
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for (int i = 0; i < num_time_steps; i++) {
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if (probs_seq[i].size() != vocabulary.size() + 1) {
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std::cout << " The shape of probs_seq does not match"
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<< " with the shape of the vocabulary!" << std::endl;
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exit(1);
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}
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std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
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std::vector<std::vector<double>> probs_seq,
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int beam_size,
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std::vector<std::string> vocabulary,
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int blank_id,
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double cutoff_prob,
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int cutoff_top_n,
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Scorer *extscorer) {
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// dimension check
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int num_time_steps = probs_seq.size();
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for (int i = 0; i < num_time_steps; i++) {
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if (probs_seq[i].size() != vocabulary.size() + 1) {
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std::cout << " The shape of probs_seq does not match"
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<< " with the shape of the vocabulary!" << std::endl;
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exit(1);
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}
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// blank_id check
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if (blank_id > vocabulary.size()) {
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std::cout << " Invalid blank_id! " << std::endl;
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exit(1);
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}
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// blank_id check
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if (blank_id > vocabulary.size()) {
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std::cout << " Invalid blank_id! " << std::endl;
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exit(1);
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}
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// assign space ID
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std::vector<std::string>::iterator it =
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std::find(vocabulary.begin(), vocabulary.end(), " ");
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int space_id = it - vocabulary.begin();
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// if no space in vocabulary
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if (space_id >= vocabulary.size()) {
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space_id = -2;
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}
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// init prefixes' root
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PathTrie root;
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root.score = root.log_prob_b_prev = 0.0;
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std::vector<PathTrie *> prefixes;
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prefixes.push_back(&root);
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if (extscorer != nullptr) {
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if (extscorer->is_char_map_empty()) {
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extscorer->set_char_map(vocabulary);
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}
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// assign space ID
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std::vector<std::string>::iterator it = std::find(vocabulary.begin(),
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vocabulary.end(), " ");
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int space_id = it - vocabulary.begin();
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// if no space in vocabulary
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if(space_id >= vocabulary.size()) {
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space_id = -2;
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if (!extscorer->is_character_based()) {
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if (extscorer->dictionary == nullptr) {
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// fill dictionary for fst
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extscorer->fill_dictionary(true);
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}
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auto fst_dict = static_cast<fst::StdVectorFst *>(extscorer->dictionary);
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fst::StdVectorFst *dict_ptr = fst_dict->Copy(true);
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root.set_dictionary(dict_ptr);
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auto matcher = std::make_shared<FSTMATCH>(*dict_ptr, fst::MATCH_INPUT);
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root.set_matcher(matcher);
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}
