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// decoder/training-graph-compiler.cc
// Copyright 2009-2011 Microsoft Corporation
// 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 "decoder/training-graph-compiler.h"
#include "hmm/hmm-utils.h" // for GetHTransducer
namespace kaldi {
TrainingGraphCompiler::TrainingGraphCompiler(const TransitionModel &trans_model,
const ContextDependency &ctx_dep, // Does not maintain reference to this.
fst::VectorFst<fst::StdArc> *lex_fst,
const std::vector<int32> &disambig_syms,
const TrainingGraphCompilerOptions &opts):
trans_model_(trans_model), ctx_dep_(ctx_dep), lex_fst_(lex_fst),
disambig_syms_(disambig_syms), opts_(opts) {
using namespace fst;
const std::vector<int32> &phone_syms = trans_model_.GetPhones(); // needed to create context fst.
KALDI_ASSERT(!phone_syms.empty());
KALDI_ASSERT(IsSortedAndUniq(phone_syms));
SortAndUniq(&disambig_syms_);
for (int32 i = 0; i < disambig_syms_.size(); i++)
if (std::binary_search(phone_syms.begin(), phone_syms.end(),
disambig_syms_[i]))
KALDI_ERR << "Disambiguation symbol " << disambig_syms_[i]
<< " is also a phone.";
int32 subseq_symbol = 1 + phone_syms.back();
if (!disambig_syms_.empty() && subseq_symbol <= disambig_syms_.back())
subseq_symbol = 1 + disambig_syms_.back();
{
int32 N = ctx_dep.ContextWidth(),
P = ctx_dep.CentralPosition();
if (P != N-1)
AddSubsequentialLoop(subseq_symbol, lex_fst_); // This is needed for
// systems with right-context or we will not successfully compose
// with C.
}
{ // make sure lexicon is olabel sorted.
fst::OLabelCompare<fst::StdArc> olabel_comp;
fst::ArcSort(lex_fst_, olabel_comp);
}
}
bool TrainingGraphCompiler::CompileGraphFromText(
const std::vector<int32> &transcript,
fst::VectorFst<fst::StdArc> *out_fst) {
using namespace fst;
VectorFst<StdArc> word_fst;
MakeLinearAcceptor(transcript, &word_fst);
return CompileGraph(word_fst, out_fst);
}
bool TrainingGraphCompiler::CompileGraph(const fst::VectorFst<fst::StdArc> &word_fst,
fst::VectorFst<fst::StdArc> *out_fst) {
using namespace fst;
KALDI_ASSERT(lex_fst_ !=NULL);
KALDI_ASSERT(out_fst != NULL);
VectorFst<StdArc> phone2word_fst;
// TableCompose more efficient than compose.
TableCompose(*lex_fst_, word_fst, &phone2word_fst, &lex_cache_);
KALDI_ASSERT(phone2word_fst.Start() != kNoStateId);
ContextFst<StdArc> *cfst = NULL;
{ // make cfst [ it's expanded on the fly ]
const std::vector<int32> &phone_syms = trans_model_.GetPhones(); // needed to create context fst.
int32 subseq_symbol = phone_syms.back() + 1;
if (!disambig_syms_.empty() && subseq_symbol <= disambig_syms_.back())
subseq_symbol = 1 + disambig_syms_.back();
cfst = new ContextFst<StdArc>(subseq_symbol,
phone_syms,
disambig_syms_,
ctx_dep_.ContextWidth(),
ctx_dep_.CentralPosition());
}
VectorFst<StdArc> ctx2word_fst;
ComposeContextFst(*cfst, phone2word_fst, &ctx2word_fst);
// ComposeContextFst is like Compose but faster for this particular Fst type.
// [and doesn't expand too many arcs in the ContextFst.]
KALDI_ASSERT(ctx2word_fst.Start() != kNoStateId);
HTransducerConfig h_cfg;
h_cfg.transition_scale = opts_.transition_scale;
std::vector<int32> disambig_syms_h; // disambiguation symbols on
// input side of H.
VectorFst<StdArc> *H = GetHTransducer(cfst->ILabelInfo(),
ctx_dep_,
trans_model_,
h_cfg,
&disambig_syms_h);
VectorFst<StdArc> &trans2word_fst = *out_fst; // transition-id to word.
TableCompose(*H, ctx2word_fst, &trans2word_fst);
KALDI_ASSERT(trans2word_fst.Start() != kNoStateId);
// Epsilon-removal and determinization combined. This will fail if not determinizable.
