YangZhou
3 years ago
parent
f880229c25
commit
a2b5eb19c8
@ -1 +0,0 @@
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paddlepaddle>=2.4rc
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@ -0,0 +1,11 @@
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cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
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project(ASR LANGUAGES CXX)
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include_directories(${CMAKE_CURRENT_SOURCE_DIR})
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include_directories(${CMAKE_CURRENT_SOURCE_DIR}/server)
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add_subdirectory(decoder)
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add_subdirectory(recognizer)
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add_subdirectory(nnet)
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add_subdirectory(server)
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@ -0,0 +1,9 @@
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ThreadPool/
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build/
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dist/
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kenlm/
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openfst-1.6.3/
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openfst-1.6.3.tar.gz
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swig_decoders.egg-info/
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decoders_wrap.cxx
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swig_decoders.py
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@ -0,0 +1,201 @@
|
||||
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@ -0,0 +1,13 @@
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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@ -0,0 +1,607 @@
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// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "COPYING.APACHE2.0");
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// you may not use this file except in compliance with the License.
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
|
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||||
#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>;
|
||||
|
||||
|
||||
std::vector<std::pair<double, std::string>> ctc_beam_search_decoding(
|
||||
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 approximate 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_decoding_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_decoding,
|
||||
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;
|
||||
}
|
||||
|
||||
void ctc_beam_search_decode_chunk_begin(PathTrie *root, Scorer *ext_scorer) {
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
||||
void ctc_beam_search_decode_chunk(
|
||||
PathTrie *root,
|
||||
std::vector<PathTrie *> &prefixes,
|
||||
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
|
||||
//
|
||||
// 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
|
||||
|
||||
return;
|
||||
}
|
||||
|
||||
|
||||
std::vector<std::pair<double, std::string>> get_decode_result(
|
||||
std::vector<PathTrie *> &prefixes,
|
||||
const std::vector<std::string> &vocabulary,
|
||||
size_t beam_size,
|
||||
Scorer *ext_scorer) {
|
||||
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;
|
||||
}
|
||||
// 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;
|
||||
}
|
||||
|
||||
std::vector<std::pair<double, std::string>> res =
|
||||
get_beam_search_result(prefixes, vocabulary, beam_size);
|
||||
|
||||
// pay back the last word of each prefix that doesn't end with space (for
|
||||
// decoding by chunk)
|
||||
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;
|
||||
}
|
||||
}
|
||||
}
|
||||
return res;
|
||||
}
|
||||
|
||||
|
||||
void free_storage(std::unique_ptr<CtcBeamSearchDecoderStorage> &storage) {
|
||||
storage = nullptr;
|
||||
}
|
||||
|
||||
|
||||
CtcBeamSearchDecoderBatch::~CtcBeamSearchDecoderBatch() {}
|
||||
|
||||
CtcBeamSearchDecoderBatch::CtcBeamSearchDecoderBatch(
|
||||
const std::vector<std::string> &vocabulary,
|
||||
size_t batch_size,
|
||||
size_t beam_size,
|
||||
size_t num_processes,
|
||||
double cutoff_prob,
|
||||
size_t cutoff_top_n,
|
||||
Scorer *ext_scorer,
|
||||
size_t blank_id)
|
||||
: batch_size(batch_size),
|
||||
beam_size(beam_size),
|
||||
num_processes(num_processes),
|
||||
cutoff_prob(cutoff_prob),
|
||||
cutoff_top_n(cutoff_top_n),
|
||||
ext_scorer(ext_scorer),
|
||||
blank_id(blank_id) {
|
||||
VALID_CHECK_GT(this->beam_size, 0, "beam_size must be greater than 0!");
|
||||
VALID_CHECK_GT(
|
||||
this->num_processes, 0, "num_processes must be nonnegative!");
|
||||
this->vocabulary = vocabulary;
|
||||
for (size_t i = 0; i < batch_size; i++) {
|
||||
this->decoder_storage_vector.push_back(
|
||||
std::unique_ptr<CtcBeamSearchDecoderStorage>(
|
||||
new CtcBeamSearchDecoderStorage()));
|
||||
ctc_beam_search_decode_chunk_begin(
|
||||
this->decoder_storage_vector[i]->root, ext_scorer);
|
||||
}
|
||||
};
|
||||
|
||||
/**
|
||||
* Input
|
||||
* probs_split: shape [B, T, D]
|
||||
*/
|
||||
void CtcBeamSearchDecoderBatch::next(
|
||||
const std::vector<std::vector<std::vector<double>>> &probs_split,
|
||||
const std::vector<std::string> &has_value) {
|
||||
VALID_CHECK_GT(num_processes, 0, "num_processes must be nonnegative!");
|
||||
// thread pool
