// decoder/nbest-decoder.h

// Copyright 2009-2011 Mirko Hannemann; Microsoft Corporation

// 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.

#ifndef KALDI_DECODER_NBEST_DECODER_H_

#define KALDI_DECODER_NBEST_DECODER_H_

#include "util/stl-utils.h"

#include "itf/options-itf.h"

#include "util/hash-list.h"

#include "fst/fstlib.h"

#include "itf/decodable-itf.h"

#include "lat/kaldi-lattice.h" // for CompactLatticeArc

namespace kaldi {

struct NBestDecoderOptions {

BaseFloat beam;

int32 max_active;

int32 n_best;

BaseFloat beam_delta;

BaseFloat hash_ratio;

NBestDecoderOptions(): beam(16.0),

max_active(std::numeric_limits<int32>::max()),

n_best(1),

beam_delta(0.5), hash_ratio(2.0) { }

void Register(OptionsItf *opts, bool full) { /// if "full", use obscure

/// options too.

/// Depends on program.

opts->Register("beam", &beam, "Decoder beam");

opts->Register("max-active", &max_active, "Decoder max active states.");

opts->Register("n-best", &n_best, "Decoder number of best tokens.");

if (full) {

opts->Register("beam-delta", &beam_delta,

"Increment used in decoder [obscure setting]");

opts->Register("hash-ratio", &hash_ratio,

"Setting used in decoder to control hash behavior");

}

}

};

/** Caution: this decoder was written essentially as testing code for lattice-faster-decoder,

and is not very useful; it would be better to pipe the lattices from lattice-faster-decoder

into lattice-to-nbest if you want an n-best list. We will likely eventually delete this

from the trunk and leave it only in ^/branches/complete.

We are not planning to modify this decoder to support the "newer interface" with

InitDecoding() and AdvanceDecoding().

*/

class NBestDecoder {

public:

// maybe use fst<LatticeArc>/fst<CompactLatticeArc>, as in lat/kaldi-lattice.h

// to store information to get graph and acoustic scores separately

typedef fst::StdArc Arc;

typedef Arc::Label Label;

typedef Arc::StateId StateId;

typedef Arc::Weight Weight;

// instantiate this class once for each thing you have to decode.

NBestDecoder(const fst::Fst<fst::StdArc> &fst,

NBestDecoderOptions opts): fst_(fst), opts_(opts) {

KALDI_ASSERT(opts_.hash_ratio >= 1.0); // less doesn't make much sense.

KALDI_ASSERT(opts_.max_active > 1);

toks_.SetSize(1000); // just so on the first frame we do something reasonable.

decodable_ = NULL;

}

void SetOptions(const NBestDecoderOptions &opts) { opts_ = opts; }

~NBestDecoder() {

ClearToks(toks_.Clear());

token_store_.Clear();

decodable_ = NULL;

}

void Decode(DecodableInterface *decodable) {

decodable_ = decodable;

// clean up from last time:

ClearToks(toks_.Clear());

token_store_.Init(decodable, &toks_, opts_.n_best);

StateId start_state = fst_.Start();

KALDI_ASSERT(start_state != fst::kNoStateId);

Token *tok = token_store_.CreateTok(0, NULL);

tok->c = Weight::One();

tok->ca = Weight::One();

tok->I = NULL;

toks_.Insert(start_state, tok);

PropagateEpsilon(std::numeric_limits<float>::max());

for (int32 frame = 0; !decodable_->IsLastFrame(frame-1); frame++) {

BaseFloat adaptive_beam = PropagateEmitting(frame);

PropagateEpsilon(adaptive_beam);

}

}

bool ReachedFinal() {

for (const Elem *e = toks_.GetList(); e != NULL; e = e->tail) {

Weight this_weight = Times(e->val->c, fst_.Final(e->key));

if (this_weight != Weight::Zero()) {

return true;

}

}

return false;

}

bool GetNBestLattice(fst::MutableFst<CompactLatticeArc> *fst_out,

bool *was_final, int32 *nbest, BaseFloat *nbest_beam,

bool use_final_probs = true) {

// GetNBestLattice gets the n-best decoding output. If "use_final_probs"

// is true AND we reached a final state, it limits itself to final states;

// otherwise it does not take into account final-probs.

