STT/native_client/ctcdecode/scorer.cpp
Reuben Morais cc62aa2eb8
Merge pull request #3279 from godefv/decoder_timesteps
The CTC decoder timesteps now corresponds to the timesteps of the most probable CTC path, instead of the earliest timesteps of all possible paths.
2020-09-17 20:31:05 +02:00

350 lines
10 KiB
C++

#ifdef _MSC_VER
#include <stdlib.h>
#include <io.h>
#include <windows.h>
#define R_OK 4 /* Read permission. */
#define W_OK 2 /* Write permission. */
#define F_OK 0 /* Existence. */
#define access _access
#else /* _MSC_VER */
#include <unistd.h>
#endif
#include "scorer.h"
#include <iostream>
#include <fstream>
#include "lm/config.hh"
#include "lm/model.hh"
#include "lm/state.hh"
#include "util/string_piece.hh"
#include "decoder_utils.h"
static const int32_t MAGIC = 'TRIE';
static const int32_t FILE_VERSION = 6;
int
Scorer::init(const std::string& lm_path,
const Alphabet& alphabet)
{
set_alphabet(alphabet);
return load_lm(lm_path);
}
int
Scorer::init(const std::string& lm_path,
const std::string& alphabet_config_path)
{
int err = alphabet_.init(alphabet_config_path.c_str());
if (err != 0) {
return err;
}
setup_char_map();
return load_lm(lm_path);
}
void
Scorer::set_alphabet(const Alphabet& alphabet)
{
alphabet_ = alphabet;
setup_char_map();
}
void Scorer::setup_char_map()
{
// (Re-)Initialize character map
char_map_.clear();
SPACE_ID_ = alphabet_.GetSpaceLabel();
for (int i = 0; i < alphabet_.GetSize(); 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_[alphabet_.DecodeSingle(i)] = i + 1;
}
}
int Scorer::load_lm(const std::string& lm_path)
{
// Check if file is readable to avoid KenLM throwing an exception
const char* filename = lm_path.c_str();
if (access(filename, R_OK) != 0) {
return DS_ERR_SCORER_UNREADABLE;
}
// Check if the file format is valid to avoid KenLM throwing an exception
lm::ngram::ModelType model_type;
if (!lm::ngram::RecognizeBinary(filename, model_type)) {
return DS_ERR_SCORER_INVALID_LM;
}
// Load the LM
lm::ngram::Config config;
config.load_method = util::LoadMethod::LAZY;
language_model_.reset(lm::ngram::LoadVirtual(filename, config));
max_order_ = language_model_->Order();
uint64_t package_size;
{
util::scoped_fd fd(util::OpenReadOrThrow(filename));
package_size = util::SizeFile(fd.get());
}
uint64_t trie_offset = language_model_->GetEndOfSearchOffset();
if (package_size <= trie_offset) {
// File ends without a trie structure
return DS_ERR_SCORER_NO_TRIE;
}
// Read metadata and trie from file
std::ifstream fin(lm_path, std::ios::binary);
fin.seekg(trie_offset);
return load_trie(fin, lm_path);
}
int Scorer::load_trie(std::ifstream& fin, const std::string& file_path)
{
int magic;
fin.read(reinterpret_cast<char*>(&magic), sizeof(magic));
if (magic != MAGIC) {
std::cerr << "Error: Can't parse scorer file, invalid header. Try updating "
"your scorer file." << std::endl;
return DS_ERR_SCORER_INVALID_TRIE;
}
int version;
fin.read(reinterpret_cast<char*>(&version), sizeof(version));
if (version != FILE_VERSION) {
std::cerr << "Error: Scorer file version mismatch (" << version
<< " instead of expected " << FILE_VERSION
<< "). ";
if (version < FILE_VERSION) {
std::cerr << "Update your scorer file.";
} else {
std::cerr << "Downgrade your scorer file or update your version of DeepSpeech.";
}
std::cerr << std::endl;
return DS_ERR_SCORER_VERSION_MISMATCH;
}
fin.read(reinterpret_cast<char*>(&is_utf8_mode_), sizeof(is_utf8_mode_));
// Read hyperparameters from header
double alpha, beta;
fin.read(reinterpret_cast<char*>(&alpha), sizeof(alpha));
fin.read(reinterpret_cast<char*>(&beta), sizeof(beta));
reset_params(alpha, beta);
fst::FstReadOptions opt;
opt.mode = fst::FstReadOptions::MAP;
opt.source = file_path;
dictionary.reset(FstType::Read(fin, opt));
return DS_ERR_OK;
}
bool Scorer::save_dictionary(const std::string& path, bool append_instead_of_overwrite)
{
std::ios::openmode om;
if (append_instead_of_overwrite) {
om = std::ios::in|std::ios::out|std::ios::binary|std::ios::ate;
} else {
om = std::ios::out|std::ios::binary;
}
std::fstream fout(path, om);
if (!fout ||fout.bad()) {
std::cerr << "Error opening '" << path << "'" << std::endl;
return false;
}
fout.write(reinterpret_cast<const char*>(&MAGIC), sizeof(MAGIC));
if (fout.bad()) {
std::cerr << "Error writing MAGIC '" << path << "'" << std::endl;
return false;
}
fout.write(reinterpret_cast<const char*>(&FILE_VERSION), sizeof(FILE_VERSION));