|
||||
size_t num_has_value = 0;
|
||||
for (int i = 0; i < has_value.size(); i++)
|
||||
if (has_value[i] == "true") num_has_value += 1;
|
||||
ThreadPool pool(std::min(num_processes, num_has_value));
|
||||
// number of samples
|
||||
size_t probs_num = probs_split.size();
|
||||
VALID_CHECK_EQ(this->batch_size,
|
||||
probs_num,
|
||||
"The batch size of the current input data should be same "
|
||||
"with the input data before");
|
||||
|
||||
// enqueue the tasks of decoding
|
||||
std::vector<std::future<void>> res;
|
||||
for (size_t i = 0; i < batch_size; ++i) {
|
||||
if (has_value[i] == "true") {
|
||||
res.emplace_back(pool.enqueue(
|
||||
ctc_beam_search_decode_chunk,
|
||||
std::ref(this->decoder_storage_vector[i]->root),
|
||||
std::ref(this->decoder_storage_vector[i]->prefixes),
|
||||
probs_split[i],
|
||||
this->vocabulary,
|
||||
this->beam_size,
|
||||
this->cutoff_prob,
|
||||
this->cutoff_top_n,
|
||||
this->ext_scorer,
|
||||
this->blank_id));
|
||||
}
|
||||
}
|
||||
|
||||
for (size_t i = 0; i < batch_size; ++i) {
|
||||
res[i].get();
|
||||
}
|
||||
return;
|
||||
};
|
||||
|
||||
/**
|
||||
* Return
|
||||
* batch_result: shape[B, beam_size,(-approx_ctc score, string)]
|
||||
*/
|
||||
std::vector<std::vector<std::pair<double, std::string>>>
|
||||
CtcBeamSearchDecoderBatch::decode() {
|
||||
VALID_CHECK_GT(
|
||||
this->num_processes, 0, "num_processes must be nonnegative!");
|
||||
// thread pool
|
||||
ThreadPool pool(this->num_processes);
|
||||
// number of samples
|
||||
// enqueue the tasks of decoding
|
||||
std::vector<std::future<std::vector<std::pair<double, std::string>>>> res;
|
||||
for (size_t i = 0; i < this->batch_size; ++i) {
|
||||
res.emplace_back(
|
||||
pool.enqueue(get_decode_result,
|
||||
std::ref(this->decoder_storage_vector[i]->prefixes),
|
||||
this->vocabulary,
|
||||
this->beam_size,
|
||||
this->ext_scorer));
|
||||
}
|
||||
// get decoding results
|
||||
std::vector<std::vector<std::pair<double, std::string>>> batch_results;
|
||||
for (size_t i = 0; i < this->batch_size; ++i) {
|
||||
batch_results.emplace_back(res[i].get());
|
||||
}
|
||||
return batch_results;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* reset the state of ctcBeamSearchDecoderBatch
|
||||
*/
|
||||
void CtcBeamSearchDecoderBatch::reset_state(size_t batch_size,
|
||||
size_t beam_size,
|
||||
size_t num_processes,
|
||||
double cutoff_prob,
|
||||
size_t cutoff_top_n) {
|
||||
this->batch_size = batch_size;
|
||||
this->beam_size = beam_size;
|
||||
this->num_processes = num_processes;
|
||||
this->cutoff_prob = cutoff_prob;
|
||||
this->cutoff_top_n = cutoff_top_n;
|
||||
|
||||
VALID_CHECK_GT(this->beam_size, 0, "beam_size must be greater than 0!");
|
||||
VALID_CHECK_GT(
|
||||
this->num_processes, 0, "num_processes must be nonnegative!");
|
||||
// thread pool
|
||||
ThreadPool pool(this->num_processes);
|
||||
// number of samples
|
||||
// enqueue the tasks of decoding
|
||||
std::vector<std::future<void>> res;
|
||||
size_t storage_size = decoder_storage_vector.size();
|
||||
for (size_t i = 0; i < storage_size; i++) {
|
||||
res.emplace_back(pool.enqueue(
|
||||
free_storage, std::ref(this->decoder_storage_vector[i])));
|
||||
}
|
||||
for (size_t i = 0; i < storage_size; ++i) {
|
||||
res[i].get();
|
||||
}
|
||||
std::vector<std::unique_ptr<CtcBeamSearchDecoderStorage>>().swap(
|
||||
decoder_storage_vector);
|
||||
for (size_t i = 0; i < this->batch_size; i++) {
|
||||
this->decoder_storage_vector.push_back(
|
||||
std::unique_ptr<CtcBeamSearchDecoderStorage>(
|
||||
new CtcBeamSearchDecoderStorage()));
|
||||
ctc_beam_search_decode_chunk_begin(
|
||||
this->decoder_storage_vector[i]->root, this->ext_scorer);
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,175 @@
|
||||
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "COPYING.APACHE2.0");
|
||||
// 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.
|
||||
|
||||
#ifndef CTC_BEAM_SEARCH_DECODER_H_
|
||||
#define CTC_BEAM_SEARCH_DECODER_H_
|
||||
|
||||
#include <string>
|
||||
#include <utility>
|
||||
#include <vector>
|
||||
|
||||
#include "scorer.h"
|
||||
|
||||
/* CTC Beam Search Decoder
|
||||
|
||||
* Parameters:
|
||||
* probs_seq: 2-D vector that each element is a vector of probabilities
|
||||
* over vocabulary of one time step.
|
||||
* vocabulary: A vector of vocabulary.
|
||||
* beam_size: The width of beam search.
|
||||
* cutoff_prob: Cutoff probability for pruning.
|
||||
* cutoff_top_n: Cutoff number for pruning.
|
||||
* ext_scorer: External scorer to evaluate a prefix, which consists of
|
||||
* n-gram language model scoring and word insertion term.
|
||||
* Default null, decoding the input sample without scorer.
|
||||
* Return:
|
||||
* A vector that each element is a pair of score and decoding result,
|
||||
* in desending order.
|
||||
*/
|
||||
std::vector<std::pair<double, std::string>> ctc_beam_search_decoding(
|
||||
const std::vector<std::vector<double>> &probs_seq,
|
||||
const std::vector<std::string> &vocabulary,
|
||||
size_t beam_size,
|
||||
double cutoff_prob = 1.0,
|
||||
size_t cutoff_top_n = 40,
|
||||
Scorer *ext_scorer = nullptr,
|
||||
size_t blank_id = 0);
|
||||
|
||||
|
||||
/* CTC Beam Search Decoder for batch data
|
||||
|
||||
* Parameters:
|
||||
* probs_seq: 3-D vector that each element is a 2-D vector that can be used
|
||||
* by ctc_beam_search_decoder().
