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205 lines
6.8 KiB
205 lines
6.8 KiB
#include "ctc_beam_search_decoder.h"
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#include <algorithm>
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#include <cmath>
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#include <iostream>
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#include <limits>
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#include <map>
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#include <utility>
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#include "ThreadPool.h"
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#include "fst/fstlib.h"
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#include "decoder_utils.h"
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#include "path_trie.h"
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using FSTMATCH = fst::SortedMatcher<fst::StdVectorFst>;
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std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
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const std::vector<std::vector<double>> &probs_seq,
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const std::vector<std::string> &vocabulary,
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size_t beam_size,
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double cutoff_prob,
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size_t cutoff_top_n,
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Scorer *ext_scorer) {
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// dimension check
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size_t num_time_steps = probs_seq.size();
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for (size_t i = 0; i < num_time_steps; ++i) {
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VALID_CHECK_EQ(probs_seq[i].size(),
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vocabulary.size() + 1,
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"The shape of probs_seq does not match with "
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"the shape of the vocabulary");
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}
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// assign blank id
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size_t blank_id = vocabulary.size();
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// assign space id
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auto it = 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 ((size_t)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 (ext_scorer != nullptr && !ext_scorer->is_character_based()) {
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auto fst_dict = static_cast<fst::StdVectorFst *>(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>(*dict_ptr, fst::MATCH_INPUT);
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root.set_matcher(matcher);
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}
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// prefix search over time
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for (size_t time_step = 0; time_step < num_time_steps; ++time_step) {
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auto &prob = probs_seq[time_step];
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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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size_t num_prefixes = std::min(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 +
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std::log(prob[blank_id]) - 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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std::vector<std::pair<size_t, float>> log_prob_idx =
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get_pruned_log_probs(prob, cutoff_prob, cutoff_top_n);
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// loop over chars
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for (size_t index = 0; index < log_prob_idx.size(); index++) {
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auto c = log_prob_idx[index].first;
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auto log_prob_c = log_prob_idx[index].second;
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for (size_t 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 =
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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 (ext_scorer != nullptr &&
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(c == space_id || ext_scorer->is_character_based())) {
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PathTrie *prefix_toscore = nullptr;
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// skip scoring the space
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if (ext_scorer->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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double score = 0.0;
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std::vector<std::string> ngram;
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ngram = ext_scorer->make_ngram(prefix_toscore);
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score = ext_scorer->get_log_cond_prob(ngram) * 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 =
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log_sum_exp(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 vocabulary
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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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}
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} // end of loop over time
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// compute aproximate ctc score as the return score, without affecting the
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// return order of decoding result. To delete when decoder gets stable.
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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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auto 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)) * ext_scorer->alpha;
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}
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prefixes[i]->approx_ctc = approx_ctc;
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}
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return get_beam_search_result(prefixes, vocabulary, beam_size);
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}
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std::vector<std::vector<std::pair<double, std::string>>>
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ctc_beam_search_decoder_batch(
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const std::vector<std::vector<std::vector<double>>> &probs_split,
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const std::vector<std::string> &vocabulary,
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size_t beam_size,
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size_t num_processes,
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double cutoff_prob,
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size_t cutoff_top_n,
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Scorer *ext_scorer) {
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VALID_CHECK_GT(num_processes, 0, "num_processes must be nonnegative!");
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// thread pool
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ThreadPool pool(num_processes);
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// number of samples
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size_t batch_size = probs_split.size();
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// enqueue the tasks of decoding
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std::vector<std::future<std::vector<std::pair<double, std::string>>>> res;
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for (size_t i = 0; i < batch_size; ++i) {
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res.emplace_back(pool.enqueue(ctc_beam_search_decoder,
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probs_split[i],
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vocabulary,
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beam_size,
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cutoff_prob,
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cutoff_top_n,
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ext_scorer));
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}
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// get decoding results
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std::vector<std::vector<std::pair<double, std::string>>> batch_results;
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for (size_t i = 0; i < batch_size; ++i) {
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batch_results.emplace_back(res[i].get());
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}
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return batch_results;
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}
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