if (fout.bad()) {
std::cerr << "Error writing FILE_VERSION '" << path << "'" << std::endl;
return false;
}
fout.write(reinterpret_cast<const char*>(&is_utf8_mode_), sizeof(is_utf8_mode_));
if (fout.bad()) {
std::cerr << "Error writing is_utf8_mode '" << path << "'" << std::endl;
return false;
}
fout.write(reinterpret_cast<const char*>(&alpha), sizeof(alpha));
if (fout.bad()) {
std::cerr << "Error writing alpha '" << path << "'" << std::endl;
return false;
}
fout.write(reinterpret_cast<const char*>(&beta), sizeof(beta));
if (fout.bad()) {
std::cerr << "Error writing beta '" << path << "'" << std::endl;
return false;
}
fst::FstWriteOptions opt;
opt.align = true;
opt.source = path;
return dictionary->Write(fout, opt);
}
bool Scorer::is_scoring_boundary(PathTrie* prefix, size_t new_label)
{
if (is_utf8_mode()) {
if (prefix->character == -1) {
return false;
}
unsigned char first_byte;
int distance_to_boundary = prefix->distance_to_codepoint_boundary(&first_byte, alphabet_);
int needed_bytes;
if ((first_byte >> 3) == 0x1E) {
needed_bytes = 4;
} else if ((first_byte >> 4) == 0x0E) {
needed_bytes = 3;
} else if ((first_byte >> 5) == 0x06) {
needed_bytes = 2;
} else if ((first_byte >> 7) == 0x00) {
needed_bytes = 1;
} else {
assert(false); // invalid byte sequence. should be unreachable, disallowed by vocabulary/trie
return false;
}
return distance_to_boundary == needed_bytes;
} else {
return new_label == SPACE_ID_;
}
}
double Scorer::get_log_cond_prob(const std::vector<std::string>& words,
bool bos,
bool eos)
{
return get_log_cond_prob(words.begin(), words.end(), bos, eos);
}
double Scorer::get_log_cond_prob(const std::vector<std::string>::const_iterator& begin,
const std::vector<std::string>::const_iterator& end,
bool bos,
bool eos)
{
const auto& vocab = language_model_->BaseVocabulary();
lm::ngram::State state_vec[2];
lm::ngram::State *in_state = &state_vec[0];
lm::ngram::State *out_state = &state_vec[1];
if (bos) {
language_model_->BeginSentenceWrite(in_state);
} else {
language_model_->NullContextWrite(in_state);
}
double cond_prob = 0.0;
for (auto it = begin; it != end; ++it) {
lm::WordIndex word_index = vocab.Index(*it);
// encounter OOV
if (word_index == lm::kUNK) {
return OOV_SCORE;
}
cond_prob = language_model_->BaseScore(in_state, word_index, out_state);
std::swap(in_state, out_state);
}
if (eos) {
cond_prob = language_model_->BaseScore(in_state, vocab.EndSentence(), out_state);
}
// return loge prob
return cond_prob/NUM_FLT_LOGE;
}
void Scorer::reset_params(float alpha, float beta)
{
this->alpha = alpha;
this->beta = beta;
}
std::vector<std::string> Scorer::split_labels_into_scored_units(const std::vector<unsigned int>& labels)
{
if (labels.empty()) return {};
std::string s = alphabet_.Decode(labels);
std::vector<std::string> words;
if (is_utf8_mode_) {
words = split_into_codepoints(s);
} else {
words = split_str(s, " ");
}
return words;
}
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++) {
if (!current_node || current_node->character == -1) {
break;
}
std::vector<unsigned int> prefix_vec;
if (is_utf8_mode_) {
new_node = current_node->get_prev_grapheme(prefix_vec, alphabet_);
} else {
new_node = current_node->get_prev_word(prefix_vec, alphabet_);
}
current_node = new_node->parent;
// reconstruct word
std::string word = alphabet_.Decode(prefix_vec);
ngram.push_back(word);
}
std::reverse(ngram.begin(), ngram.end());
return ngram;
}
void Scorer::fill_dictionary(const std::unordered_set<std::string>& vocabulary)
{
// ConstFst is immutable, so we need to use a MutableFst to create the trie,
// and then we convert to a ConstFst for the decoder and for storing on disk.
fst::StdVectorFst dictionary;
// For each unigram convert to ints and put in trie
for (const auto& word : vocabulary) {
if (word != START_TOKEN && word != UNK_TOKEN && word != END_TOKEN) {
add_word_to_dictionary(word, char_map_, is_utf8_mode_, SPACE_ID_ + 1, &dictionary);
}
}
/* 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);
std::unique_ptr<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.get());
/* Finds the simplest equivalent fst. This is unnecessary but decreases
* memory usage of the dictionary
*/
fst::Minimize(new_dict.get());
// Now we convert the MutableFst to a ConstFst (Scorer::FstType) via its ctor
std::unique_ptr<FstType> converted(new FstType(*new_dict));
this->dictionary = std::move(converted);
}