|
||||
* vocabulary: A vector of vocabulary.
|
||||
* beam_size: The width of beam search.
|
||||
* num_processes: Number of threads for beam search.
|
||||
* cutoff_prob: Cutoff probability for pruning.
|
||||
* cutoff_top_n: Cutoff number for pruning.
|
||||
* ext_scorer: External scorer to evaluate a prefix, which consists of
|
||||
* n-gram language model scoring and word insertion term.
|
||||
* Default null, decoding the input sample without scorer.
|
||||
* Return:
|
||||
* A 2-D vector that each element is a vector of beam search decoding
|
||||
* result for one audio sample.
|
||||
*/
|
||||
std::vector<std::vector<std::pair<double, std::string>>>
|
||||
ctc_beam_search_decoding_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 = 1.0,
|
||||
size_t cutoff_top_n = 40,
|
||||
Scorer *ext_scorer = nullptr,
|
||||
size_t blank_id = 0);
|
||||
|
||||
/**
|
||||
* Store the root and prefixes for decoder
|
||||
*/
|
||||
|
||||
class CtcBeamSearchDecoderStorage {
|
||||
public:
|
||||
PathTrie *root = nullptr;
|
||||
std::vector<PathTrie *> prefixes;
|
||||
|
||||
CtcBeamSearchDecoderStorage() {
|
||||
// init prefixes' root
|
||||
this->root = new PathTrie();
|
||||
this->root->log_prob_b_prev = 0.0;
|
||||
// The score of root is in log scale.Since the prob=1.0, the prob score
|
||||
// in log scale is 0.0
|
||||
this->root->score = root->log_prob_b_prev;
|
||||
// std::vector<PathTrie *> prefixes;
|
||||
this->prefixes.push_back(root);
|
||||
};
|
||||
|
||||
~CtcBeamSearchDecoderStorage() {
|
||||
if (root != nullptr) {
|
||||
delete root;
|
||||
root = nullptr;
|
||||
}
|
||||
};
|
||||
};
|
||||
|
||||
/**
|
||||
* The ctc beam search decoder, support batchsize >= 1
|
||||
*/
|
||||
class CtcBeamSearchDecoderBatch {
|
||||
public:
|
||||
CtcBeamSearchDecoderBatch(const std::vector<std::string> &vocabulary,
|
||||
size_t batch_size,
|
||||
size_t beam_size,
|
||||
size_t num_processes,
|
||||
double cutoff_prob,
|
||||
size_t cutoff_top_n,
|
||||
Scorer *ext_scorer,
|
||||
size_t blank_id);
|
||||
|
||||
~CtcBeamSearchDecoderBatch();
|
||||
void next(const std::vector<std::vector<std::vector<double>>> &probs_split,
|
||||
const std::vector<std::string> &has_value);
|
||||
|
||||
std::vector<std::vector<std::pair<double, std::string>>> decode();
|
||||
|
||||
void reset_state(size_t batch_size,
|
||||
size_t beam_size,
|
||||
size_t num_processes,
|
||||
double cutoff_prob,
|
||||
size_t cutoff_top_n);
|
||||
|
||||
private:
|
||||
std::vector<std::string> vocabulary;
|
||||
size_t batch_size;
|
||||
size_t beam_size;
|
||||
size_t num_processes;
|
||||
double cutoff_prob;
|
||||
size_t cutoff_top_n;
|
||||
Scorer *ext_scorer;
|
||||
size_t blank_id;
|
||||
std::vector<std::unique_ptr<CtcBeamSearchDecoderStorage>>
|
||||
decoder_storage_vector;
|
||||
};
|
||||
|
||||
/**
|
||||
* function for chunk decoding
|
||||
*/
|
||||
void ctc_beam_search_decode_chunk(
|
||||
PathTrie *root,
|
||||
std::vector<PathTrie *> &prefixes,
|
||||
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);
|
||||
|
||||
std::vector<std::pair<double, std::string>> get_decode_result(
|
||||
std::vector<PathTrie *> &prefixes,
|
||||
const std::vector<std::string> &vocabulary,
|
||||
size_t beam_size,
|
||||
Scorer *ext_scorer);
|
||||
|
||||
/**
|
||||
* free the CtcBeamSearchDecoderStorage
|
||||
*/
|
||||
void free_storage(std::unique_ptr<CtcBeamSearchDecoderStorage> &storage);
|
||||
|
||||
/**
|
||||
* initialize the root
|
||||
*/
|
||||
void ctc_beam_search_decode_chunk_begin(PathTrie *root, Scorer *ext_scorer);
|
||||
|
||||
#endif // CTC_BEAM_SEARCH_DECODER_H_
|
||||
@ -0,0 +1,61 @@
|
||||
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "COPYING.APACHE2.0");
|
||||
// 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_greedy_decoder.h"
|
||||
#include "decoder_utils.h"
|
||||
|
||||
std::string ctc_greedy_decoding(
|
||||
const std::vector<std::vector<double>> &probs_seq,
|
||||
const std::vector<std::string> &vocabulary,
|
||||
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(),
|
||||
"The shape of probs_seq does not match with "
|
||||
"the shape of the vocabulary");
|
||||
}
|
||||
|
||||
// size_t blank_id = vocabulary.size();
|
||||
|
||||
std::vector<size_t> max_idx_vec(num_time_steps, 0);
|
||||
std::vector<size_t> idx_vec;
|
||||
for (size_t i = 0; i < num_time_steps; ++i) {
|
||||
double max_prob = 0.0;
|
||||
size_t max_idx = 0;
|
||||
const std::vector<double> &probs_step = probs_seq[i];
|
||||
for (size_t j = 0; j < probs_step.size(); ++j) {
|
||||
if (max_prob < probs_step[j]) {
|
||||
max_idx = j;
|
||||
max_prob = probs_step[j];
|
||||
}
|
||||
}
|
||||
// id with maximum probability in current time step
|
||||
max_idx_vec[i] = max_idx;
|
||||
// deduplicate
|
||||
if ((i == 0) || ((i > 0) && max_idx_vec[i] != max_idx_vec[i - 1])) {
|
||||
idx_vec.push_back(max_idx_vec[i]);
|
||||
}
|
||||
}
|
||||
|
||||
std::string best_path_result;
|
||||
for (size_t i = 0; i < idx_vec.size(); ++i) {
|
||||
if (idx_vec[i] != blank_id) {
|
||||
std::string ch = vocabulary[idx_vec[i]];
|
||||
best_path_result += (ch == kSPACE) ? tSPACE : ch;
|
||||
}
|
||||
}
|
||||
return best_path_result;
|
||||
}
|
||||
@ -0,0 +1,35 @@
|
||||
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "COPYING.APACHE2.0");
|
||||
// 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.