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}
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// prefix search over time
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for (int time_step = 0; time_step < num_time_steps; time_step++) {
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std::vector<double> prob = probs_seq[time_step];
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std::vector<std::pair<int, double>> prob_idx;
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for (int i = 0; i < prob.size(); i++) {
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prob_idx.push_back(std::pair<int, double>(i, prob[i]));
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}
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// init prefixes' root
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PathTrie root;
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root._score = root._log_prob_b_prev = 0.0;
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std::vector<PathTrie*> prefixes;
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prefixes.push_back(&root);
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float min_cutoff = -NUM_FLT_INF;
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bool full_beam = false;
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if (extscorer != nullptr) {
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int num_prefixes = std::min((int)prefixes.size(), beam_size);
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std::sort(
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prefixes.begin(), prefixes.begin() + num_prefixes, prefix_compare);
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min_cutoff = prefixes[num_prefixes - 1]->score + log(prob[blank_id]) -
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std::max(0.0, extscorer->beta);
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full_beam = (num_prefixes == beam_size);
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}
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if ( ext_scorer != nullptr) {
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if (ext_scorer->is_char_map_empty()) {
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ext_scorer->set_char_map(vocabulary);
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}
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if (!ext_scorer->is_character_based()) {
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if (ext_scorer->dictionary == nullptr) {
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// fill dictionary for fst
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ext_scorer->fill_dictionary(true);
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}
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auto fst_dict = static_cast<fst::StdVectorFst*>
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(ext_scorer->dictionary);
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fst::StdVectorFst* dict_ptr = fst_dict->Copy(true);
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root.set_dictionary(dict_ptr);
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auto matcher = std::make_shared<FSTMATCH>
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(*dict_ptr, fst::MATCH_INPUT);
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root.set_matcher(matcher);
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// pruning of vacobulary
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int cutoff_len = prob.size();
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if (cutoff_prob < 1.0 || cutoff_top_n < prob.size()) {
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std::sort(
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prob_idx.begin(), prob_idx.end(), pair_comp_second_rev<int, double>);
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if (cutoff_prob < 1.0) {
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double cum_prob = 0.0;
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cutoff_len = 0;
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for (int i = 0; i < prob_idx.size(); i++) {
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cum_prob += prob_idx[i].second;
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cutoff_len += 1;
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if (cum_prob >= cutoff_prob) break;
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}
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}
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cutoff_len = std::min(cutoff_len, cutoff_top_n);
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prob_idx = std::vector<std::pair<int, double>>(
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prob_idx.begin(), prob_idx.begin() + cutoff_len);
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}
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std::vector<std::pair<int, float>> log_prob_idx;
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for (int i = 0; i < cutoff_len; i++) {
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log_prob_idx.push_back(std::pair<int, float>(
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prob_idx[i].first, log(prob_idx[i].second + NUM_FLT_MIN)));
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}
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// prefix search over time
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for (int time_step = 0; time_step < num_time_steps; time_step++) {
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std::vector<double> prob = probs_seq[time_step];
