// nnet3bin/nnet3-latgen-faster-looped.cc

// Copyright 2012-2016 Johns Hopkins University (author: Daniel Povey)

// 2014 Guoguo Chen

// 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/decoder-wrappers.h"

#include "nnet3/decodable-simple-looped.h"

#include "nnet3/nnet-utils.h"

#include "base/timer.h"

int main(int argc, char *argv[]) {

// note: making this program work with GPUs is as simple as initializing the

// device, but it probably won't make a huge difference in speed for typical

// setups.

try {

using namespace kaldi;

using namespace kaldi::nnet3;

typedef kaldi::int32 int32;

using fst::SymbolTable;

using fst::Fst;

using fst::StdArc;

const char *usage =

"Generate lattices using nnet3 neural net model.\n"

"[this version uses the 'looped' computation, which may be slightly faster for\n"

"many architectures, but should not be used for backwards-recurrent architectures\n"

"such as BLSTMs.\n"

"Usage: nnet3-latgen-faster-looped [options] <nnet-in> <fst-in|fsts-rspecifier> <features-rspecifier>"

" <lattice-wspecifier> [ <words-wspecifier> [<alignments-wspecifier>] ]\n";

ParseOptions po(usage);

Timer timer;

bool allow_partial = false;

LatticeFasterDecoderConfig config;

NnetSimpleLoopedComputationOptions decodable_opts;

std::string word_syms_filename;

std::string ivector_rspecifier,

online_ivector_rspecifier,

utt2spk_rspecifier;

int32 online_ivector_period = 0;

config.Register(&po);

decodable_opts.Register(&po);

po.Register("word-symbol-table", &word_syms_filename,

"Symbol table for words [for debug output]");

po.Register("allow-partial", &allow_partial,

"If true, produce output even if end state was not reached.");

po.Register("ivectors", &ivector_rspecifier, "Rspecifier for "

"iVectors as vectors (i.e. not estimated online); per utterance "

"by default, or per speaker if you provide the --utt2spk option.");

po.Register("online-ivectors", &online_ivector_rspecifier, "Rspecifier for "

"iVectors estimated online, as matrices. If you supply this,"

" you must set the --online-ivector-period option.");

po.Register("online-ivector-period", &online_ivector_period, "Number of frames "

"between iVectors in matrices supplied to the --online-ivectors "

"option");

po.Read(argc, argv);

if (po.NumArgs() < 4 || po.NumArgs() > 6) {

po.PrintUsage();

exit(1);

}

std::string model_in_filename = po.GetArg(1),

fst_in_str = po.GetArg(2),

feature_rspecifier = po.GetArg(3),

lattice_wspecifier = po.GetArg(4),

words_wspecifier = po.GetOptArg(5),

alignment_wspecifier = po.GetOptArg(6);

TransitionModel trans_model;

AmNnetSimple am_nnet;

{

bool binary;

Input ki(model_in_filename, &binary);

trans_model.Read(ki.Stream(), binary);

am_nnet.Read(ki.Stream(), binary);

SetBatchnormTestMode(true, &(am_nnet.GetNnet()));

SetDropoutTestMode(true, &(am_nnet.GetNnet()));

CollapseModel(CollapseModelConfig(), &(am_nnet.GetNnet()));

}

bool determinize = config.determinize_lattice;

CompactLatticeWriter compact_lattice_writer;

LatticeWriter lattice_writer;

if (! (determinize ? compact_lattice_writer.Open(lattice_wspecifier)

: lattice_writer.Open(lattice_wspecifier)))

KALDI_ERR << "Could not open table for writing lattices: "

<< lattice_wspecifier;

RandomAccessBaseFloatMatrixReader online_ivector_reader(

online_ivector_rspecifier);

RandomAccessBaseFloatVectorReaderMapped ivector_reader(

ivector_rspecifier, utt2spk_rspecifier);

Int32VectorWriter words_writer(words_wspecifier);

Int32VectorWriter alignment_writer(alignment_wspecifier);

fst::SymbolTable *word_syms = NULL;

if (word_syms_filename != "")

if (!(word_syms = fst::SymbolTable::ReadText(word_syms_filename)))

KALDI_ERR << "Could not read symbol table from file "

<< word_syms_filename;

double tot_like = 0.0;

kaldi::int64 frame_count = 0;

int num_success = 0, num_fail = 0;

// this object contains precomputed stuff that is used by all decodable

// objects. It takes a pointer to am_nnet because if it has iVectors it has

// to modify the nnet to accept iVectors at intervals.