|
||||
|
||||
#ifndef CTC_GREEDY_DECODER_H
|
||||
#define CTC_GREEDY_DECODER_H
|
||||
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
/* CTC Greedy (Best Path) Decoder
|
||||
*
|
||||
* Parameters:
|
||||
* probs_seq: 2-D vector that each element is a vector of probabilities
|
||||
* over vocabulary of one time step.
|
||||
* vocabulary: A vector of vocabulary.
|
||||
* Return:
|
||||
* The decoding result in string
|
||||
*/
|
||||
std::string ctc_greedy_decoding(
|
||||
const std::vector<std::vector<double>>& probs_seq,
|
||||
const std::vector<std::string>& vocabulary,
|
||||
size_t blank_id);
|
||||
|
||||
#endif // CTC_GREEDY_DECODER_H
|
||||
@ -0,0 +1,193 @@
|
||||
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "COPYING.APACHE2.0");
|
||||
// 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 "decoder_utils.h"
|
||||
|
||||
#include <algorithm>
|
||||
#include <cmath>
|
||||
#include <limits>
|
||||
|
||||
std::vector<std::pair<size_t, float>> get_pruned_log_probs(
|
||||
const std::vector<double> &prob_step,
|
||||
double cutoff_prob,
|
||||
size_t cutoff_top_n) {
|
||||
std::vector<std::pair<int, double>> prob_idx;
|
||||
for (size_t i = 0; i < prob_step.size(); ++i) {
|
||||
prob_idx.push_back(std::pair<int, double>(i, prob_step[i]));
|
||||
}
|
||||
// pruning of vocabulary
|
||||
size_t cutoff_len = prob_step.size();
|
||||
if (cutoff_prob < 1.0 || cutoff_top_n < cutoff_len) {
|
||||
std::sort(prob_idx.begin(),
|
||||
prob_idx.end(),
|
||||
pair_comp_second_rev<int, double>);
|
||||
if (cutoff_prob < 1.0) {
|
||||
double cum_prob = 0.0;
|
||||
cutoff_len = 0;
|
||||
for (size_t i = 0; i < prob_idx.size(); ++i) {
|
||||
cum_prob += prob_idx[i].second;
|
||||
cutoff_len += 1;
|
||||
if (cum_prob >= cutoff_prob || cutoff_len >= cutoff_top_n)
|
||||
break;
|
||||
}
|
||||
}
|
||||
prob_idx = std::vector<std::pair<int, double>>(
|
||||
prob_idx.begin(), prob_idx.begin() + cutoff_len);
|
||||
}
|
||||
std::vector<std::pair<size_t, float>> log_prob_idx;
|
||||
for (size_t i = 0; i < cutoff_len; ++i) {
|
||||
log_prob_idx.push_back(std::pair<int, float>(
|
||||
prob_idx[i].first, log(prob_idx[i].second + NUM_FLT_MIN)));
|
||||
}
|
||||
return log_prob_idx;
|
||||
}
|
||||
|
||||
|
||||
std::vector<std::pair<double, std::string>> get_beam_search_result(
|
||||
const std::vector<PathTrie *> &prefixes,
|
||||
const std::vector<std::string> &vocabulary,
|
||||
size_t beam_size) {
|
||||
// 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]);
|
||||
}
|
||||
}
|
||||
|
||||
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 (size_t j = 0; j < output.size(); j++) {
|
||||
std::string ch = vocabulary[output[j]];
|
||||
output_str += (ch == kSPACE) ? tSPACE : ch;
|
||||
}
|
||||
std::pair<double, std::string> output_pair(
|
||||
-space_prefixes[i]->approx_ctc, output_str);
|
||||
output_vecs.emplace_back(output_pair);
|
||||
}
|
||||
|
||||
return output_vecs;
|
||||
}
|
||||
|
||||
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;
|
||||
}
|
||||
|
||||
std::vector<std::string> split_utf8_str(const std::string &str) {
|
||||
std::vector<std::string> result;
|
||||
std::string out_str;
|
||||
|
||||
for (char c : str) {
|
||||
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);
|
||||
}
|
||||
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;
|
||||
}
|
||||
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);
|
||||
}
|
||||
} 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());
|
||||
}
|
||||
|
||||
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;
|
||||
|
||||
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);
|
||||
}
|
||||
|
||||
add_word_to_fst(int_word, dictionary);
|
||||
return true; // return with successful adding
|
||||
}
|
||||
@ -0,0 +1,111 @@
|
||||
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "COPYING.APACHE2.0");
|
||||
// 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.