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std::vector<std::pair<int, double> > prob_idx;
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for (int i=0; i<prob.size(); i++) {
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prob_idx.push_back(std::pair<int, double>(i, prob[i]));
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}
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// loop over chars
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for (int index = 0; index < log_prob_idx.size(); index++) {
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auto c = log_prob_idx[index].first;
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float log_prob_c = log_prob_idx[index].second;
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float min_cutoff = -NUM_FLT_INF;
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bool full_beam = false;
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if (ext_scorer != nullptr) {
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int num_prefixes = std::min((int)prefixes.size(), beam_size);
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std::sort(prefixes.begin(), prefixes.begin() + num_prefixes,
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prefix_compare);
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min_cutoff = prefixes[num_prefixes-1]->_score + log(prob[blank_id])
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- std::max(0.0, ext_scorer->beta);
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full_beam = (num_prefixes == beam_size);
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}
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// pruning of vacobulary
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int cutoff_len = prob.size();
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if (cutoff_prob < 1.0 || cutoff_top_n < prob.size()) {
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std::sort(prob_idx.begin(),
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prob_idx.end(),
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pair_comp_second_rev<int, double>);
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if (cutoff_prob < 1.0) {
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double cum_prob = 0.0;
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cutoff_len = 0;
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for (int i=0; i<prob_idx.size(); i++) {
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cum_prob += prob_idx[i].second;
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cutoff_len += 1;
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if (cum_prob >= cutoff_prob) break;
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}
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}
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cutoff_len = std::min(cutoff_len, cutoff_top_n);
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prob_idx = std::vector<std::pair<int, double> >( prob_idx.begin(),
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prob_idx.begin() + cutoff_len);
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}
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std::vector<std::pair<int, float> > log_prob_idx;
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for (int i = 0; i < cutoff_len; i++) {
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log_prob_idx.push_back(std::pair<int, float>
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(prob_idx[i].first, log(prob_idx[i].second + NUM_FLT_MIN)));
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}
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for (int i = 0; i < prefixes.size() && i < beam_size; i++) {
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auto prefix = prefixes[i];
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// loop over chars
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for (int index = 0; index < log_prob_idx.size(); index++) {
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auto c = log_prob_idx[index].first;
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float log_prob_c = log_prob_idx[index].second;
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for (int i = 0; i < prefixes.size() && i<beam_size; i++) {
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auto prefix = prefixes[i];
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if (full_beam && log_prob_c + prefix->_score < min_cutoff) {
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break;
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}
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// blank
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if (c == blank_id) {
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prefix->_log_prob_b_cur = log_sum_exp(
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prefix->_log_prob_b_cur,
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log_prob_c + prefix->_score);
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continue;
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}
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// repeated character
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if (c == prefix->_character) {
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prefix->_log_prob_nb_cur = log_sum_exp(
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prefix->_log_prob_nb_cur,
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log_prob_c + prefix->_log_prob_nb_prev);
