chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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// decoder/faster-decoder.cc
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// Copyright 2009-2011 Microsoft Corporation
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// 2012-2013 Johns Hopkins University (author: Daniel Povey)
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// See ../../COPYING for clarification regarding multiple authors
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
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// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
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// MERCHANTABLITY OR NON-INFRINGEMENT.
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// See the Apache 2 License for the specific language governing permissions and
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// limitations under the License.
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#include "decoder/faster-decoder.h"
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namespace kaldi {
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FasterDecoder::FasterDecoder(const fst::Fst<fst::StdArc> &fst,
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const FasterDecoderOptions &opts):
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fst_(fst), config_(opts), num_frames_decoded_(-1) {
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KALDI_ASSERT(config_.hash_ratio >= 1.0); // less doesn't make much sense.
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KALDI_ASSERT(config_.max_active > 1);
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KALDI_ASSERT(config_.min_active >= 0 && config_.min_active < config_.max_active);
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toks_.SetSize(1000); // just so on the first frame we do something reasonable.
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}
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void FasterDecoder::InitDecoding() {
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// clean up from last time:
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ClearToks(toks_.Clear());
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StateId start_state = fst_.Start();
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KALDI_ASSERT(start_state != fst::kNoStateId);
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Arc dummy_arc(0, 0, Weight::One(), start_state);
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toks_.Insert(start_state, new Token(dummy_arc, NULL));
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ProcessNonemitting(std::numeric_limits<float>::max());
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num_frames_decoded_ = 0;
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}
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void FasterDecoder::Decode(DecodableInterface *decodable) {
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InitDecoding();
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AdvanceDecoding(decodable);
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}
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void FasterDecoder::AdvanceDecoding(DecodableInterface *decodable,
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int32 max_num_frames) {
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KALDI_ASSERT(num_frames_decoded_ >= 0 &&
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"You must call InitDecoding() before AdvanceDecoding()");
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int32 num_frames_ready = decodable->NumFramesReady();
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// num_frames_ready must be >= num_frames_decoded, or else
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// the number of frames ready must have decreased (which doesn't
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// make sense) or the decodable object changed between calls
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// (which isn't allowed).
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KALDI_ASSERT(num_frames_ready >= num_frames_decoded_);
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int32 target_frames_decoded = num_frames_ready;
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if (max_num_frames >= 0)
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target_frames_decoded = std::min(target_frames_decoded,
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num_frames_decoded_ + max_num_frames);
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while (num_frames_decoded_ < target_frames_decoded) {
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// note: ProcessEmitting() increments num_frames_decoded_
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double weight_cutoff = ProcessEmitting(decodable);
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ProcessNonemitting(weight_cutoff);
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}
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}
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bool FasterDecoder::ReachedFinal() const {
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for (const Elem *e = toks_.GetList(); e != NULL; e = e->tail) {
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if (e->val->cost_ != std::numeric_limits<double>::infinity() &&
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fst_.Final(e->key) != Weight::Zero())
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return true;
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}
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return false;
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}
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bool FasterDecoder::GetBestPath(fst::MutableFst<LatticeArc> *fst_out,
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bool use_final_probs) {
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// GetBestPath gets the decoding output. If "use_final_probs" is true
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// AND we reached a final state, it limits itself to final states;
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// otherwise it gets the most likely token not taking into
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// account final-probs. fst_out will be empty (Start() == kNoStateId) if
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// nothing was available. It returns true if it got output (thus, fst_out
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// will be nonempty).
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fst_out->DeleteStates();
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Token *best_tok = NULL;
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bool is_final = ReachedFinal();
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if (!is_final) {
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for (const Elem *e = toks_.GetList(); e != NULL; e = e->tail)
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if (best_tok == NULL || *best_tok < *(e->val) )
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best_tok = e->val;
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} else {
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double infinity = std::numeric_limits<double>::infinity(),
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best_cost = infinity;
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for (const Elem *e = toks_.GetList(); e != NULL; e = e->tail) {
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double this_cost = e->val->cost_ + fst_.Final(e->key).Value();
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if (this_cost < best_cost && this_cost != infinity) {
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best_cost = this_cost;
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best_tok = e->val;
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}
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}
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}
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if (best_tok == NULL) return false; // No output.
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std::vector<LatticeArc> arcs_reverse; // arcs in reverse order.
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for (Token *tok = best_tok; tok != NULL; tok = tok->prev_) {
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BaseFloat tot_cost = tok->cost_ -
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(tok->prev_ ? tok->prev_->cost_ : 0.0),
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graph_cost = tok->arc_.weight.Value(),
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ac_cost = tot_cost - graph_cost;
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LatticeArc l_arc(tok->arc_.ilabel,
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tok->arc_.olabel,
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LatticeWeight(graph_cost, ac_cost),
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tok->arc_.nextstate);
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arcs_reverse.push_back(l_arc);
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}
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KALDI_ASSERT(arcs_reverse.back().nextstate == fst_.Start());
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arcs_reverse.pop_back(); // that was a "fake" token... gives no info.