DeterminizeStarInLog(&trans2word_fst);
if (!disambig_syms_h.empty()) {
RemoveSomeInputSymbols(disambig_syms_h, &trans2word_fst);
// we elect not to remove epsilons after this phase, as it is
// a little slow.
if (opts_.rm_eps)
RemoveEpsLocal(&trans2word_fst);
}
// Encoded minimization.
MinimizeEncoded(&trans2word_fst);
std::vector<int32> disambig;
bool check_no_self_loops = true;
AddSelfLoops(trans_model_,
disambig,
opts_.self_loop_scale,
opts_.reorder,
check_no_self_loops,
&trans2word_fst);
delete H;
delete cfst;
return true;
}
bool TrainingGraphCompiler::CompileGraphsFromText(
const std::vector<std::vector<int32> > &transcripts,
std::vector<fst::VectorFst<fst::StdArc>*> *out_fsts) {
using namespace fst;
std::vector<const VectorFst<StdArc>* > word_fsts(transcripts.size());
for (size_t i = 0; i < transcripts.size(); i++) {
VectorFst<StdArc> *word_fst = new VectorFst<StdArc>();
MakeLinearAcceptor(transcripts[i], word_fst);
word_fsts[i] = word_fst;
}
bool ans = CompileGraphs(word_fsts, out_fsts);
for (size_t i = 0; i < transcripts.size(); i++)
delete word_fsts[i];
return ans;
}
bool TrainingGraphCompiler::CompileGraphs(
const std::vector<const fst::VectorFst<fst::StdArc>* > &word_fsts,
std::vector<fst::VectorFst<fst::StdArc>* > *out_fsts) {
using namespace fst;
KALDI_ASSERT(lex_fst_ !=NULL);
KALDI_ASSERT(out_fsts != NULL && out_fsts->empty());
out_fsts->resize(word_fsts.size(), NULL);
if (word_fsts.empty()) return true;
ContextFst<StdArc> *cfst = NULL;
{ // make cfst [ it's expanded on the fly ]
const std::vector<int32> &phone_syms = trans_model_.GetPhones(); // needed to create context fst.
int32 subseq_symbol = phone_syms.back() + 1;
if (!disambig_syms_.empty() && subseq_symbol <= disambig_syms_.back())
subseq_symbol = 1 + disambig_syms_.back();
cfst = new ContextFst<StdArc>(subseq_symbol,
phone_syms,
disambig_syms_,
ctx_dep_.ContextWidth(),
ctx_dep_.CentralPosition());
}
for (size_t i = 0; i < word_fsts.size(); i++) {
VectorFst<StdArc> phone2word_fst;
// TableCompose more efficient than compose.
TableCompose(*lex_fst_, *(word_fsts[i]), &phone2word_fst, &lex_cache_);
KALDI_ASSERT(phone2word_fst.Start() != kNoStateId &&
"Perhaps you have words missing in your lexicon?");
VectorFst<StdArc> ctx2word_fst;
ComposeContextFst(*cfst, phone2word_fst, &ctx2word_fst);
// ComposeContextFst is like Compose but faster for this particular Fst type.
// [and doesn't expand too many arcs in the ContextFst.]
KALDI_ASSERT(ctx2word_fst.Start() != kNoStateId);
(*out_fsts)[i] = ctx2word_fst.Copy(); // For now this contains the FST with symbols
// representing phones-in-context.
}
HTransducerConfig h_cfg;
h_cfg.transition_scale = opts_.transition_scale;
std::vector<int32> disambig_syms_h;
VectorFst<StdArc> *H = GetHTransducer(cfst->ILabelInfo(),
ctx_dep_,
trans_model_,
h_cfg,
&disambig_syms_h);
for (size_t i = 0; i < out_fsts->size(); i++) {
VectorFst<StdArc> &ctx2word_fst = *((*out_fsts)[i]);
VectorFst<StdArc> trans2word_fst;
TableCompose(*H, ctx2word_fst, &trans2word_fst);
DeterminizeStarInLog(&trans2word_fst);
if (!disambig_syms_h.empty()) {
RemoveSomeInputSymbols(disambig_syms_h, &trans2word_fst);
if (opts_.rm_eps)
RemoveEpsLocal(&trans2word_fst);
}
// Encoded minimization.
MinimizeEncoded(&trans2word_fst);
std::vector<int32> disambig;
bool check_no_self_loops = true;
AddSelfLoops(trans_model_,
disambig,
opts_.self_loop_scale,
opts_.reorder,
check_no_self_loops,
&trans2word_fst);
KALDI_ASSERT(trans2word_fst.Start() != kNoStateId);
*((*out_fsts)[i]) = trans2word_fst;
}
delete H;
delete cfst;
return true;
}
} // end namespace kaldi