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}
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// get new prefix
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auto prefix_new = prefix->get_path_trie(c);
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if (prefix_new != nullptr) {
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float log_p = -NUM_FLT_INF;
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if (c == prefix->_character
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&& prefix->_log_prob_b_prev > -NUM_FLT_INF) {
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log_p = log_prob_c + prefix->_log_prob_b_prev;
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} else if (c != prefix->_character) {
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log_p = log_prob_c + prefix->_score;
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}
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// language model scoring
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if (ext_scorer != nullptr &&
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(c == space_id || ext_scorer->is_character_based()) ) {
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PathTrie *prefix_to_score = nullptr;
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// skip scoring the space
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if (ext_scorer->is_character_based()) {
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prefix_to_score = prefix_new;
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} else {
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prefix_to_score = prefix;
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}
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double score = 0.0;
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std::vector<std::string> ngram;
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ngram = ext_scorer->make_ngram(prefix_to_score);
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score = ext_scorer->get_log_cond_prob(ngram) *
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ext_scorer->alpha;
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log_p += score;
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log_p += ext_scorer->beta;
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}
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prefix_new->_log_prob_nb_cur = log_sum_exp(
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prefix_new->_log_prob_nb_cur, log_p);
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}
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} // end of loop over prefix
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} // end of loop over chars
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prefixes.clear();
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// update log probs
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root.iterate_to_vec(prefixes);
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// only preserve top beam_size prefixes
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if (prefixes.size() >= beam_size) {
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std::nth_element(prefixes.begin(),
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prefixes.begin() + beam_size,
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prefixes.end(),
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prefix_compare);
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for (size_t i = beam_size; i < prefixes.size(); i++) {
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prefixes[i]->remove();
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}
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if (full_beam && log_prob_c + prefix->score < min_cutoff) {
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break;
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}
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} // end of loop over time
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// compute aproximate ctc score as the return score
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for (size_t i = 0; i < beam_size && i < prefixes.size(); i++) {
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double approx_ctc = prefixes[i]->_score;
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if (ext_scorer != nullptr) {
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std::vector<int> output;
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prefixes[i]->get_path_vec(output);
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size_t prefix_length = output.size();
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auto words = ext_scorer->split_labels(output);
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// remove word insert
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approx_ctc = approx_ctc - prefix_length * ext_scorer->beta;
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// remove language model weight:
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approx_ctc -= (ext_scorer->get_sent_log_prob(words))
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* ext_scorer->alpha;
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// blank
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if (c == blank_id) {
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prefix->log_prob_b_cur =
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log_sum_exp(prefix->log_prob_b_cur, log_prob_c + prefix->score);
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continue;