// fst_out will be empty (Start() == kNoStateId) if nothing was available.

// It returns number of paths if it got output (thus, fst_out will be

// nonempty), otherwise zero.

int n_paths = 0;

BaseFloat worst_final = 0.0,

best_final = std::numeric_limits<BaseFloat>::infinity();

*was_final = ReachedFinal();

Elem *last_toks = toks_.Clear(); // P <- C , C = {}

Token *tok = token_store_.CreateTok(0, NULL);

StateId end_state = 1E9; // some imaginary super end state

tok->c = Weight::Zero();

tok->I = NULL;

toks_.Insert(end_state, tok);

Elem *best_e = toks_.Find(end_state);

if (!(*was_final)) { // only look for best tokens in this frame

for (Elem *e = last_toks, *e_tail; e != NULL; e = e_tail) {

token_store_.CombineN(best_e, e->val);

e_tail = e->tail;

toks_.Delete(e);

}

} else { // find best tokens in final states

for (Elem *e = last_toks, *e_tail; e != NULL; e = e_tail) {

Token *source = e->val;

Weight fw = use_final_probs ? fst_.Final(e->key) : Weight::One();

if (fw != Weight::Zero()) {

source->c = Times(source->c, fw);

token_store_.CombineN(best_e, source);

} else {

token_store_.DeleteTok(source);

}

e_tail = e->tail;

toks_.Delete(e);

}

}

// start building output FST

fst_out->DeleteStates();

StateId start_state = fst_out->AddState();

fst_out->SetStart(start_state);

last_toks = toks_.Clear(); // just tokens in super end state this time

if (last_toks->val == NULL) return false; // No output.

// go through tokens in imaginary super end state

for (Elem *e = last_toks, *e_tail = NULL; e != NULL; e = e_tail) {

Token *best_tok = e->val;

BaseFloat amscore = best_tok->ca.Value(),

lmscore = best_tok->c.Value() - amscore;

if (KALDI_ISINF(amscore) || KALDI_ISINF(lmscore)) {

KALDI_WARN << "infinity token! probably too narrow beam to retrieve n best";

e_tail = e->tail;

toks_.Delete(e);

continue; // skip that token

}

LatticeWeight path_w(lmscore, amscore);

CompactLatticeWeight path_weight(path_w, vector<int32>());

std::vector<CompactLatticeArc*> arcs_reverse; // reverse order output arcs

// outer loop for word tokens

for (Token *tok = best_tok; tok != NULL; tok = tok->previous) {

// inner loop for input label tokens

std::vector<int32> str_rev, str;

for (SeqToken *stok = tok->I; stok != NULL; stok = stok->previous) {

str_rev.push_back(stok->i);

}

// reverse vector

std::vector<int32>::reverse_iterator rit;

for (rit = str_rev.rbegin(); rit < str_rev.rend(); ++rit)

str.push_back(*rit);

arcs_reverse.push_back(new CompactLatticeArc(

tok->o, tok->o, CompactLatticeWeight(LatticeWeight::One(), str), 0));

// no weight info (tok->c), no state info

}

token_store_.DeleteTok(best_tok);

StateId cur_state = start_state;

for (ssize_t i = static_cast<ssize_t>(arcs_reverse.size())-1; i >= 0; i--) {

CompactLatticeArc *arc = arcs_reverse[i];

arc->nextstate = fst_out->AddState();

fst_out->AddArc(cur_state, *arc);

cur_state = arc->nextstate;

delete arc;

}

fst_out->SetFinal(cur_state, path_weight);

e_tail = e->tail;

toks_.Delete(e);

n_paths++;

BaseFloat p_score = amscore + lmscore;

if (p_score > worst_final) worst_final = p_score;

if (p_score < best_final) best_final = p_score;

}

if (nbest_beam) *nbest_beam = worst_final - best_final;

if (nbest) *nbest = n_paths;

RemoveEpsLocal(fst_out);

return true;

}

// bool GetBestPath(fst::MutableFst<LatticeArc> *fst_out, bool *was_final) {

// fst::VectorFst<CompactLatticeArc> fst, fst_one;

// if (!GetNBestLattice(&fst, was_final)) return false;