DecodableNnetSimpleLoopedInfo decodable_info(decodable_opts,

&am_nnet);

if (ClassifyRspecifier(fst_in_str, NULL, NULL) == kNoRspecifier) {

SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);

// Input FST is just one FST, not a table of FSTs.

Fst<StdArc> *decode_fst = fst::ReadFstKaldiGeneric(fst_in_str);

timer.Reset();

{

LatticeFasterDecoder decoder(*decode_fst, config);

for (; !feature_reader.Done(); feature_reader.Next()) {

std::string utt = feature_reader.Key();

const Matrix<BaseFloat> &features (feature_reader.Value());

if (features.NumRows() == 0) {

KALDI_WARN << "Zero-length utterance: " << utt;

num_fail++;

continue;

}

const Matrix<BaseFloat> *online_ivectors = NULL;

const Vector<BaseFloat> *ivector = NULL;

if (!ivector_rspecifier.empty()) {

if (!ivector_reader.HasKey(utt)) {

KALDI_WARN << "No iVector available for utterance " << utt;

num_fail++;

continue;

} else {

ivector = &ivector_reader.Value(utt);

}

}

if (!online_ivector_rspecifier.empty()) {

if (!online_ivector_reader.HasKey(utt)) {

KALDI_WARN << "No online iVector available for utterance " << utt;

num_fail++;

continue;

} else {

online_ivectors = &online_ivector_reader.Value(utt);

}

}

DecodableAmNnetSimpleLooped nnet_decodable(

decodable_info, trans_model, features, ivector, online_ivectors,

online_ivector_period);

double like;

if (DecodeUtteranceLatticeFaster(

decoder, nnet_decodable, trans_model, word_syms, utt,

decodable_opts.acoustic_scale, determinize, allow_partial,

&alignment_writer, &words_writer, &compact_lattice_writer,

&lattice_writer,

&like)) {

tot_like += like;

frame_count += nnet_decodable.NumFramesReady();

num_success++;

} else num_fail++;

}

}

delete decode_fst; // delete this only after decoder goes out of scope.

} else { // We have different FSTs for different utterances.

SequentialTableReader<fst::VectorFstHolder> fst_reader(fst_in_str);

RandomAccessBaseFloatMatrixReader feature_reader(feature_rspecifier);

for (; !fst_reader.Done(); fst_reader.Next()) {

std::string utt = fst_reader.Key();

if (!feature_reader.HasKey(utt)) {

KALDI_WARN << "Not decoding utterance " << utt

<< " because no features available.";

num_fail++;

continue;

}

const Matrix<BaseFloat> &features = feature_reader.Value(utt);

if (features.NumRows() == 0) {

KALDI_WARN << "Zero-length utterance: " << utt;

num_fail++;

continue;

}

LatticeFasterDecoder decoder(fst_reader.Value(), config);

const Matrix<BaseFloat> *online_ivectors = NULL;

const Vector<BaseFloat> *ivector = NULL;

if (!ivector_rspecifier.empty()) {

if (!ivector_reader.HasKey(utt)) {

KALDI_WARN << "No iVector available for utterance " << utt;

num_fail++;

continue;

} else {

ivector = &ivector_reader.Value(utt);

}

}

if (!online_ivector_rspecifier.empty()) {

if (!online_ivector_reader.HasKey(utt)) {

KALDI_WARN << "No online iVector available for utterance " << utt;

num_fail++;

continue;

} else {

online_ivectors = &online_ivector_reader.Value(utt);

}

}

DecodableAmNnetSimpleLooped nnet_decodable(

decodable_info, trans_model, features, ivector, online_ivectors,

online_ivector_period);

double like;

if (DecodeUtteranceLatticeFaster(

decoder, nnet_decodable, trans_model, word_syms, utt,

decodable_opts.acoustic_scale, determinize, allow_partial,

&alignment_writer, &words_writer, &compact_lattice_writer,

&lattice_writer, &like)) {

tot_like += like;

frame_count += nnet_decodable.NumFramesReady();

num_success++;

} else num_fail++;

}

}

kaldi::int64 input_frame_count =

frame_count * decodable_opts.frame_subsampling_factor;

double elapsed = timer.Elapsed();

KALDI_LOG << "Time taken "<< elapsed

<< "s: real-time factor assuming 100 frames/sec is "

<< (elapsed * 100.0 / input_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;

if (num_success != 0) return 0;

else return 1;

} catch(const std::exception &e) {

std::cerr << e.what();

return -1;

}

}