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PaddleSpeech/deepspeech/decoders/swig/ctc_beam_search_decoder.cpp

245 lines
9.3 KiB

// 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 "ctc_beam_search_decoder.h"
#include <algorithm>
#include <cmath>
#include <iostream>
#include <limits>
#include <map>
#include <utility>
#include "ThreadPool.h"
#include "fst/fstlib.h"
#include "decoder_utils.h"
#include "path_trie.h"
using FSTMATCH = fst::SortedMatcher<fst::StdVectorFst>;
const std::string kSPACE = "<space>";
std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
const std::vector<std::vector<double>> &probs_seq,
const std::vector<std::string> &vocabulary,
size_t beam_size,
double cutoff_prob,
size_t cutoff_top_n,
Scorer *ext_scorer,
size_t blank_id) {
// dimension check
size_t num_time_steps = probs_seq.size();
for (size_t i = 0; i < num_time_steps; ++i) {
VALID_CHECK_EQ(probs_seq[i].size(),
// vocabulary.size() + 1,
vocabulary.size(),
"The shape of probs_seq does not match with "
"the shape of the vocabulary");
}
// assign space id
auto it = std::find(vocabulary.begin(), vocabulary.end(), kSPACE);
int space_id = it - vocabulary.begin();
// if no space in vocabulary
if ((size_t)space_id >= vocabulary.size()) {
space_id = -2;
}
// init prefixes' root
PathTrie root;
root.score = root.log_prob_b_prev = 0.0;
std::vector<PathTrie *> prefixes;
prefixes.push_back(&root);
if (ext_scorer != nullptr && !ext_scorer->is_character_based()) {
auto fst_dict =
static_cast<fst::StdVectorFst *>(ext_scorer->dictionary);
fst::StdVectorFst *dict_ptr = fst_dict->Copy(true);
root.set_dictionary(dict_ptr);
auto matcher = std::make_shared<FSTMATCH>(*dict_ptr, fst::MATCH_INPUT);
root.set_matcher(matcher);
}
// prefix search over time
for (size_t time_step = 0; time_step < num_time_steps; ++time_step) {
auto &prob = probs_seq[time_step];
float min_cutoff = -NUM_FLT_INF;
bool full_beam = false;
if (ext_scorer != nullptr) {
size_t num_prefixes = std::min(prefixes.size(), beam_size);
std::sort(prefixes.begin(),
prefixes.begin() + num_prefixes,
prefix_compare);
min_cutoff = prefixes[num_prefixes - 1]->score +
std::log(prob[blank_id]) -
std::max(0.0, ext_scorer->beta);
full_beam = (num_prefixes == beam_size);
}
std::vector<std::pair<size_t, float>> log_prob_idx =
get_pruned_log_probs(prob, cutoff_prob, cutoff_top_n);
// loop over chars
for (size_t index = 0; index < log_prob_idx.size(); index++) {
auto c = log_prob_idx[index].first;
auto log_prob_c = log_prob_idx[index].second;
for (size_t i = 0; i < prefixes.size() && i < beam_size; ++i) {
auto prefix = prefixes[i];
if (full_beam && log_prob_c + prefix->score < min_cutoff) {
break;
}
// blank
if (c == blank_id) {
prefix->log_prob_b_cur = log_sum_exp(
prefix->log_prob_b_cur, log_prob_c + prefix->score);
continue;
}
// repeated character
if (c == prefix->character) {
prefix->log_prob_nb_cur =
log_sum_exp(prefix->log_prob_nb_cur,
log_prob_c + prefix->log_prob_nb_prev);
}
// get new prefix
auto prefix_new = prefix->get_path_trie(c);
if (prefix_new != nullptr) {
float log_p = -NUM_FLT_INF;
if (c == prefix->character &&
prefix->log_prob_b_prev > -NUM_FLT_INF) {
log_p = log_prob_c + prefix->log_prob_b_prev;
} else if (c != prefix->character) {
log_p = log_prob_c + prefix->score;
}
// language model scoring
if (ext_scorer != nullptr &&
(c == space_id || ext_scorer->is_character_based())) {
PathTrie *prefix_to_score = nullptr;
// skip scoring the space
if (ext_scorer->is_character_based()) {
prefix_to_score = prefix_new;
} else {
prefix_to_score = prefix;
}
float score = 0.0;
std::vector<std::string> ngram;
ngram = ext_scorer->make_ngram(prefix_to_score);
score = ext_scorer->get_log_cond_prob(ngram) *
ext_scorer->alpha;
log_p += score;
log_p += ext_scorer->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 vocabulary
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();
}
}
} // end of loop over time
// score the last word of each prefix that doesn't end with space
if (ext_scorer != nullptr && !ext_scorer->is_character_based()) {
for (size_t i = 0; i < beam_size && i < prefixes.size(); ++i) {
auto prefix = prefixes[i];
if (!prefix->is_empty() && prefix->character != space_id) {
float score = 0.0;
std::vector<std::string> ngram = ext_scorer->make_ngram(prefix);
score =
ext_scorer->get_log_cond_prob(ngram) * ext_scorer->alpha;
score += ext_scorer->beta;
prefix->score += score;
}
}
}
size_t num_prefixes = std::min(prefixes.size(), beam_size);
std::sort(
prefixes.begin(), prefixes.begin() + num_prefixes, prefix_compare);
// compute aproximate ctc score as the return score, without affecting the
// return order of decoding result. To delete when decoder gets stable.
for (size_t i = 0; i < beam_size && i < prefixes.size(); ++i) {
double approx_ctc = prefixes[i]->score;
if (ext_scorer != nullptr) {
std::vector<int> output;
prefixes[i]->get_path_vec(output);
auto prefix_length = output.size();
auto words = ext_scorer->split_labels(output);
// remove word insert
approx_ctc = approx_ctc - prefix_length * ext_scorer->beta;
// remove language model weight:
approx_ctc -=
(ext_scorer->get_sent_log_prob(words)) * ext_scorer->alpha;
}
prefixes[i]->approx_ctc = approx_ctc;
}
return get_beam_search_result(prefixes, vocabulary, beam_size);
}
std::vector<std::vector<std::pair<double, std::string>>>
ctc_beam_search_decoder_batch(
const std::vector<std::vector<std::vector<double>>> &probs_split,
const std::vector<std::string> &vocabulary,
size_t beam_size,
size_t num_processes,
double cutoff_prob,
size_t cutoff_top_n,
Scorer *ext_scorer,
size_t blank_id) {
VALID_CHECK_GT(num_processes, 0, "num_processes must be nonnegative!");
// thread pool
ThreadPool pool(num_processes);
// number of samples
size_t batch_size = probs_split.size();
// enqueue the tasks of decoding
std::vector<std::future<std::vector<std::pair<double, std::string>>>> res;
for (size_t i = 0; i < batch_size; ++i) {
res.emplace_back(pool.enqueue(ctc_beam_search_decoder,
probs_split[i],
vocabulary,
beam_size,
cutoff_prob,
cutoff_top_n,
ext_scorer,
blank_id));
}
// get decoding results
std::vector<std::vector<std::pair<double, std::string>>> batch_results;
for (size_t i = 0; i < batch_size; ++i) {
batch_results.emplace_back(res[i].get());
}
return batch_results;
}