// std::cout << "n-best paths:\n";

// fst::FstPrinter<CompactLatticeArc> fstprinter(fst, NULL, NULL, NULL, false, true);

// fstprinter.Print(&std::cout, "standard output");

// ShortestPath(fst, &fst_one);

// ConvertLattice(fst_one, fst_out, true);

// return true;

// }

private:

// TokenStore is a store of linked tokens with its own allocator

class TokenStore {

// the pointer *previous has a double function:

// it's also used in linked lists to store allocated tokens

public:

struct SeqToken { // an incremental/relative token inside a full Token

Label i; // input label i

SeqToken *previous; // lattice backward pointer (also as linked list)

int refs; // reference counter (for memory management)

};

class Token {

public:

// here will be the c and I of 'full tokens'

Weight c; // c (total weight)

Weight ca; // acoustic part of c

SeqToken *I; // sequence I

Label o; // o

Token *previous; // t'

int32 refs; // reference counter (for memory management)

unsigned hash; // hashing the output symbol sequence

inline bool operator < (const Token &other) {

return c.Value() > other.c.Value();

// This makes sense for log + tropical semiring.

}

inline bool Equal(Token *other) { // compares output sequences of Tokens

if (hash != other->hash) return false;

Token *t1 = this, *t2 = other;

while (t1 != NULL && t2 != NULL) {

if (t1->o != t2->o) return false;

t1 = t1->previous; t2 = t2->previous;

if (t1 == t2) { return true; }

if ((!t1) || (!t2)) { return false; }

}

KALDI_ASSERT(false); // should never reach this point

return true;

}

};

typedef HashList<StateId, Token*> TokenHash;

typedef TokenHash::Elem Elem;

void Init(DecodableInterface *decodable, TokenHash *toks, int32 n_best) {

Clear();

n_best_ = n_best;

decodable_ = decodable;

toks_ = toks;

}

inline void DeleteSeq(SeqToken *seq) {

// delete seq token: either decrease reference count or put to linked list

seq->refs--;

if (seq->refs > 0) return;

// really kill sequence token

// clean up unused sequence tokens recursively backwards

if (seq->previous != NULL) DeleteSeq(seq->previous);

// save the unused sequence token in linked list (abusing *previous)

seq->previous = free_st_head_;

free_st_head_ = seq;

}

inline SeqToken *NewSeq() {

// new seq token: either take from linked list or extend list

SeqToken *tmp;

if (free_st_head_) { // take from free list

tmp = free_st_head_;

free_st_head_ = free_st_head_->previous;

} else { // make new entries in free list

tmp = new SeqToken[allocate_block_size_];

for (size_t i = 0; i + 1 < allocate_block_size_; i++) {

tmp[i].previous = tmp + i + 1;

}

tmp[allocate_block_size_ - 1].previous = NULL;

allocated_s_.push_back(tmp);

free_st_head_ = tmp->previous;

}

// initialize new sequence token (append or start new sequence)

tmp->refs = 1;

return tmp;

}

inline SeqToken *CreateSeq(Label input, SeqToken *prev) {

SeqToken *tmp = NULL;

if (input > 0) {

tmp = NewSeq();

tmp->i = input;

tmp->previous = prev;

if (prev) {

prev->refs++;

}

} else {

if (prev) {

tmp = prev;

if (prev) { // prev->previous?

prev->refs++;

}

}

}

return tmp;

}

inline void DeleteTok(Token *tok) {

// delete token: either decrease reference count or put to linked list

tok->refs--;

if (tok->refs > 0) return;

// really kill token

// clean up unused tokens recursively backwards

if (tok->previous != NULL) {

DeleteTok(tok->previous);

}

if (tok->I != NULL) { // delete sequence I

DeleteSeq(tok->I);

}

// save the unused token in linked list (abusing *previous)

tok->previous = free_t_head_;

free_t_head_ = tok;

}

inline Token *CreateTok(Label output, Token *prev) {

// new token: either take from linked list or extend list

Token *tmp;

if (free_t_head_) { // take from free list

tmp = free_t_head_;

free_t_head_ = free_t_head_->previous;

} else { // make new entries in free list

tmp = new Token[allocate_block_size_];

for (size_t i = 0; i + 1 < allocate_block_size_; i++) {

tmp[i].previous = tmp + i + 1;