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StateId cur_state = fst_out->AddState();
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fst_out->SetStart(cur_state);
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for (ssize_t i = static_cast<ssize_t>(arcs_reverse.size())-1; i >= 0; i--) {
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LatticeArc arc = arcs_reverse[i];
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arc.nextstate = fst_out->AddState();
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fst_out->AddArc(cur_state, arc);
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cur_state = arc.nextstate;
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}
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if (is_final && use_final_probs) {
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Weight final_weight = fst_.Final(best_tok->arc_.nextstate);
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fst_out->SetFinal(cur_state, LatticeWeight(final_weight.Value(), 0.0));
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} else {
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fst_out->SetFinal(cur_state, LatticeWeight::One());
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}
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RemoveEpsLocal(fst_out);
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return true;
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}
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// Gets the weight cutoff. Also counts the active tokens.
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double FasterDecoder::GetCutoff(Elem *list_head, size_t *tok_count,
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BaseFloat *adaptive_beam, Elem **best_elem) {
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double best_cost = std::numeric_limits<double>::infinity();
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size_t count = 0;
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if (config_.max_active == std::numeric_limits<int32>::max() &&
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config_.min_active == 0) {
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for (Elem *e = list_head; e != NULL; e = e->tail, count++) {
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double w = e->val->cost_;
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if (w < best_cost) {
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best_cost = w;
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if (best_elem) *best_elem = e;
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}
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}
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if (tok_count != NULL) *tok_count = count;
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if (adaptive_beam != NULL) *adaptive_beam = config_.beam;
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return best_cost + config_.beam;
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} else {
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tmp_array_.clear();
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for (Elem *e = list_head; e != NULL; e = e->tail, count++) {
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double w = e->val->cost_;
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tmp_array_.push_back(w);
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if (w < best_cost) {
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best_cost = w;
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if (best_elem) *best_elem = e;
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}
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}
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if (tok_count != NULL) *tok_count = count;
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double beam_cutoff = best_cost + config_.beam,
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min_active_cutoff = std::numeric_limits<double>::infinity(),
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max_active_cutoff = std::numeric_limits<double>::infinity();
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if (tmp_array_.size() > static_cast<size_t>(config_.max_active)) {
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std::nth_element(tmp_array_.begin(),
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tmp_array_.begin() + config_.max_active,
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tmp_array_.end());
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max_active_cutoff = tmp_array_[config_.max_active];
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}
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if (max_active_cutoff < beam_cutoff) { // max_active is tighter than beam.
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if (adaptive_beam)
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*adaptive_beam = max_active_cutoff - best_cost + config_.beam_delta;
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return max_active_cutoff;
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}
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if (tmp_array_.size() > static_cast<size_t>(config_.min_active)) {
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if (config_.min_active == 0) min_active_cutoff = best_cost;
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else {
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std::nth_element(tmp_array_.begin(),
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tmp_array_.begin() + config_.min_active,
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tmp_array_.size() > static_cast<size_t>(config_.max_active) ?
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tmp_array_.begin() + config_.max_active :
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tmp_array_.end());
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min_active_cutoff = tmp_array_[config_.min_active];
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}
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}
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if (min_active_cutoff > beam_cutoff) { // min_active is looser than beam.
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if (adaptive_beam)
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*adaptive_beam = min_active_cutoff - best_cost + config_.beam_delta;
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return min_active_cutoff;
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} else {
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*adaptive_beam = config_.beam;
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return beam_cutoff;
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}
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}
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}
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void FasterDecoder::PossiblyResizeHash(size_t num_toks) {
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size_t new_sz = static_cast<size_t>(static_cast<BaseFloat>(num_toks)
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* config_.hash_ratio);
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if (new_sz > toks_.Size()) {
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toks_.SetSize(new_sz);
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}
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}
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// ProcessEmitting returns the likelihood cutoff used.
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double FasterDecoder::ProcessEmitting(DecodableInterface *decodable) {
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int32 frame = num_frames_decoded_;
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Elem *last_toks = toks_.Clear();
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size_t tok_cnt;
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BaseFloat adaptive_beam;
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Elem *best_elem = NULL;
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double weight_cutoff = GetCutoff(last_toks, &tok_cnt,
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&adaptive_beam, &best_elem);
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KALDI_VLOG(3) << tok_cnt << " tokens active.";
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PossiblyResizeHash(tok_cnt); // This makes sure the hash is always big enough.
