decode-faster.cc
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// bin/decode-faster.cc
// Copyright 2009-2011 Microsoft Corporation
// 2013 Johns Hopkins University (author: Daniel Povey)
// See ../../COPYING for clarification regarding multiple authors
//
// 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
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "tree/context-dep.h"
#include "hmm/transition-model.h"
#include "fstext/fstext-lib.h"
#include "decoder/faster-decoder.h"
#include "decoder/decodable-matrix.h"
#include "base/timer.h"
#include "lat/kaldi-lattice.h" // for {Compact}LatticeArc
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
typedef kaldi::int32 int32;
using fst::SymbolTable;
using fst::VectorFst;
using fst::StdArc;
const char *usage =
"Decode, reading log-likelihoods (of transition-ids or whatever symbol is on the graph)\n"
"as matrices. Note: you'll usually want decode-faster-mapped rather than this program.\n"
"\n"
"Usage: decode-faster [options] <fst-in> <loglikes-rspecifier> <words-wspecifier> [<alignments-wspecifier>]\n";
ParseOptions po(usage);
bool binary = true;
BaseFloat acoustic_scale = 0.1;
bool allow_partial = true;
std::string word_syms_filename;
FasterDecoderOptions decoder_opts;
decoder_opts.Register(&po, true); // true == include obscure settings.
po.Register("binary", &binary, "Write output in binary mode");
po.Register("allow-partial", &allow_partial, "Produce output even when final state was not reached");
po.Register("acoustic-scale", &acoustic_scale, "Scaling factor for acoustic likelihoods");
po.Register("word-symbol-table", &word_syms_filename, "Symbol table for words [for debug output]");
po.Read(argc, argv);
if (po.NumArgs() < 3 || po.NumArgs() > 4) {
po.PrintUsage();
exit(1);
}
std::string fst_in_filename = po.GetArg(1),
loglikes_rspecifier = po.GetArg(2),
words_wspecifier = po.GetArg(3),
alignment_wspecifier = po.GetOptArg(4);
Int32VectorWriter words_writer(words_wspecifier);
Int32VectorWriter alignment_writer(alignment_wspecifier);
fst::SymbolTable *word_syms = NULL;
if (word_syms_filename != "") {
word_syms = fst::SymbolTable::ReadText(word_syms_filename);
if (!word_syms)
KALDI_ERR << "Could not read symbol table from file "<<word_syms_filename;
}
SequentialBaseFloatMatrixReader loglikes_reader(loglikes_rspecifier);
// It's important that we initialize decode_fst after loglikes_reader, as it
// can prevent crashes on systems installed without enough virtual memory.
// It has to do with what happens on UNIX systems if you call fork() on a
// large process: the page-table entries are duplicated, which requires a
// lot of virtual memory.
VectorFst<StdArc> *decode_fst = fst::ReadFstKaldi(fst_in_filename);
BaseFloat tot_like = 0.0;
kaldi::int64 frame_count = 0;
int num_success = 0, num_fail = 0;
FasterDecoder decoder(*decode_fst, decoder_opts);
Timer timer;
for (; !loglikes_reader.Done(); loglikes_reader.Next()) {
std::string key = loglikes_reader.Key();
const Matrix<BaseFloat> &loglikes (loglikes_reader.Value());
if (loglikes.NumRows() == 0) {
KALDI_WARN << "Zero-length utterance: " << key;
num_fail++;
continue;
}
DecodableMatrixScaled decodable(loglikes, acoustic_scale);
decoder.Decode(&decodable);
VectorFst<LatticeArc> decoded; // linear FST.
if ( (allow_partial || decoder.ReachedFinal())
&& decoder.GetBestPath(&decoded) ) {
num_success++;
if (!decoder.ReachedFinal())
KALDI_WARN << "Decoder did not reach end-state, outputting partial traceback.";
std::vector<int32> alignment;
std::vector<int32> words;
LatticeWeight weight;
frame_count += loglikes.NumRows();
GetLinearSymbolSequence(decoded, &alignment, &words, &weight);
words_writer.Write(key, words);
if (alignment_writer.IsOpen())
alignment_writer.Write(key, alignment);
if (word_syms != NULL) {
std::cerr << key << ' ';
for (size_t i = 0; i < words.size(); i++) {
std::string s = word_syms->Find(words[i]);
if (s == "")
KALDI_ERR << "Word-id " << words[i] <<" not in symbol table.";
std::cerr << s << ' ';
}
std::cerr << '\n';
}
BaseFloat like = -weight.Value1() -weight.Value2();
tot_like += like;
KALDI_LOG << "Log-like per frame for utterance " << key << " is "
<< (like / loglikes.NumRows()) << " over "
<< loglikes.NumRows() << " frames.";
} else {
num_fail++;
KALDI_WARN << "Did not successfully decode utterance " << key
<< ", len = " << loglikes.NumRows();
}
}
double elapsed = timer.Elapsed();
KALDI_LOG << "Time taken [excluding initialization] "<< elapsed
<< "s: real-time factor assuming 100 frames/sec is "
<< (elapsed*100.0/frame_count);
KALDI_LOG << "Done " << num_success << " utterances, failed for "
<< num_fail;
KALDI_LOG << "Overall log-likelihood per frame is " << (tot_like/frame_count)
<< " over " << frame_count << " frames.";
delete word_syms;
delete decode_fst;
if (num_success != 0) return 0;
else return 1;
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}