}

tmp[allocate_block_size_ - 1].previous = NULL;

allocated_t_.push_back(tmp);

free_t_head_ = tmp->previous;

}

// initialize data

tmp->refs = 1;

tmp->c = Weight::Zero();

tmp->I = NULL;

tmp->o = output;

tmp->previous = prev;

if (prev) {

prev->refs++;

tmp->hash = prev->hash * 97 + static_cast<unsigned>(output);

} else {

tmp->hash = static_cast<unsigned>(output);

}

return tmp;

}

inline Token* Combine(Token *tok1, Token *tok2) { // Viterbi version

KALDI_ASSERT(tok1);

if (!tok2) return tok1;

if (tok1 == tok2) return tok1;

if (tok1->c.Value() < tok2->c.Value()) {

DeleteTok(tok2);

return tok1;

} else {

DeleteTok(tok1);

return tok2;

}

}

inline bool CombineN(Elem *head, Token *new_tok) { // n-best version

if (!new_tok) return false;

Elem *e = head;

StateId state = e->key;

BaseFloat new_weight = static_cast<BaseFloat>(new_tok->c.Value());

size_t count = 0;

BaseFloat worst_weight = 0.0; // small == low cost

Elem *worst_elem = NULL;

do {

count++;

Token *tok = e->val;

if (tok == new_tok) { return false; }

BaseFloat w = static_cast<BaseFloat>(tok->c.Value());

if (w > worst_weight) {

worst_weight = w;

worst_elem = e;

}

if (tok->Equal(new_tok)) { // if they have the same output sequence

if (w < new_weight) {

DeleteTok(new_tok);

return false;

} else {

DeleteTok(tok);

e->val = new_tok;

return true;

}

}

e = e->tail;

} while ( (e != NULL) && (e->key == state) );

// if we are here, no Token with the same output sequence was found

if (count < n_best_) {

toks_->InsertMore(state, new_tok);

return true;

} else {

if (worst_weight < new_weight) {

DeleteTok(new_tok);

return false;

} else {

DeleteTok(worst_elem->val);

worst_elem->val = new_tok;

return true;

}

}

}

inline Token* Advance(Token *source, Arc &arc, int32 frame,

BaseFloat cutoff) {

// compute new weight

Weight w = Times(source->c, arc.weight);

Weight amscore = Weight::One();

if (arc.ilabel > 0) { // emitting arc

amscore = Weight(- decodable_->LogLikelihood(frame, arc.ilabel));

w = Times(w, amscore);

}

Weight wa = Times(source->ca, amscore);

if (w.Value() > cutoff) { // prune

return NULL;

}

// create new token

Token *tok;

if (arc.olabel > 0) { // create new token

// find or create corresponding Token

tok = CreateTok(arc.olabel, source); // "new" Token

tok->I = CreateSeq(arc.ilabel, NULL); // new sequence I starts

} else { // append sequence I

tok = CreateTok(source->o, source->previous); // copy previous Token

tok->I = CreateSeq(arc.ilabel, source->I);

}

tok->c = w;

tok->ca = wa;

return tok;

}

void Clear() {

// check that all seq tokens are freed

for (size_t i = 0; i < allocated_t_.size(); i++) delete[] allocated_t_[i];

allocated_t_.clear();

free_t_head_ = NULL;

// check that all seq tokens are freed

for (size_t i = 0; i < allocated_s_.size(); i++) delete[] allocated_s_[i];

allocated_s_.clear();

free_st_head_ = NULL;

}

TokenStore() {

free_t_head_ = NULL;

free_st_head_ = NULL;

}

~TokenStore() {

Clear();

}

private:

// head of list of currently free Tokens ready for allocation

Token *free_t_head_;

std::vector<Token*> allocated_t_; // list of allocated tokens

// head of list of currently free SeqTokens ready for allocation

SeqToken *free_st_head_;

std::vector<SeqToken*> allocated_s_; // list of allocated seq tokens

DecodableInterface *decodable_;

TokenHash *toks_;

int32 n_best_;

// number of tokens to allocate in one block

static const size_t allocate_block_size_ = 8192;

}; // class TokenStore

typedef TokenStore::Token Token;

typedef TokenStore::SeqToken SeqToken;

// typedef HashList<StateId, Token*>::Elem Elem;

typedef HashList<StateId, Token*> TokenHash;

typedef TokenHash::Elem Elem;

/// Gets the weight cutoff. Also counts the active tokens.