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}
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// repeated character
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if (c == prefix->character) {
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prefix->log_prob_nb_cur = log_sum_exp(
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prefix->log_prob_nb_cur, log_prob_c + prefix->log_prob_nb_prev);
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}
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// get new prefix
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auto prefix_new = prefix->get_path_trie(c);
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if (prefix_new != nullptr) {
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float log_p = -NUM_FLT_INF;
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if (c == prefix->character &&
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prefix->log_prob_b_prev > -NUM_FLT_INF) {
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log_p = log_prob_c + prefix->log_prob_b_prev;
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} else if (c != prefix->character) {
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log_p = log_prob_c + prefix->score;
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}
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// language model scoring
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if (extscorer != nullptr &&
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(c == space_id || extscorer->is_character_based())) {
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PathTrie *prefix_toscore = nullptr;
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// skip scoring the space
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if (extscorer->is_character_based()) {
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prefix_toscore = prefix_new;
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} else {
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prefix_toscore = prefix;
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}
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prefixes[i]->_approx_ctc = approx_ctc;
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}
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double score = 0.0;
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std::vector<std::string> ngram;
|
||||
ngram = extscorer->make_ngram(prefix_toscore);
|
||||
score = extscorer->get_log_cond_prob(ngram) * extscorer->alpha;
|
||||
|
||||
// allow for the post processing
|
||||
std::vector<PathTrie*> space_prefixes;
|
||||
if (space_prefixes.empty()) {
|
||||
for (size_t i = 0; i < beam_size && i< prefixes.size(); i++) {
|
||||
space_prefixes.push_back(prefixes[i]);
|
||||
log_p += score;
|
||||
log_p += extscorer->beta;
|
||||
}
|
||||
prefix_new->log_prob_nb_cur =
|
||||
log_sum_exp(prefix_new->log_prob_nb_cur, log_p);
|
||||
}
|
||||
} // end of loop over prefix
|
||||
} // end of loop over chars
|
||||
|
||||
prefixes.clear();
|
||||
// update log probs
|
||||
root.iterate_to_vec(prefixes);
|
||||
|
||||
// only preserve top beam_size prefixes
|
||||
if (prefixes.size() >= beam_size) {
|
||||
std::nth_element(prefixes.begin(),
|
||||
prefixes.begin() + beam_size,
|
||||
prefixes.end(),
|
||||
prefix_compare);
|
||||
|
||||
for (size_t i = beam_size; i < prefixes.size(); i++) {
|
||||
prefixes[i]->remove();
|
||||
}
|
||||
}
|
||||
|
||||
std::sort(space_prefixes.begin(), space_prefixes.end(), prefix_compare);
|
||||
std::vector<std::pair<double, std::string> > output_vecs;
|
||||
for (size_t i = 0; i < beam_size && i < space_prefixes.size(); i++) {
|
||||
std::vector<int> output;
|
||||
space_prefixes[i]->get_path_vec(output);
|
||||
// convert index to string
|
||||
std::string output_str;
|
||||
for (int j = 0; j < output.size(); j++) {
|
||||
output_str += vocabulary[output[j]];
|
||||
}
|
||||
std::pair<double, std::string>
|
||||
output_pair(-space_prefixes[i]->_approx_ctc, output_str);
|
||||
output_vecs.emplace_back(output_pair);
|
||||
} // end of loop over time
|
||||
|
||||
// compute aproximate ctc score as the return score
|
||||
for (size_t i = 0; i < beam_size && i < prefixes.size(); i++) {
|
||||
double approx_ctc = prefixes[i]->score;
|
||||
|
||||
if (extscorer != nullptr) {
|
||||
std::vector<int> output;
|
||||
prefixes[i]->get_path_vec(output);
|
||||
size_t prefix_length = output.size();
|
||||
auto words = extscorer->split_labels(output);
|
||||
// remove word insert
|
||||
approx_ctc = approx_ctc - prefix_length * extscorer->beta;
|
||||
// remove language model weight:
|
||||
approx_ctc -= (extscorer->get_sent_log_prob(words)) * extscorer->alpha;
|
||||
}
|
||||
|
||||
return output_vecs;
|
||||
}
|
||||
|
||||
|
||||
std::vector<std::vector<std::pair<double, std::string> > >
|
||||
ctc_beam_search_decoder_batch(
|
||||
std::vector<std::vector<std::vector<double>>> probs_split,
|
||||
int beam_size,
|
||||
std::vector<std::string> vocabulary,
|
||||
int blank_id,
|
||||
int num_processes,
|
||||
double cutoff_prob,
|
||||
int cutoff_top_n,
|
||||
Scorer *ext_scorer
|
||||
) {
|
||||
if (num_processes <= 0) {
|
||||
std::cout << "num_processes must be nonnegative!" << std::endl;
|
||||
exit(1);
|
||||
prefixes[i]->approx_ctc = approx_ctc;
|
||||
}
|
||||
|
||||
// allow for the post processing
|
||||
std::vector<PathTrie *> space_prefixes;
|
||||
if (space_prefixes.empty()) {
|
||||
for (size_t i = 0; i < beam_size && i < prefixes.size(); i++) {
|
||||