|
||||
|
||||
#ifndef DECODER_UTILS_H_
|
||||
#define DECODER_UTILS_H_
|
||||
|
||||
#include <string>
|
||||
#include <utility>
|
||||
#include "fst/log.h"
|
||||
#include "path_trie.h"
|
||||
|
||||
const std::string kSPACE = "<space>";
|
||||
const std::string tSPACE = " ";
|
||||
const float NUM_FLT_INF = std::numeric_limits<float>::max();
|
||||
const float NUM_FLT_MIN = std::numeric_limits<float>::min();
|
||||
|
||||
// inline function for validation check
|
||||
inline void check(
|
||||
bool x, const char *expr, const char *file, int line, const char *err) {
|
||||
if (!x) {
|
||||
std::cout << "[" << file << ":" << line << "] ";
|
||||
LOG(FATAL) << "\"" << expr << "\" check failed. " << err;
|
||||
}
|
||||
}
|
||||
|
||||
#define VALID_CHECK(x, info) \
|
||||
check(static_cast<bool>(x), #x, __FILE__, __LINE__, info)
|
||||
#define VALID_CHECK_EQ(x, y, info) VALID_CHECK((x) == (y), info)
|
||||
#define VALID_CHECK_GT(x, y, info) VALID_CHECK((x) > (y), info)
|
||||
#define VALID_CHECK_LT(x, y, info) VALID_CHECK((x) < (y), info)
|
||||
|
||||
|
||||
// Function template for comparing two pairs
|
||||
template <typename T1, typename T2>
|
||||
bool pair_comp_first_rev(const std::pair<T1, T2> &a,
|
||||
const std::pair<T1, T2> &b) {
|
||||
return a.first > b.first;
|
||||
}
|
||||
|
||||
// Function template for comparing two pairs
|
||||
template <typename T1, typename T2>
|
||||
bool pair_comp_second_rev(const std::pair<T1, T2> &a,
|
||||
const std::pair<T1, T2> &b) {
|
||||
return a.second > b.second;
|
||||
}
|
||||
|
||||
// Return the sum of two probabilities in log scale
|
||||
template <typename T>
|
||||
T log_sum_exp(const T &x, const T &y) {
|
||||
static T num_min = -std::numeric_limits<T>::max();
|
||||
if (x <= num_min) return y;
|
||||
if (y <= num_min) return x;
|
||||
T xmax = std::max(x, y);
|
||||
return std::log(std::exp(x - xmax) + std::exp(y - xmax)) + xmax;
|
||||
}
|
||||
|
||||
// Get pruned probability vector for each time step's beam search
|
||||
std::vector<std::pair<size_t, float>> get_pruned_log_probs(
|
||||
const std::vector<double> &prob_step,
|
||||
double cutoff_prob,
|
||||
size_t cutoff_top_n);
|
||||
|
||||
// Get beam search result from prefixes in trie tree
|
||||
std::vector<std::pair<double, std::string>> get_beam_search_result(
|
||||
const std::vector<PathTrie *> &prefixes,
|
||||
const std::vector<std::string> &vocabulary,
|
||||
size_t beam_size);
|
||||
|
||||
// Functor for prefix comparsion
|
||||
bool prefix_compare(const PathTrie *x, const PathTrie *y);
|
||||
|
||||
/* Get length of utf8 encoding string
|
||||
* See: http://stackoverflow.com/a/4063229
|
||||
*/
|
||||
size_t get_utf8_str_len(const std::string &str);
|
||||
|
||||
/* Split a string into a list of strings on a given string
|
||||
* delimiter. NB: delimiters on beginning / end of string are
|
||||
* trimmed. Eg, "FooBarFoo" split on "Foo" returns ["Bar"].
|
||||
*/
|
||||
std::vector<std::string> split_str(const std::string &s,
|
||||
const std::string &delim);
|
||||
|
||||
/* Splits string into vector of strings representing
|
||||
* UTF-8 characters (not same as chars)
|
||||
*/
|
||||
std::vector<std::string> split_utf8_str(const std::string &str);
|
||||
|
||||
// Add a word in index to the dicionary of fst
|
||||
void add_word_to_fst(const std::vector<int> &word,
|
||||
fst::StdVectorFst *dictionary);
|
||||
|
||||
// Add a word in string to dictionary
|
||||
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);
|
||||
#endif // DECODER_UTILS_H
|
||||
@ -0,0 +1,33 @@
|
||||
%module paddlespeech_ctcdecoders
|
||||
%{
|
||||
#include "scorer.h"
|
||||
#include "ctc_greedy_decoder.h"
|
||||
#include "ctc_beam_search_decoder.h"
|
||||
#include "decoder_utils.h"
|
||||
%}
|
||||
|
||||
%include "std_vector.i"
|
||||
%include "std_pair.i"
|
||||
%include "std_string.i"
|
||||
%import "decoder_utils.h"
|
||||
|
||||
namespace std {
|
||||
%template(DoubleVector) std::vector<double>;
|
||||
%template(IntVector) std::vector<int>;
|
||||
%template(StringVector) std::vector<std::string>;
|
||||
%template(VectorOfStructVector) std::vector<std::vector<double> >;
|
||||
%template(FloatVector) std::vector<float>;
|
||||
%template(Pair) std::pair<float, std::string>;
|
||||
%template(PairFloatStringVector) std::vector<std::pair<float, std::string> >;
|
||||
%template(PairDoubleStringVector) std::vector<std::pair<double, std::string> >;
|
||||
%template(PairDoubleStringVector2) std::vector<std::vector<std::pair<double, std::string> > >;
|
||||
%template(DoubleVector3) std::vector<std::vector<std::vector<double> > >;
|
||||
}
|
||||
|
||||
%template(IntDoublePairCompSecondRev) pair_comp_second_rev<int, double>;
|
||||
%template(StringDoublePairCompSecondRev) pair_comp_second_rev<std::string, double>;
|
||||
%template(DoubleStringPairCompFirstRev) pair_comp_first_rev<double, std::string>;
|
||||
|
||||
%include "scorer.h"
|
||||
%include "ctc_greedy_decoder.h"