BaseFloat GetCutoff(Elem *list_head, size_t *tok_count,

BaseFloat *adaptive_beam, Elem **best_elem) {

BaseFloat best_weight = 1.0e+10; // positive == high cost == bad.

size_t count = 0;

// find best token

tmp_array_.clear();

for (Elem *e = list_head; e != NULL; e = e->tail, count++) {

BaseFloat w = static_cast<BaseFloat>(e->val->c.Value());

tmp_array_.push_back(w); // ??check this - not always necessary

if (w < best_weight) {

best_weight = w;

if (best_elem) *best_elem = e;

}

}

if (tok_count != NULL) *tok_count = count;

// compute adaptive beam

if ((opts_.max_active == std::numeric_limits<int32>::max()) ||

(tmp_array_.size() <= static_cast<size_t>(opts_.max_active))) {

if (adaptive_beam != NULL) *adaptive_beam = opts_.beam;

return best_weight + opts_.beam;

} else {

// the lowest elements (lowest costs, highest likes)

// will be put in the left part of tmp_array.

std::nth_element(tmp_array_.begin(),

tmp_array_.begin() + opts_.max_active,

tmp_array_.end());

// return the tighter of the two beams.

BaseFloat ans = std::min(best_weight + opts_.beam,

*(tmp_array_.begin() + opts_.max_active));

if (adaptive_beam)

*adaptive_beam = std::min(opts_.beam,

ans - best_weight + opts_.beam_delta);

return ans;

}

}

void PossiblyResizeHash(size_t num_toks) {

size_t new_sz = static_cast<size_t>(static_cast<BaseFloat>(num_toks)

* opts_.hash_ratio);

if (new_sz > toks_.Size()) {

toks_.SetSize(new_sz);

}

}

// PropagateEmitting returns the likelihood cutoff used.

BaseFloat PropagateEmitting(int32 frame) {

Elem *last_toks = toks_.Clear(); // P <- C , C = {}

size_t tok_cnt;

BaseFloat adaptive_beam;

Elem *best_elem = NULL;

BaseFloat weight_cutoff = GetCutoff(last_toks, &tok_cnt,

&adaptive_beam, &best_elem);

PossiblyResizeHash(tok_cnt); // This makes sure the hash is always big enough.

// This is the cutoff we use after adding in the log-likes (i.e.

// for the next frame). This is a bound on the cutoff we will use

// on the next frame.

BaseFloat next_weight_cutoff = 1.0e+10;

// First process the best token to get a hopefully

// reasonably tight bound on the next cutoff.

if (best_elem) {

StateId state = best_elem->key;

Token *tok = best_elem->val;

for (fst::ArcIterator<fst::Fst<Arc> > aiter(fst_, state);

!aiter.Done();

aiter.Next()) {

Arc arc = aiter.Value();

if (arc.ilabel != 0) { // propagate..

arc.weight = Times(arc.weight,

Weight(- decodable_->LogLikelihood(frame, arc.ilabel)));

BaseFloat new_weight = arc.weight.Value() + tok->c.Value();

if (new_weight + adaptive_beam < next_weight_cutoff)

next_weight_cutoff = new_weight + adaptive_beam;

}

}

}

// int32 n = 0, np = 0;

// the tokens are now owned here, in last_toks, and the hash is empty.

// 'owned' is a complex thing here; the point is we need to call DeleteElem

// on each elem 'e' to let toks_ know we're done with them.

for (Elem *e = last_toks, *e_tail; e != NULL; e = e_tail) {

// for all (s,t) in P

// n++;

// because we delete "e" as we go.

StateId state = e->key;

Token *tok = e->val;

if (tok->c.Value() < weight_cutoff) { // not pruned.