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// This is the cutoff we use after adding in the log-likes (i.e.
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// for the next frame). This is a bound on the cutoff we will use
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// on the next frame.
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double next_weight_cutoff = std::numeric_limits<double>::infinity();
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// First process the best token to get a hopefully
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// reasonably tight bound on the next cutoff.
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if (best_elem) {
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StateId state = best_elem->key;
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Token *tok = best_elem->val;
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for (fst::ArcIterator<fst::Fst<Arc> > aiter(fst_, state);
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!aiter.Done();
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aiter.Next()) {
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const Arc &arc = aiter.Value();
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if (arc.ilabel != 0) { // we'd propagate..
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BaseFloat ac_cost = - decodable->LogLikelihood(frame, arc.ilabel);
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double new_weight = arc.weight.Value() + tok->cost_ + ac_cost;
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if (new_weight + adaptive_beam < next_weight_cutoff)
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next_weight_cutoff = new_weight + adaptive_beam;
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}
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}
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}
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// int32 n = 0, np = 0;
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// the tokens are now owned here, in last_toks, and the hash is empty.
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// 'owned' is a complex thing here; the point is we need to call TokenDelete
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// on each elem 'e' to let toks_ know we're done with them.
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for (Elem *e = last_toks, *e_tail; e != NULL; e = e_tail) { // loop this way
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// n++;
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// because we delete "e" as we go.
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StateId state = e->key;
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Token *tok = e->val;
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if (tok->cost_ < weight_cutoff) { // not pruned.
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// np++;
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KALDI_ASSERT(state == tok->arc_.nextstate);
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for (fst::ArcIterator<fst::Fst<Arc> > aiter(fst_, state);
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!aiter.Done();
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aiter.Next()) {
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Arc arc = aiter.Value();
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if (arc.ilabel != 0) { // propagate..
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BaseFloat ac_cost = - decodable->LogLikelihood(frame, arc.ilabel);
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double new_weight = arc.weight.Value() + tok->cost_ + ac_cost;
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if (new_weight < next_weight_cutoff) { // not pruned..
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Token *new_tok = new Token(arc, ac_cost, tok);
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Elem *e_found = toks_.Insert(arc.nextstate, new_tok);
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if (new_weight + adaptive_beam < next_weight_cutoff)
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next_weight_cutoff = new_weight + adaptive_beam;
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if (e_found->val != new_tok) {
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if (*(e_found->val) < *new_tok) {
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Token::TokenDelete(e_found->val);
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e_found->val = new_tok;
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} else {
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Token::TokenDelete(new_tok);
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}
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}
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}
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}
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}
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}
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e_tail = e->tail;
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Token::TokenDelete(e->val);
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toks_.Delete(e);
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}
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num_frames_decoded_++;
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return next_weight_cutoff;
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}
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// TODO: first time we go through this, could avoid using the queue.
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void FasterDecoder::ProcessNonemitting(double cutoff) {
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// Processes nonemitting arcs for one frame.
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KALDI_ASSERT(queue_.empty());
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for (const Elem *e = toks_.GetList(); e != NULL; e = e->tail)
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queue_.push_back(e);
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while (!queue_.empty()) {
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const Elem* e = queue_.back();
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queue_.pop_back();
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StateId state = e->key;
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Token *tok = e->val; // would segfault if state not
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// in toks_ but this can't happen.
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if (tok->cost_ > cutoff) { // Don't bother processing successors.
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continue;
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}
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KALDI_ASSERT(tok != NULL && state == tok->arc_.nextstate);
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for (fst::ArcIterator<fst::Fst<Arc> > aiter(fst_, state);
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!aiter.Done();
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aiter.Next()) {
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const Arc &arc = aiter.Value();
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if (arc.ilabel == 0) { // propagate nonemitting only...
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Token *new_tok = new Token(arc, tok);
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if (new_tok->cost_ > cutoff) { // prune
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Token::TokenDelete(new_tok);
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} else {
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Elem *e_found = toks_.Insert(arc.nextstate, new_tok);
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if (e_found->val == new_tok) {
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queue_.push_back(e_found);
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} else {
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if (*(e_found->val) < *new_tok) {
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Token::TokenDelete(e_found->val);
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e_found->val = new_tok;
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queue_.push_back(e_found);
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} else {
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Token::TokenDelete(new_tok);
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}
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}
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}
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}
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}
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}
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}
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void FasterDecoder::ClearToks(Elem *list) {
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for (Elem *e = list, *e_tail; e != NULL; e = e_tail) {
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Token::TokenDelete(e->val);
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e_tail = e->tail;
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toks_.Delete(e);
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}
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}
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} // end namespace kaldi.
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