PaddleSpeech/deepspeech/decoders/swig/scorer.cpp

// 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.

#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();

    // Set the char map for the FST for spelling correction
    for (size_t i = 0; i < char_list_.size(); i++) {
        if (char_list_[i] == " ") {
            SPACE_ID_ = i;
        }
        // The initial state of FST is state 0, hence the index of chars in
        // the FST should start from 1 to avoid the conflict with the initial
        // state, otherwise wrong decoding results would be given.
        char_map_[char_list_[i]] = i + 1;
    }
}

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;
    // 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_ + 1, &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;
}