|
||||
%include "ctc_beam_search_decoder.h"
|
||||
@ -0,0 +1,164 @@
|
||||
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "COPYING.APACHE2.0");
|
||||
// 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 "path_trie.h"
|
||||
|
||||
#include <algorithm>
|
||||
#include <limits>
|
||||
#include <memory>
|
||||
#include <utility>
|
||||
#include <vector>
|
||||
|
||||
#include "decoder_utils.h"
|
||||
|
||||
PathTrie::PathTrie() {
|
||||
log_prob_b_prev = -NUM_FLT_INF;
|
||||
log_prob_nb_prev = -NUM_FLT_INF;
|
||||
log_prob_b_cur = -NUM_FLT_INF;
|
||||
log_prob_nb_cur = -NUM_FLT_INF;
|
||||
score = -NUM_FLT_INF;
|
||||
|
||||
ROOT_ = -1;
|
||||
character = ROOT_;
|
||||
exists_ = true;
|
||||
parent = nullptr;
|
||||
|
||||
dictionary_ = nullptr;
|
||||
dictionary_state_ = 0;
|
||||
has_dictionary_ = false;
|
||||
|
||||
matcher_ = nullptr;
|
||||
}
|
||||
|
||||
PathTrie::~PathTrie() {
|
||||
for (auto child : children_) {
|
||||
delete child.second;
|
||||
child.second = nullptr;
|
||||
}
|
||||
}
|
||||
|
||||
PathTrie* PathTrie::get_path_trie(int new_char, bool reset) {
|
||||
auto child = children_.begin();
|
||||
for (child = children_.begin(); child != children_.end(); ++child) {
|
||||
if (child->first == new_char) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (child != children_.end()) {
|
||||
if (!child->second->exists_) {
|
||||
child->second->exists_ = true;
|
||||
child->second->log_prob_b_prev = -NUM_FLT_INF;
|
||||
child->second->log_prob_nb_prev = -NUM_FLT_INF;
|
||||
child->second->log_prob_b_cur = -NUM_FLT_INF;
|
||||
child->second->log_prob_nb_cur = -NUM_FLT_INF;
|
||||
}
|
||||
return (child->second);
|
||||
} else {
|
||||
if (has_dictionary_) {
|
||||
matcher_->SetState(dictionary_state_);
|
||||
bool found = matcher_->Find(new_char + 1);
|
||||
if (!found) {
|
||||
// Adding this character causes word outside dictionary
|
||||
auto FSTZERO = fst::TropicalWeight::Zero();
|
||||
auto final_weight = dictionary_->Final(dictionary_state_);
|
||||
bool is_final = (final_weight != FSTZERO);
|
||||
if (is_final && reset) {
|
||||
dictionary_state_ = dictionary_->Start();
|
||||
}
|
||||
return nullptr;
|
||||
} else {
|
||||
PathTrie* new_path = new PathTrie;
|
||||
new_path->character = new_char;
|
||||
new_path->parent = this;
|
||||
new_path->dictionary_ = dictionary_;
|
||||
new_path->dictionary_state_ = matcher_->Value().nextstate;
|
||||
new_path->has_dictionary_ = true;
|
||||
new_path->matcher_ = matcher_;
|
||||
children_.push_back(std::make_pair(new_char, new_path));
|
||||
return new_path;
|
||||
}
|
||||
} else {
|
||||
PathTrie* new_path = new PathTrie;
|
||||
new_path->character = new_char;
|
||||
new_path->parent = this;
|
||||
children_.push_back(std::make_pair(new_char, new_path));
|
||||
return new_path;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
PathTrie* PathTrie::get_path_vec(std::vector<int>& output) {
|
||||
return get_path_vec(output, ROOT_);
|
||||
}
|
||||
|
||||
PathTrie* PathTrie::get_path_vec(std::vector<int>& output,
|
||||
int stop,
|
||||
size_t max_steps) {
|
||||
if (character == stop || character == ROOT_ || output.size() == max_steps) {
|
||||
std::reverse(output.begin(), output.end());
|
||||
return this;
|
||||
} else {
|
||||
output.push_back(character);
|
||||
return parent->get_path_vec(output, stop, max_steps);
|
||||
}
|
||||
}
|
||||
|
||||
void PathTrie::iterate_to_vec(std::vector<PathTrie*>& output) {
|
||||
if (exists_) {
|
||||
log_prob_b_prev = log_prob_b_cur;
|
||||
log_prob_nb_prev = log_prob_nb_cur;
|
||||
|
||||
log_prob_b_cur = -NUM_FLT_INF;
|
||||
log_prob_nb_cur = -NUM_FLT_INF;
|
||||
|
||||
score = log_sum_exp(log_prob_b_prev, log_prob_nb_prev);
|
||||
output.push_back(this);
|
||||
}
|
||||
for (auto child : children_) {
|
||||
child.second->iterate_to_vec(output);
|
||||
}
|
||||
}
|
||||
|
||||
void PathTrie::remove() {
|
||||
exists_ = false;
|
||||
if (children_.size() == 0) {
|
||||
if (parent != nullptr) {
|
||||
auto child = parent->children_.begin();
|
||||
for (child = parent->children_.begin();
|
||||
child != parent->children_.end();
|
||||
++child) {
|
||||
if (child->first == character) {
|
||||
parent->children_.erase(child);
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (parent->children_.size() == 0 && !parent->exists_) {
|
||||
parent->remove();
|
||||
}
|
||||
}
|
||||
delete this;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void PathTrie::set_dictionary(fst::StdVectorFst* dictionary) {
|
||||
dictionary_ = dictionary;
|
||||
dictionary_state_ = dictionary->Start();
|
||||
has_dictionary_ = true;
|
||||
}
|
||||
|
||||
using FSTMATCH = fst::SortedMatcher<fst::StdVectorFst>;
|
||||
void PathTrie::set_matcher(std::shared_ptr<FSTMATCH> matcher) {
|
||||
matcher_ = matcher;
|
||||
}
|
||||
@ -0,0 +1,82 @@
|
||||
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "COPYING.APACHE2.0");
|
||||
// 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.