space_prefixes.push_back(prefixes[i]);
|
||||
}
|
||||
// thread pool
|
||||
ThreadPool pool(num_processes);
|
||||
// number of samples
|
||||
int batch_size = probs_split.size();
|
||||
|
||||
// scorer filling up
|
||||
if ( ext_scorer != nullptr) {
|
||||
if (ext_scorer->is_char_map_empty()) {
|
||||
ext_scorer->set_char_map(vocabulary);
|
||||
}
|
||||
if(!ext_scorer->is_character_based()
|
||||
&& ext_scorer->dictionary == nullptr) {
|
||||
// init dictionary
|
||||
ext_scorer->fill_dictionary(true);
|
||||
}
|
||||
}
|
||||
|
||||
std::sort(space_prefixes.begin(), space_prefixes.end(), prefix_compare);
|
||||
std::vector<std::pair<double, std::string>> output_vecs;
|
||||
for (size_t i = 0; i < beam_size && i < space_prefixes.size(); i++) {
|
||||
std::vector<int> output;
|
||||
space_prefixes[i]->get_path_vec(output);
|
||||
// convert index to string
|
||||
std::string output_str;
|
||||
for (int j = 0; j < output.size(); j++) {
|
||||
output_str += vocabulary[output[j]];
|
||||
}
|
||||
std::pair<double, std::string> output_pair(-space_prefixes[i]->approx_ctc,
|
||||
output_str);
|
||||
output_vecs.emplace_back(output_pair);
|
||||
}
|
||||
|
||||
// enqueue the tasks of decoding
|
||||
std::vector<std::future<std::vector<std::pair<double, std::string>>>> res;
|
||||
for (int i = 0; i < batch_size; i++) {
|
||||
res.emplace_back(
|
||||
pool.enqueue(ctc_beam_search_decoder, probs_split[i],
|
||||
beam_size, vocabulary, blank_id, cutoff_prob,
|
||||
cutoff_top_n, ext_scorer)
|
||||
);
|
||||
}
|
||||
return output_vecs;
|
||||
}
|
||||
|
||||
// get decoding results
|
||||
std::vector<std::vector<std::pair<double, std::string> > > batch_results;
|
||||
for (int i = 0; i < batch_size; i++) {
|
||||
batch_results.emplace_back(res[i].get());
|
||||
std::vector<std::vector<std::pair<double, std::string>>>
|
||||
ctc_beam_search_decoder_batch(
|
||||
std::vector<std::vector<std::vector<double>>> probs_split,
|
||||
int beam_size,
|
||||
std::vector<std::string> vocabulary,
|
||||
int blank_id,
|
||||
int num_processes,
|
||||
double cutoff_prob,
|
||||
int cutoff_top_n,
|
||||
Scorer *extscorer) {
|
||||
if (num_processes <= 0) {
|
||||
std::cout << "num_processes must be nonnegative!" << std::endl;
|
||||
exit(1);
|
||||
}
|
||||
// thread pool
|
||||
ThreadPool pool(num_processes);
|
||||
// number of samples
|
||||
int batch_size = probs_split.size();
|
||||
|
||||
// scorer filling up
|
||||
if (extscorer != nullptr) {
|
||||
if (extscorer->is_char_map_empty()) {
|
||||
extscorer->set_char_map(vocabulary);
|
||||
}
|
||||
if (!extscorer->is_character_based() &&
|
||||
extscorer->dictionary == nullptr) {
|
||||
// init dictionary
|
||||
extscorer->fill_dictionary(true);
|
||||
}
|
||||
return batch_results;
|
||||
}
|
||||
|
||||
// enqueue the tasks of decoding
|
||||
std::vector<std::future<std::vector<std::pair<double, std::string>>>> res;
|
||||
for (int i = 0; i < batch_size; i++) {
|
||||
res.emplace_back(pool.enqueue(ctc_beam_search_decoder,
|
||||
probs_split[i],
|
||||
beam_size,
|
||||
vocabulary,
|
||||
blank_id,
|
||||
cutoff_prob,
|
||||
cutoff_top_n,
|
||||
extscorer));
|
||||
}
|
||||
|
||||
// get decoding results
|
||||
std::vector<std::vector<std::pair<double, std::string>>> batch_results;
|
||||
for (int i = 0; i < batch_size; i++) {
|
||||
batch_results.emplace_back(res[i].get());
|
||||
}
|
||||
return batch_results;
|
||||
}
|
||||
|
||||
@ -1,113 +1,111 @@
|
||||
#include <limits>
|
||||
#include "decoder_utils.h"
|
||||
#include <algorithm>
|
||||
#include <cmath>
|
||||
#include "decoder_utils.h"
|
||||
#include <limits>
|
||||
|
||||
size_t get_utf8_str_len(const std::string& str) {
|
||||
size_t str_len = 0;
|
||||
for (char c : str) {
|
||||
str_len += ((c & 0xc0) != 0x80);
|
||||
}
|
||||
return str_len;
|
||||
size_t str_len = 0;
|
||||
for (char c : str) {
|
||||
str_len += ((c & 0xc0) != 0x80);
|
||||
}
|
||||
return str_len;
|
||||
}
|
||||
|
||||
std::vector<std::string> split_utf8_str(const std::string& str)
|
||||
{
|
||||
std::vector<std::string> split_utf8_str(const std::string& str) {
|
||||
std::vector<std::string> result;
|
||||
std::string out_str;
|
||||
|
||||
for (char c : str)
|
||||
for (char c : str) {
|
||||
if ((c & 0xc0) != 0x80) // new UTF-8 character
|
||||
{
|
||||
if ((c & 0xc0) != 0x80) //new UTF-8 character
|
||||
{
|
||||
if (!out_str.empty())
|
||||
{
|
||||
result.push_back(out_str);
|
||||
out_str.clear();
|
||||
}
|
||||
}
|
||||
|
||||
out_str.append(1, c);
|
||||
if (!out_str.empty()) {
|
||||
result.push_back(out_str);
|
||||
out_str.clear();
|
||||
}
|
||||
}
|
||||
|
||||
out_str.append(1, c);
|
||||
}
|
||||
result.push_back(out_str);
|
||||
return result;
|
||||
}
|
||||
|
||||
std::vector<std::string> split_str(const std::string &s,
|
||||
const std::string &delim) {
|
||||
std::vector<std::string> result;
|
||||
std::size_t start = 0, delim_len = delim.size();
|
||||
while (true) {
|
||||
std::size_t end = s.find(delim, start);
|
||||
if (end == std::string::npos) {
|
||||
if (start < s.size()) {
|
||||
result.push_back(s.substr(start));
|
||||
}
|
||||
break;
|
||||
}
|
||||
if (end > start) {
|
||||
result.push_back(s.substr(start, end - start));
|
||||
}
|
||||
start = end + delim_len;
|
||||
std::vector<std::string> split_str(const std::string& s,
|
||||
const std::string& delim) {
|
||||
std::vector<std::string> result;
|
||||
std::size_t start = 0, delim_len = delim.size();
|
||||
while (true) {
|
||||
std::size_t end = s.find(delim, start);
|
||||
if (end == std::string::npos) {
|
||||
if (start < s.size()) {
|
||||
