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.
113 lines
3.0 KiB
113 lines
3.0 KiB
#ifndef SCORER_H_
|
|
#define SCORER_H_
|
|
|
|
#include <memory>
|
|
#include <string>
|
|
#include <unordered_map>
|
|
#include <vector>
|
|
|
|
#include "lm/enumerate_vocab.hh"
|
|
#include "lm/virtual_interface.hh"
|
|
#include "lm/word_index.hh"
|
|
#include "util/string_piece.hh"
|
|
|
|
#include "path_trie.h"
|
|
|
|
const double OOV_SCORE = -1000.0;
|
|
const std::string START_TOKEN = "<s>";
|
|
const std::string UNK_TOKEN = "<unk>";
|
|
const std::string END_TOKEN = "</s>";
|
|
|
|
// Implement a callback to retrive the dictionary of language model.
|
|
class RetriveStrEnumerateVocab : public lm::EnumerateVocab {
|
|
public:
|
|
RetriveStrEnumerateVocab() {}
|
|
|
|
void Add(lm::WordIndex index, const StringPiece &str) {
|
|
vocabulary.push_back(std::string(str.data(), str.length()));
|
|
}
|
|
|
|
std::vector<std::string> vocabulary;
|
|
};
|
|
|
|
/* External scorer to query score for n-gram or sentence, including language
|
|
* model scoring and word insertion.
|
|
*
|
|
* Example:
|
|
* Scorer scorer(alpha, beta, "path_of_language_model");
|
|
* scorer.get_log_cond_prob({ "WORD1", "WORD2", "WORD3" });
|
|
* scorer.get_sent_log_prob({ "WORD1", "WORD2", "WORD3" });
|
|
*/
|
|
class Scorer {
|
|
public:
|
|
Scorer(double alpha,
|
|
double beta,
|
|
const std::string &lm_path,
|
|
const std::vector<std::string> &vocabulary);
|
|
~Scorer();
|
|
|
|
double get_log_cond_prob(const std::vector<std::string> &words);
|
|
|
|
double get_sent_log_prob(const std::vector<std::string> &words);
|
|
|
|
// return the max order
|
|
size_t get_max_order() const { return max_order_; }
|
|
|
|
// return the dictionary size of language model
|
|
size_t get_dict_size() const { return dict_size_; }
|
|
|
|
// retrun true if the language model is character based
|
|
bool is_character_based() const { return is_character_based_; }
|
|
|
|
// reset params alpha & beta
|
|
void reset_params(float alpha, float beta);
|
|
|
|
// make ngram for a given prefix
|
|
std::vector<std::string> make_ngram(PathTrie *prefix);
|
|
|
|
// trransform the labels in index to the vector of words (word based lm) or
|
|
// the vector of characters (character based lm)
|
|
std::vector<std::string> split_labels(const std::vector<int> &labels);
|
|
|
|
// language model weight
|
|
double alpha;
|
|
// word insertion weight
|
|
double beta;
|
|
|
|
// pointer to the dictionary of FST
|
|
void *dictionary;
|
|
|
|
protected:
|
|
// necessary setup: load language model, set char map, fill FST's dictionary
|
|
void setup(const std::string &lm_path,
|
|
const std::vector<std::string> &vocab_list);
|
|
|
|
// load language model from given path
|
|
void load_lm(const std::string &lm_path);
|
|
|
|
// fill dictionary for FST
|
|
void fill_dictionary(bool add_space);
|
|
|
|
// set char map
|
|
void set_char_map(const std::vector<std::string> &char_list);
|
|
|
|
double get_log_prob(const std::vector<std::string> &words);
|
|
|
|
// translate the vector in index to string
|
|
std::string vec2str(const std::vector<int> &input);
|
|
|
|
private:
|
|
void *language_model_;
|
|
bool is_character_based_;
|
|
size_t max_order_;
|
|
size_t dict_size_;
|
|
|
|
int SPACE_ID_;
|
|
std::vector<std::string> char_list_;
|
|
std::unordered_map<std::string, int> char_map_;
|
|
|
|
std::vector<std::string> vocabulary_;
|
|
};
|
|
|
|
#endif // SCORER_H_
|