|
||||
|
||||
#ifndef PATH_TRIE_H
|
||||
#define PATH_TRIE_H
|
||||
|
||||
#include <algorithm>
|
||||
#include <limits>
|
||||
#include <memory>
|
||||
#include <utility>
|
||||
#include <vector>
|
||||
|
||||
#include "fst/fstlib.h"
|
||||
|
||||
/* Trie tree for prefix storing and manipulating, with a dictionary in
|
||||
* finite-state transducer for spelling correction.
|
||||
*/
|
||||
class PathTrie {
|
||||
public:
|
||||
PathTrie();
|
||||
~PathTrie();
|
||||
|
||||
// get new prefix after appending new char
|
||||
PathTrie* get_path_trie(int new_char, bool reset = true);
|
||||
|
||||
// get the prefix in index from root to current node
|
||||
PathTrie* get_path_vec(std::vector<int>& output);
|
||||
|
||||
// get the prefix in index from some stop node to current nodel
|
||||
PathTrie* get_path_vec(
|
||||
std::vector<int>& output,
|
||||
int stop,
|
||||
size_t max_steps = std::numeric_limits<size_t>::max());
|
||||
|
||||
// update log probs
|
||||
void iterate_to_vec(std::vector<PathTrie*>& output);
|
||||
|
||||
// set dictionary for FST
|
||||
void set_dictionary(fst::StdVectorFst* dictionary);
|
||||
|
||||
void set_matcher(std::shared_ptr<fst::SortedMatcher<fst::StdVectorFst>>);
|
||||
|
||||
bool is_empty() { return ROOT_ == character; }
|
||||
|
||||
// remove current path from root
|
||||
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;
|
||||
|
||||
private:
|
||||
int ROOT_;
|
||||
bool exists_;
|
||||
bool has_dictionary_;
|
||||
|
||||
std::vector<std::pair<int, PathTrie*>> children_;
|
||||
|
||||
// pointer to dictionary of FST
|
||||
fst::StdVectorFst* dictionary_;
|
||||
fst::StdVectorFst::StateId dictionary_state_;
|
||||
// true if finding ars in FST
|
||||
std::shared_ptr<fst::SortedMatcher<fst::StdVectorFst>> matcher_;
|
||||
};
|
||||
|
||||
#endif // PATH_TRIE_H
|
||||
@ -0,0 +1,232 @@
|
||||
// Licensed under GNU Lesser General Public License v3 (LGPLv3) (LGPL-3) (the
|
||||
// "COPYING.LESSER.3");
|
||||
|
||||
#include "scorer.h"
|
||||
|
||||
#include <unistd.h>
|
||||
#include <iostream>
|
||||
|
||||
#include "lm/config.hh"
|
||||
#include "lm/model.hh"
|
||||
#include "lm/state.hh"
|
||||
|
||||
#include "decoder_utils.h"
|
||||
|
||||
using namespace lm::ngram;
|
||||
// if your platform is windows ,you need add the define
|
||||
#define F_OK 0
|
||||
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] == kSPACE) {
|
||||
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 deterministic, 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;
|
||||
}
|
||||
@ -0,0 +1,114 @@
|
||||
// Licensed under GNU Lesser General Public License v3 (LGPLv3) (LGPL-3) (the
|
||||
// "COPYING.LESSER.3");
|
||||
|
||||
#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 "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_
|
||||
@ -0,0 +1,138 @@
|
||||
# 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.
|
||||
"""Script to build and install decoder package."""