// np++;

// KALDI_ASSERT(state == tok->arc_.nextstate);

for (fst::ArcIterator<fst::Fst<Arc> > aiter(fst_, state);

!aiter.Done(); aiter.Next()) {

// for all a in A(state)

Arc arc = aiter.Value();

if (arc.ilabel != 0) { // propagate only emitting

Token *new_tok =

token_store_.Advance(tok, arc, frame, next_weight_cutoff);

if (new_tok) {

Elem *e_found = toks_.Find(arc.nextstate);

if (e_found == NULL) {

toks_.Insert(arc.nextstate, new_tok);

} else {

token_store_.CombineN(e_found, new_tok);

}

}

}

}

}

e_tail = e->tail; // several tokens with the same key can follow

token_store_.DeleteTok(e->val);

toks_.Delete(e);

}

// std::cerr << n << ', ' << np << ', ' <<adaptive_beam<<' ';

return adaptive_beam;

}

void PropagateEpsilon(BaseFloat adaptive_beam) {

// Processes nonemitting arcs for one frame. Propagates within

// cur_toks_.

KALDI_ASSERT(queue_.empty());

queue_.max_load_factor(1.0);

float best_weight = 1.0e+10;

for (const Elem *e = toks_.GetList(); e != NULL; e = e->tail) {

// queue_.push_back(e->key);

queue_.insert(e->key);

best_weight = std::min(best_weight, e->val->c.Value());

}

BaseFloat cutoff = best_weight + adaptive_beam;

while (!queue_.empty()) {

// StateId state = queue_.back();

StateId state = *(queue_.begin());

// queue_.pop_back();

queue_.erase(queue_.begin());

Elem *elem = toks_.Find(state); // would segfault if state not

// in toks_ but this can't happen.

// we have to pop all tokens with the same state

// this may create some unneccessary repetitions, since only the new token

// needs to be forwarded, but I don't know yet how to solve this

while (elem && elem->key == state) {

Token *tok = elem->val;

elem = elem->tail;

if (tok->c.Value() > cutoff) { // Don't bother processing successors.

continue;

}

// KALDI_ASSERT(tok != NULL && state == tok->arc_.nextstate);

KALDI_ASSERT(tok != NULL);

for (fst::ArcIterator<fst::Fst<Arc> > aiter(fst_, state);

!aiter.Done(); aiter.Next()) {

// for all a in A(state)

Arc arc = aiter.Value();

if (arc.ilabel == 0) { // propagate nonemitting only...

Token *new_tok = token_store_.Advance(tok, arc, -1, cutoff); // -1:eps

if (new_tok) {

Elem *e_found = toks_.Find(arc.nextstate);

if (e_found == NULL) {

toks_.Insert(arc.nextstate, new_tok);

// queue_.push_back(arc.nextstate);

queue_.insert(arc.nextstate); // might be pushed several times

} else {

if (token_store_.CombineN(e_found, new_tok)) { // C was updated

// queue_.push_back(arc.nextstate);

queue_.insert(arc.nextstate);

}

}

}

} // if nonemitting

} // for Arciterator

} // while

}

}

// HashList defined in ../util/hash-list.h. It actually allows us to maintain

// more than one list (e.g. for current and previous frames), but only one of

// them at a time can be indexed by StateId.

TokenHash toks_;

const fst::Fst<fst::StdArc> &fst_;

NBestDecoderOptions opts_;

typedef unordered_set<StateId> StateQueue;

StateQueue queue_; // used in PropagateEpsilon,

// std::vector<StateId> queue_; // temp variable used in PropagateEpsilon,

std::vector<BaseFloat> tmp_array_; // used in GetCutoff.

// make it class member to avoid internal new/delete.

TokenStore token_store_;

DecodableInterface *decodable_;

// It might seem unclear why we call ClearToks(toks_.Clear()).

// There are two separate cleanup tasks we need to do at when we start a new file.

// one is to delete the Token objects in the list; the other is to delete

// the Elem objects. toks_.Clear() just clears them from the hash and gives ownership

// to the caller, who then has to call toks_.Delete(e) for each one. It was designed

// this way for convenience in propagating tokens from one frame to the next.

void ClearToks(Elem *list) {

for (Elem *e = list, *e_tail; e != NULL; e = e_tail) {

token_store_.DeleteTok(e->val);

e_tail = e->tail;

toks_.Delete(e);

}

}

};

} // end namespace kaldi.

#endif