result.push_back(s.substr(start));
|
||||
}
|
||||
break;
|
||||
}
|
||||
if (end > start) {
|
||||
result.push_back(s.substr(start, end - start));
|
||||
}
|
||||
return result;
|
||||
start = end + delim_len;
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
bool prefix_compare(const PathTrie* x, const PathTrie* y) {
|
||||
if (x->_score == y->_score) {
|
||||
if (x->_character == y->_character) {
|
||||
return false;
|
||||
} else {
|
||||
return (x->_character < y->_character);
|
||||
}
|
||||
bool prefix_compare(const PathTrie* x, const PathTrie* y) {
|
||||
if (x->score == y->score) {
|
||||
if (x->character == y->character) {
|
||||
return false;
|
||||
} else {
|
||||
return x->_score > y->_score;
|
||||
return (x->character < y->character);
|
||||
}
|
||||
} else {
|
||||
return x->score > y->score;
|
||||
}
|
||||
}
|
||||
|
||||
void add_word_to_fst(const std::vector<int>& word,
|
||||
fst::StdVectorFst* dictionary) {
|
||||
if (dictionary->NumStates() == 0) {
|
||||
fst::StdVectorFst::StateId start = dictionary->AddState();
|
||||
assert(start == 0);
|
||||
dictionary->SetStart(start);
|
||||
}
|
||||
fst::StdVectorFst::StateId src = dictionary->Start();
|
||||
fst::StdVectorFst::StateId dst;
|
||||
for (auto c : word) {
|
||||
dst = dictionary->AddState();
|
||||
dictionary->AddArc(src, fst::StdArc(c, c, 0, dst));
|
||||
src = dst;
|
||||
}
|
||||
dictionary->SetFinal(dst, fst::StdArc::Weight::One());
|
||||
if (dictionary->NumStates() == 0) {
|
||||
fst::StdVectorFst::StateId start = dictionary->AddState();
|
||||
assert(start == 0);
|
||||
dictionary->SetStart(start);
|
||||
}
|
||||
fst::StdVectorFst::StateId src = dictionary->Start();
|
||||
fst::StdVectorFst::StateId dst;
|
||||
for (auto c : word) {
|
||||
dst = dictionary->AddState();
|
||||
dictionary->AddArc(src, fst::StdArc(c, c, 0, dst));
|
||||
src = dst;
|
||||
}
|
||||
dictionary->SetFinal(dst, fst::StdArc::Weight::One());
|
||||
}
|
||||
|
||||
bool add_word_to_dictionary(const std::string& word,
|
||||
const std::unordered_map<std::string, int>& char_map,
|
||||
bool add_space,
|
||||
int SPACE_ID,
|
||||
fst::StdVectorFst* dictionary) {
|
||||
auto characters = split_utf8_str(word);
|
||||
bool add_word_to_dictionary(
|
||||
const std::string& word,
|
||||
const std::unordered_map<std::string, int>& char_map,
|
||||
bool add_space,
|
||||
int SPACE_ID,
|
||||
fst::StdVectorFst* dictionary) {
|
||||
auto characters = split_utf8_str(word);
|
||||
|
||||
std::vector<int> int_word;
|
||||
std::vector<int> int_word;
|
||||
|
||||
for (auto& c : characters) {
|
||||
if (c == " ") {
|
||||
int_word.push_back(SPACE_ID);
|
||||
} else {
|
||||
auto int_c = char_map.find(c);
|
||||
if (int_c != char_map.end()) {
|
||||
int_word.push_back(int_c->second);
|
||||
} else {
|
||||
return false; // return without adding
|
||||
}
|
||||
}
|
||||
for (auto& c : characters) {
|
||||
if (c == " ") {
|
||||
int_word.push_back(SPACE_ID);
|
||||
} else {
|
||||
auto int_c = char_map.find(c);
|
||||
if (int_c != char_map.end()) {
|
||||
int_word.push_back(int_c->second);
|
||||
} else {
|
||||
return false; // return without adding
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (add_space) {
|
||||
int_word.push_back(SPACE_ID);
|
||||
}
|
||||
if (add_space) {
|
||||
int_word.push_back(SPACE_ID);
|
||||
}
|
||||
|
||||
add_word_to_fst(int_word, dictionary);
|
||||
return true;
|
||||
add_word_to_fst(int_word, dictionary);
|
||||
return true;
|
||||
}
|
||||
|
||||
@ -1,59 +1,57 @@
|
||||
#ifndef PATH_TRIE_H
|
||||
#define PATH_TRIE_H
|
||||
#pragma once
|
||||
#include <fst/fstlib.h>
|
||||
#include <algorithm>
|
||||
#include <limits>
|
||||
#include <memory>
|
||||
#include <utility>
|
||||
#include <vector>
|
||||
#include <fst/fstlib.h>
|
||||
|
||||
using FSTMATCH = fst::SortedMatcher<fst::StdVectorFst>;
|
||||
|
||||
class PathTrie {
|
||||
public:
|
||||
PathTrie();
|
||||
~PathTrie();
|
||||
|
||||
PathTrie* get_path_trie(int new_char, bool reset = true);
|
||||
PathTrie();
|
||||
~PathTrie();
|
||||
|
||||
PathTrie* get_path_vec(std::vector<int> &output);
|
||||
PathTrie* get_path_trie(int new_char, bool reset = true);
|
||||
|
||||
PathTrie* get_path_vec(std::vector<int>& output,
|
||||
int stop,
|
||||
size_t max_steps = std::numeric_limits<size_t>::max());
|
||||
PathTrie* get_path_vec(std::vector<int>& output);
|
||||
|
||||
void iterate_to_vec(std::vector<PathTrie*> &output);
|
||||
PathTrie* get_path_vec(std::vector<int>& output,
|
||||
int stop,
|
||||
size_t max_steps = std::numeric_limits<size_t>::max());
|
||||
|
||||
void set_dictionary(fst::StdVectorFst* dictionary);
|
||||
void iterate_to_vec(std::vector<PathTrie*>& output);
|
||||
|
||||
void set_matcher(std::shared_ptr<FSTMATCH> matcher);
|
||||
void set_dictionary(fst::StdVectorFst* dictionary);
|
||||
|
||||
bool is_empty() {
|
||||
return _ROOT == _character;
|
||||
}
|
||||
void set_matcher(std::shared_ptr<FSTMATCH> matcher);
|
||||
|
||||
void remove();
|
||||
bool is_empty() { return _ROOT == character; }
|
||||
|
||||
float _log_prob_b_prev;
|
||||
float _log_prob_nb_prev;
|
||||
float _log_prob_b_cur;
|
||||
float _log_prob_nb_cur;
|
||||
float _score;
|
||||
float _approx_ctc;
|
||||
void remove();
|
||||
|
||||
float log_prob_b_prev;
|
||||
float log_prob_nb_prev;
|
||||
float log_prob_b_cur;
|
||||
float log_prob_nb_cur;
|
||||
float score;
|
||||
float approx_ctc;
|
||||
int character;
|
||||
PathTrie* parent;
|
||||
|
||||
int _ROOT;
|
||||
int _character;
|
||||
bool _exists;
|
||||
private:
|
||||
int _ROOT;
|
||||
bool _exists;
|
||||
|
||||