|
||||
import argparse
|
||||
import glob
|
||||
import multiprocessing.pool
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
|
||||
from setuptools import distutils
|
||||
from setuptools import Extension
|
||||
from setuptools import setup
|
||||
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument(
|
||||
"--num_processes",
|
||||
default=1,
|
||||
type=int,
|
||||
help="Number of cpu processes to build package. (default: %(default)d)")
|
||||
args = parser.parse_known_args()
|
||||
|
||||
# reconstruct sys.argv to pass to setup below
|
||||
sys.argv = [sys.argv[0]] + args[1]
|
||||
|
||||
|
||||
# monkey-patch for parallel compilation
|
||||
# See: https://stackoverflow.com/a/13176803
|
||||
def parallelCCompile(self,
|
||||
sources,
|
||||
output_dir=None,
|
||||
macros=None,
|
||||
include_dirs=None,
|
||||
debug=0,
|
||||
extra_preargs=None,
|
||||
extra_postargs=None,
|
||||
depends=None):
|
||||
# those lines are copied from distutils.ccompiler.CCompiler directly
|
||||
macros, objects, extra_postargs, pp_opts, build = self._setup_compile(
|
||||
output_dir, macros, include_dirs, sources, depends, extra_postargs)
|
||||
cc_args = self._get_cc_args(pp_opts, debug, extra_preargs)
|
||||
|
||||
# parallel code
|
||||
def _single_compile(obj):
|
||||
try:
|
||||
src, ext = build[obj]
|
||||
except KeyError:
|
||||
return
|
||||
self._compile(obj, src, ext, cc_args, extra_postargs, pp_opts)
|
||||
|
||||
# convert to list, imap is evaluated on-demand
|
||||
thread_pool = multiprocessing.pool.ThreadPool(args[0].num_processes)
|
||||
list(thread_pool.imap(_single_compile, objects))
|
||||
return objects
|
||||
|
||||
|
||||
def compile_test(header, library):
|
||||
dummy_path = os.path.join(os.path.dirname(__file__), "dummy")
|
||||
command = "bash -c \"g++ -include " + header \
|
||||
+ " -l" + library + " -x c++ - <<<'int main() {}' -o " \
|
||||
+ dummy_path + " >/dev/null 2>/dev/null && rm " \
|
||||
+ dummy_path + " 2>/dev/null\""
|
||||
return os.system(command) == 0
|
||||
|
||||
|
||||
# hack compile to support parallel compiling
|
||||
distutils.ccompiler.CCompiler.compile = parallelCCompile
|
||||
|
||||
FILES = glob.glob('kenlm/util/*.cc') \
|
||||
+ glob.glob('kenlm/lm/*.cc') \
|
||||
+ glob.glob('kenlm/util/double-conversion/*.cc')
|
||||
|
||||
FILES += glob.glob('openfst-1.6.3/src/lib/*.cc')
|
||||
|
||||
# yapf: disable
|
||||
FILES = [
|
||||
fn for fn in FILES if not (fn.endswith('main.cc') or fn.endswith('test.cc')
|
||||
or fn.endswith('unittest.cc'))
|
||||
]
|
||||
# yapf: enable
|
||||
LIBS = ['stdc++']
|
||||
if platform.system() != 'Darwin':
|
||||
LIBS.append('rt')
|
||||
if platform.system() == 'Windows':
|
||||
LIBS = ['-static-libstdc++']
|
||||
|
||||
ARGS = ['-O3', '-DNDEBUG', '-DKENLM_MAX_ORDER=6', '-std=c++11']
|
||||
|
||||
if compile_test('zlib.h', 'z'):
|
||||
ARGS.append('-DHAVE_ZLIB')
|
||||
LIBS.append('z')
|
||||
|
||||
if compile_test('bzlib.h', 'bz2'):
|
||||
ARGS.append('-DHAVE_BZLIB')
|
||||
LIBS.append('bz2')
|
||||
|
||||
if compile_test('lzma.h', 'lzma'):
|
||||
ARGS.append('-DHAVE_XZLIB')
|
||||
LIBS.append('lzma')
|
||||
|
||||
os.system('swig -python -c++ ./decoders.i')
|
||||
|
||||
decoders_module = [
|
||||
Extension(
|
||||
name='_paddlespeech_ctcdecoders',
|
||||
sources=FILES + glob.glob('*.cxx') + glob.glob('*.cpp'),
|
||||
language='c++',
|
||||
include_dirs=[
|
||||
'.',
|
||||
'kenlm',
|
||||
'openfst-1.6.3/src/include',
|
||||
'ThreadPool',
|
||||
],
|
||||
libraries=LIBS,
|
||||
extra_compile_args=ARGS)
|
||||
]
|
||||
|
||||
setup(
|
||||
name='paddlespeech_ctcdecoders',
|
||||
version='0.2.0',
|
||||
description="CTC decoders in paddlespeech",
|
||||
author="PaddlePaddle Speech and Language Team",
|
||||
author_email="paddlesl@baidu.com",
|
||||
url="https://github.com/PaddlePaddle/PaddleSpeech",
|
||||
license='Apache 2.0, GNU Lesser General Public License v3 (LGPLv3) (LGPL-3)',
|
||||
ext_modules=decoders_module,
|
||||
py_modules=['paddlespeech_ctcdecoders'])
|
||||
@ -0,0 +1,24 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
if [ ! -d kenlm ]; then
|
||||
git clone https://github.com/kpu/kenlm.git
|
||||
cd kenlm/
|
||||
git checkout df2d717e95183f79a90b2fa6e4307083a351ca6a
|
||||
cd ..
|
||||
echo -e "\n"
|
||||
fi
|
||||
|
||||
if [ ! -d openfst-1.6.3 ]; then
|
||||
echo "Download and extract openfst ..."
|
||||
wget http://www.openfst.org/twiki/pub/FST/FstDownload/openfst-1.6.3.tar.gz --no-check-certificate
|
||||
tar -xzvf openfst-1.6.3.tar.gz
|
||||
echo -e "\n"
|
||||
fi
|
||||
|
||||
if [ ! -d ThreadPool ]; then
|
||||
git clone https://github.com/progschj/ThreadPool.git
|
||||
echo -e "\n"
|
||||
fi
|
||||
|
||||
echo "Install decoders ..."
|
||||
python3 setup.py install --num_processes 4
|
||||
@ -0,0 +1,16 @@
|
||||
include_directories(
|
||||
${CMAKE_CURRENT_SOURCE_DIR}
|
||||
${CMAKE_CURRENT_SOURCE_DIR}/base
|
||||
)
|
||||
|
||||
include_directories(
|
||||
${CMAKE_CURRENT_SOURCE_DIR}/../
|
||||
${CMAKE_CURRENT_SOURCE_DIR}/utils
|
||||
)
|
||||
add_subdirectory(utils)
|
||||
|
||||
include_directories(
|
||||
${CMAKE_CURRENT_SOURCE_DIR}
|
||||
${CMAKE_CURRENT_SOURCE_DIR}/frontend
|
||||
)
|
||||
add_subdirectory(frontend)
|
||||
Some files were not shown because too many files have changed in this diff Show More
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Reference in new issue