PathTrie *_parent;
|
||||
std::vector<std::pair<int, PathTrie*> > _children;
|
||||
std::vector<std::pair<int, PathTrie*>> _children;
|
||||
|
||||
fst::StdVectorFst* _dictionary;
|
||||
fst::StdVectorFst::StateId _dictionary_state;
|
||||
bool _has_dictionary;
|
||||
std::shared_ptr<FSTMATCH> _matcher;
|
||||
fst::StdVectorFst* _dictionary;
|
||||
fst::StdVectorFst::StateId _dictionary_state;
|
||||
bool _has_dictionary;
|
||||
std::shared_ptr<FSTMATCH> _matcher;
|
||||
};
|
||||
|
||||
#endif // PATH_TRIE_H
|
||||
#endif // PATH_TRIE_H
|
||||
|
||||
@ -1,219 +1,208 @@
|
||||
#include <iostream>
|
||||
#include "scorer.h"
|
||||
#include <unistd.h>
|
||||
#include <iostream>
|
||||
#include "decoder_utils.h"
|
||||
#include "lm/config.hh"
|
||||
#include "lm/state.hh"
|
||||
#include "lm/model.hh"
|
||||
#include "util/tokenize_piece.hh"
|
||||
#include "lm/state.hh"
|
||||
#include "util/string_piece.hh"
|
||||
#include "scorer.h"
|
||||
#include "decoder_utils.h"
|
||||
#include "util/tokenize_piece.hh"
|
||||
|
||||
using namespace lm::ngram;
|
||||
|
||||
Scorer::Scorer(double alpha, double beta, const std::string& lm_path) {
|
||||
this->alpha = alpha;
|
||||
this->beta = beta;
|
||||
_is_character_based = true;
|
||||
_language_model = nullptr;
|
||||
dictionary = nullptr;
|
||||
_max_order = 0;
|
||||
_SPACE_ID = -1;
|
||||
// load language model
|
||||
load_LM(lm_path.c_str());
|
||||
this->alpha = alpha;
|
||||
this->beta = beta;
|
||||
_is_character_based = true;
|
||||
_language_model = nullptr;
|
||||
dictionary = nullptr;
|
||||
_max_order = 0;
|
||||
_SPACE_ID = -1;
|
||||
// load language model
|
||||
load_LM(lm_path.c_str());
|
||||
}
|
||||
|
||||
Scorer::~Scorer() {
|
||||
if (_language_model != nullptr)
|
||||
delete static_cast<lm::base::Model*>(_language_model);
|
||||
if (dictionary != nullptr)
|
||||
delete static_cast<fst::StdVectorFst*>(dictionary);
|
||||
if (_language_model != nullptr)
|
||||
delete static_cast<lm::base::Model*>(_language_model);
|
||||
if (dictionary != nullptr) delete static_cast<fst::StdVectorFst*>(dictionary);
|
||||
}
|
||||
|
||||
void Scorer::load_LM(const char* filename) {
|
||||
if (access(filename, F_OK) != 0) {
|
||||
std::cerr << "Invalid language model file !!!" << std::endl;
|
||||
exit(1);
|
||||
}
|
||||
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;
|
||||
}
|
||||
if (access(filename, F_OK) != 0) {
|
||||
std::cerr << "Invalid language model file !!!" << std::endl;
|
||||
exit(1);
|
||||
}
|
||||
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;
|
||||
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;
|
||||
}
|
||||
// log10 prob
|
||||
return cond_prob;
|
||||
cond_prob = model->BaseScore(&state, word_index, &out_state);
|
||||
tmp_state = state;
|
||||
state = out_state;
|
||||
out_state = tmp_state;
|
||||
}
|
||||
// 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());
|
||||
std::vector<std::string> sentence;
|
||||
if (words.size() == 0) {
|
||||
for (size_t i = 0; i < _max_order; ++i) {
|
||||
sentence.push_back(START_TOKEN);
|
||||
}
|
||||
sentence.push_back(END_TOKEN);
|
||||
return get_log_prob(sentence);
|
||||
} 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;
|
||||
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;
|
||||
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::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;
|
||||
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(std::vector<std::string> char_list) {
|
||||
_char_list = char_list;
|
||||
_char_map.clear();
|
||||
|
||||
for(unsigned int 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;
|
||||
}
|
||||
_char_list = char_list;
|
||||
_char_map.clear();
|
||||
|
||||
for (unsigned int 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) {
|
||||
std::vector<std::string> ngram;
|
||||
PathTrie* current_node = prefix;
|
||||
PathTrie* new_node = nullptr;
|
||||
|
||||
fst::StdVectorFst dictionary;
|
||||
// First reverse char_list so ints can be accessed by chars
|
||||
std::unordered_map<std::string, int> char_map;
|
||||
for (unsigned int i = 0; i < _char_list.size(); i++) {
|
||||
char_map[_char_list[i]] = i;
|
||||
}
|
||||
for (int order = 0; order < _max_order; order++) {
|
||||
std::vector<int> prefix_vec;
|
||||
|
||||
// For each unigram convert to ints and put in trie
|
||||
int vocab_size = 0;
|
||||
for (const auto& word : _vocabulary) {
|
||||
bool added = add_word_to_dictionary(word,
|
||||
char_map,
|
||||
add_space,
|
||||
_SPACE_ID,
|
||||
&dictionary);
|
||||
vocab_size += added ? 1 : 0;
|
||||
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
|
||||
}
|
||||
|
||||
std::cerr << "Vocab Size " << vocab_size << std::endl;
|
||||
|
||||
// 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;
|
||||
// reconstruct word
|
||||
std::string word = vec2str(prefix_vec);
|
||||
ngram.push_back(word);
|
||||
|
||||
// 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;
|
||||
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 (unsigned int i = 0; i < _char_list.size(); i++) {
|
||||
char_map[_char_list[i]] = i;
|
||||
}
|
||||
|
||||
// For each unigram convert to ints and put in trie
|
||||
int vocab_size = 0;
|
||||
for (const auto& word : _vocabulary) {
|
||||
bool added = add_word_to_dictionary(
|
||||
word, char_map, add_space, _SPACE_ID, &dictionary);
|
||||
vocab_size += added ? 1 : 0;
|
||||
}
|
||||
|
||||
std::cerr << "Vocab Size " << vocab_size << std::endl;
|
||||
|
||||
// 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;
|
||||
}
|
||||
|
||||
Loading…
Reference in new issue